AI and data science for smart emergency, crisis and disaster resilience.

Cao, Longbing

doi:10.1007/s41060-023-00393-w

Cited by 102Anomaly Detection Techniques and ApplicationsBig Data and Business IntelligenceCOVID-19 diagnosis using AI

AI and data science for smart emergency, crisis and disaster resilience.

Open access

Cao, Longbing·2023·International journal of data science and analytics

DOI 10.1007/s41060-023-00393-wPMC PMC10041487

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Abstract

The uncertain world has seen increasing emergencies, crises and disasters (ECDs), such as the COVID-19 pandemic, hurricane Ian, global financial inflation and recession, misinformation disaster, and cyberattacks. AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems. A systematic overview of diverse ECDs, classic ECD management, ECD data complexities, and an AISDR research landscape are presented in this article. Translational disaster AI is essential to enable smart disaster resilience.

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Crossref · 10.1007/s41060-023-00393-w

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

9 of 19 cited sources checked · 6 supported · 0 flagged · accuracy not yet measured

Interpretationcitation
With advanced AI and data science, the mitigation, preparedness, response, and recovery framework can be personalized, strengthened, and upgraded with intelligence toward more proactive, active, fine-grained, tailored, and full-life-span-oriented emergency, crisis, and disaster resilience.
“With advanced AIDS (including shallow and deep machine learning and analytics), the MPRR framework can be further personalized, strengthened and upgraded with intelligence toward more proactive, active, fine-grained, tailored, and full life span-oriented ECD resilience.”
Quote in the paperNot a substantial claim1 of 2 sources checked
- cites [5] Cao, L.: Data Science Thinking: The Next Scientific, Technological and Economic Revolution. Data Analytics. Springer, New York (2018) source
- cites [6] Cao, L., Yang, Q., Yu, P.S.: Data science and AI in fintech: an overview. Int. J. Data Sci. Anal. 12(2), 81–99 (2021) source
  Section 3, “The smart FinTech ecosystem” (page unavailable), discusses using data science and AI to predict and mitigate financial crises. Searches for the MPRR framework, emergency and disaster resilience, and full-life-span ECD resilience found no passage addressing the attributed claim. (LLM, conf=0.76)
  Experimental — accuracy not yet measured
- Rests on an assumption: Successful AI and data science applications in other domains can be transferred to emergency, crisis, and disaster resilience.
Premisecitation
AI and data science made a major contribution to combating the COVID-19 disaster.
“AI and data science have played a profound role in fighting COVID-19 disasters.”
Quote in the paperNot a substantial claim1 of 1 sources checked
- cites [10] Cao, L., Liu, Q.: COVID-19 modeling: a review. medRxiv 1–105 (2021) source
Interpretationcitation
Lessons from successful AI and data science applications in other domains lead to a new-generation smart emergency, crisis, and disaster resilience framework.
“Taking the lessons learned from successful AIDS applications in other domains [5, 6], a new-generation smart ECD resilience framework emerges.”
Quote in the paperSubstantial claim1 of 2 sources checked
- cites [5] Cao, L.: Data Science Thinking: The Next Scientific, Technological and Economic Revolution. Data Analytics. Springer, New York (2018) source
- cites [6] Cao, L., Yang, Q., Yu, P.S.: Data science and AI in fintech: an overview. Int. J. Data Sci. Anal. 12(2), 81–99 (2021) source
- Rests on an assumption: Successful AI and data science applications in other domains can be transferred to emergency, crisis, and disaster resilience.
Premisecitation
COVID-19 constituted a global health emergency, crisis, and disaster.
“COVID-19 have been not only a global health emergency, crisis and disaster [7, 8]”
Quote in the paperNot a substantial claim2 of 2 sources checked
- cites [7] Cao, L.: AI in combating the COVID-19 pandemic. IEEE Intell. Syst. 37(2), 3–13 (2022) source
- cites [8] Sakurai, M., Chughtai, H.: Resilience against crises: COVID-19 and lessons from natural disasters. Eur. J. Inf. Syst. 29(5), 585–594 (2020) source
Premisecitation
In AIDS, many AI techniques-especially modeling, simulation, and deep learning-have been applied broadly across every phase of the MPRR disaster-management framework.
“In AIDS, all-round AI techniques, in particular, modeling, simulation and deep learning methods, have been widely applied to all phases of the MPRR framework for disaster management [20, 21]”
Quote in the paperNot a substantial claim1 of 2 sources checked
- cites [20] Sun, W., Bocchini, P., Davison, B.D.: Applications of artificial intelligence for disaster management. Nat. Hazards 103(3), 2631–2689 (2020) source
- cites [21] Linardos, V., Drakaki, M., Tzionas, P., Karnavas, Y.L.: Machine learning in disaster management: recent developments in methods and applications. Mach. Learn. Knowl. Extr. 4(2), 446–473 (2022) source
  The Introduction describes ML and deep learning as AI techniques used in disaster management, and Section 4.9 gives an example of an application that simulates evacuation behavior. But Section 5.1 says the review identified no ML/DL studies focused on long-term disaster recovery, so the claim that these methods have been widely applied across every MPRR phase overstates the source. The paper also does not establish modeling and simulation as particularly widespread methods across those phases. (LLM, conf=0.88)
  Experimental — accuracy not yet measured

Premisecitation
Classic emergency, crisis, and disaster management depends on single-discipline and domain-specific studies and methods for prevention, early warning, mitigation, preparedness, response, and recovery.
“Classic ECD management relies on singular disciplinary and domain-specific studies and methods for prevention, early warning, mitigation, preparedness, response, and recovery [1].”
Quote in the paperSubstantial claim0 of 1 sources checked
- cites [1] Lettieri, E., Masella, C., Radaelli, G.: Disaster management: findings from a systematic review. Disaster Prev. Manag. Int. J. 18(2), 117–136 (2009) source
Interpretationreasoning
Classic emergency, crisis, and disaster management areas face significant capability limitations and gaps in understanding, quantifying, and managing increasingly complex, uncertain, large-scale, and evolving natural, man-made, technological, and political crises and disasters.
“These areas also face significant capability limitations and gaps in understanding, quantifying, and managing the increasingly complex, uncertain, large-scale, and evolving natural, man-made, technological, and political crises and disasters.”
Quote in the paperSubstantial claim
Premisecitation
Today's spectrum of emergencies, crises, and disasters extends beyond the classic focus on isolated natural hazards and their management.
“As a result, today’s spectrum of ECDs goes beyond the classic focus on isolated natural hazards and their management.”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [2] Cao, L.: Metasynthetic Computing and Engineering of Complex Systems. Advanced Information and Knowledge Processing. Springer, New York (2015) source
Premisecitation
Emergencies, crises, and disasters have been increasing in frequency, ferocity, scale, and extent of devastating impacts.
“The uncertain world has seen increasing emergencies, crises and disasters (ECDs), such as the COVID-19 pandemic, hurricane Ian, global financial inflation and recession, misinformation disaster, and cyberattacks.”
- Also stated in: § Introduction · plain
  “Emergencies, crises, and disasters (ECDs) have been increasing in frequency, ferocity and their scale and extent of devastating impacts.”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [1] Lettieri, E., Masella, C., Radaelli, G.: Disaster management: findings from a systematic review. Disaster Prev. Manag. Int. J. 18(2), 117–136 (2009) source
Interpretationreasoning
Classic emergency, crisis, and disaster management lacks proactive, adaptive, generalized, and end-to-end foundations and capacities for full-spectrum, full-cycle, large-scale, uncertain, and evolving multi-hazard resilience.
“However, they lack proactive, adaptive, generalized, and end-to-end foundations and capacities for full-spectrum, full-cycle, large-scale, uncertain, and evolving multi-hazard ECD resilience.”
Quote in the paperSubstantial claim
Premisecitation
AIDS techniques extend beyond shallow and deep analytics and learning.
“AIDS techniques are not limited to shallow and deep analytics and learning.”
Quote in the paperSubstantial claim0 of 1 sources checked
- cites [29] Cao, L.: A new age of AI: features and futures. IEEE Intell. Syst. 37(1), 25–37 (2022) source
Interpretationnone given
Research capabilities and knowledge are concentrated on modeling post-disaster response, assessing damage and risk, and predicting disasters.
“Intensive research capabilities and knowledge are on post-disaster response modeling, damage assessment, risk assessment, and disaster prediction.”
Quote in the paperNot a substantial claim
Interpretationreasoning
AISDR research exhibits an imbalance in ECD methods.
“Last but not least, there is an ECD method imbalance.”
Quote in the paperNot a substantial claim
Interpretationreasoning
The high-level methodological roles of the mitigation, preparedness, response, and recovery framework do not adapt to the expected objectives and performance for smart emergency, crisis, and disaster resilience.
“Its high-level methodological roles do not adapt to the expected objectives and performance for smart ECD resilience.”
Quote in the paperSubstantial claim
Interpretationreasoning
AISDR research exhibits an imbalance in ECD data.
“Fourth, there is an ECD data imbalance.”
Quote in the paperSubstantial claim
Interpretationreasoning
AISDR research exhibits an imbalance in the areas of AIDS that are studied.
“First, there is an AIDS research area imbalance.”
Quote in the paperSubstantial claim
Interpretationreasoning
AISDR research exhibits an imbalance across ECD phases.
“Third, there is an ECD phase imbalance.”
Quote in the paperSubstantial claim
Interpretationreasoning
AISDR research exhibits an imbalance across ECD types.
“Second, there is an ECD type imbalance.”
Quote in the paperSubstantial claim

May combine two claims. 9
Classified as probably not a substantial claim. 31

May combine two claims.
Premisecitation
COVID-19 also caused, worsened, or generated additional ECD events, including supply-chain disruption, misinformation, conspiracy, and geopolitical crises.
“it has also overwhelmingly caused, intensified and created other ECD events, such as supply chain disconnection, misinformation, conspiracy [9], and geopolitical crises [10].”
Quote in the paperNot a substantial claim2 of 2 sources checked
- cites [9] Batzdorfer, V., Steinmetz, H., Biella, M., Alizadeh, M.: Conspiracy theories on twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic. Int. J. Data Sci. Anal. 13(4), 315–333 (2022) source
- cites [10] Cao, L., Liu, Q.: COVID-19 modeling: a review. medRxiv 1–105 (2021) source
  The citation to this source is attached to “geopolitical crises.” Section 8.4 (p. 71) says COVID-19 reshaped national interests, political relations, and globalization, supporting a broad geopolitical impact. It does not say the pandemic caused, intensified, or created geopolitical crises specifically. (LLM, conf=0.83)
  Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Interpretationcitation
AISDR can benefit from the practice and lessons learned from successful AIDS applications in other domains such as AI in finance.
“AISDR can benefit from the practice and lessons learned from the successful AIDS applications in other domains such as AI in finance [6].”
Quote in the paperNot a substantial claim1 of 1 sources checked
- cites [6] Cao, L., Yang, Q., Yu, P.S.: Data science and AI in fintech: an overview. Int. J. Data Sci. Anal. 12(2), 81–99 (2021) source
Classified as probably not a substantial claim.
Interpretationreasoning
AI for smart disaster resilience plays a foundational role in enabling smart digital emergency, crisis, and disaster resilience.
“For these, AI for SDR (AISDR) plays a foundational role in enabling SDR.”
Quote in the paperSubstantial claim
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given
Research on wildfire, avalanche, and tsunami is much less and insufficient.
“Much less and insufficient research is available on wildfire, avalanche, and tsunami.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given
When structured data are limited, shallow machine-learning methods are reported to achieve better prediction accuracy.
“In cases with limited structured data, shallow machine learning methods report better prediction accuracy;”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given
The complexity of ECD data and the goals of smart ECD resilience pose challenges to existing domain-specific and disciplinary ECD management methods.
“Smart ECD resilience and the ECD data characteristics and complexities challenge the existing domain-specific and disciplinary ECD management and methods.”
Quote in the paperNot a substantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
- Rests on an assumption: The random and outlying nature of ECDs makes their data both general and special.
- Rests on an assumption: Real-world ECD events and their associated entities are often rare, random, and outlying, which present specific outlying data characteristics and complexities.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given
Deep neural networks are reported to perform well on images, videos, and texts that have annotated samples.
“while deep neural networks report high performance on images, videos, and texts with annotated samples.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given
Research has concentrated intensively on structured and annotated ECD data.
“Intensive research has been on structured and annotated ECD data.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given
Research is insufficient on emergent ECD events such as wildfire forecasting, AI disasters, machine failures, and cyberattacks.
“Insufficient research is on emergent ECD events such as wildfire forecasting, AI disasters, machine failures, and cyberattacks.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given
Shallow machine-learning methods are widely used for outlier detection, clustering, and classification in disaster early warning, risk and vulnerability assessment, and sensor-based disaster monitoring.
“Shallow machine learning methods are widely used for outlier detection, clustering, and classification of disaster early warning, risk and vulnerability assessment, and disaster monitoring on sensor data.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given
Much less effort has gone to early warning, disaster detection, and disaster monitoring.
“Much less efforts are on early warning, disaster detection, and disaster monitoring.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based).
Interpretationreasoning
The mitigation, preparedness, response, and recovery framework is decomposed and expanded into more fine-grained and collaborative phases for full-cycle, full-spectrum, and full-scale emergency, crisis, and disaster management.
“The MPRR framework is decomposed and expanded to more fine-grained and collaborative phases for full-cycle, full-spectrum, and full-scale ECD management.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation
Methods from specific domains and disciplines have seen wide use in disaster management.
“Domain-specific and disciplinary methods have been widely applied to disaster management [1], which are not focused in this article.”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [1] Lettieri, E., Masella, C., Radaelli, G.: Disaster management: findings from a systematic review. Disaster Prev. Manag. Int. J. 18(2), 117–136 (2009) source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone given
Deep neural networks are dominant for detecting disasters, analyzing sentiment, assessing damage, and evaluating response using video, aerial imagery, online news, social media, and space-time signals.
“Deep neural networks dominate disaster detection, sentiment analysis, damage assessment, and response evaluation on video, aerial imagery, online news, social media posts, and spatio-temporal signals.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given
Existing work is confined to simple applications centered on analytical and machine-learning methods.
“The existing work only focuses on simple applications concentrated on analytical and machine learning methods.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given
There are no systematic AIDS theories and tools oriented to ECDs.
“No ECD-oriented systematic AIDS theories and tools are available.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone given
AI for smart disaster resilience transforms classic reactive and scripted disaster management into digital proactive and intelligent resilience across emergency, crisis, and disaster ecosystems.
“AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation
A typical emergency, crisis, and disaster management framework for natural hazards and health emergencies consists of four phases: mitigation, preparedness, response, and recovery.
“A typical ECD management framework for managing natural hazards and health emergencies consists of four phases: mitigation, preparedness, response, and recovery (MPRR) [4].”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [4] Albarakati, N., Obradovic, Z.: Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department. Int. J. Data Sci. Anal. 8(4), 385–403 (2019) source
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationreasoning
AI for smart emergency, crisis and disaster resilience (AIECD), also called AI for smart disaster resilience (AISDR) and AI for Natural Disaster Management (AI4NDM), is emerging as a critical AI application and a new AI area that can transform ECD management into SDR.
“From the AIDS perspective, AI for smart emergency, crisis and disaster resilience (AIECD), or simply, AI for smart disaster resilience (AISDR) and AI for Natural Disaster Management (AI4NDM),6 emerges as a critical AI application and a new AI area, enabling to transform ECD management to SDR.”
Quote in the paperNot a substantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone given
The mitigation, preparedness, response, and recovery framework is problem-specific, process-focused, passive, coarse, and lacks intelligence.
“However, MPRR are problem-specific, process-focused, passive, and coarse, and lack intelligence.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationreasoning
AISDR is emerging as a critical and foundational area for developing translational AIDS theories and tools to understand, quantify and manage ECD challenges and complexities.
“AISDR7 emerges as a critical and foundational area to develop translational AIDS theories and tools to deeply understand, quantify and manage ECD challenges and complexities.”
Quote in the paperNot a substantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given
Widely available ECD data encourage and support data-driven and evidence-based digital ECD resilience.
“The widely available ECD data further inspire and support data-driven, and evidence-based digital ECD resilience.”
Quote in the paperNot a substantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation
The emergency, crisis, and disaster landscape spans a very wide range of events and forms open complex systems with significant system complexities.
“The ECD landscape overspread a very wide range of E, C, and D events, forming open complex systems with significant system complexities [2].”
Quote in the paperNot a substantial claim0 of 1 sources checked
- cites [2] Cao, L.: Metasynthetic Computing and Engineering of Complex Systems. Advanced Information and Knowledge Processing. Springer, New York (2015) source
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationreasoning
Disciplinary ECD management focuses on domain-specific problems and cannot satisfy the goals of smart ECD resilience, lacking intelligence and data-driven discovery.
“Disciplinary ECD management focuses on domain-specific problems and their challenges and resolution, while is incapable of satisfying the goals for smart ECD resilience and lacks intelligence and data-driven discovery.”
Quote in the paperNot a substantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
Probably not a substantial claim: may not pass S2 (checkable).
Interpretationreasoning
These capabilities provide capacity building, enhancement, and optimization for historical, present, and future emergency, crisis, and disaster events, risk, and impacts.
“These provide the capability and capacity building, enhancement and optimization of historical, present and future ECD events, risk and impacts for smart ECD resilience.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone given
AIDS, including shallow machine learning and deep learning, have been increasingly applied to ECD management but focus on simple applications without deep understanding of ECD characteristics and complexities.
“AIDS, including shallow machine learning and deep learning, have been increasingly applied to ECD management, which focus on simple applications without a deep understanding and characterization of ECD characteristics and complexities.”
Quote in the paperNot a substantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given
Research has concentrated intensively on floods, earthquakes, hurricanes, and landslides.
“In the broad spectrum of ECDs, intensive research has been on floods, earthquakes, hurricanes, and landslides.”
Quote in the paperNot a substantial claim
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone given
Forecasting results may inform prevention.
“Forecasting results may inform prevention.”
Quote in the paperNot a substantial claim
Classified as probably not a substantial claim.
Interpretationreasoning
AISDR addresses various gaps and limitations in existing ECD management, both with and without advanced AIDS.
“Second, AISDR addresses various gaps and limitations in existing ECD management with and without using advanced AIDS.”
Quote in the paperSubstantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
- Rests on an assumption: Crisis and disaster management is experiencing significant paradigm shifts.
Classified as probably not a substantial claim.
Interpretationreasoning
Digital ECD resilience offers new opportunities for intelligent ECD management.
“Third, digital ECD resilience, or digital disaster, presents new opportunities for intelligent ECD management.”
Quote in the paperSubstantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationreasoning
Disaster AI and disaster data science are emerging as a new AIDS area and a new generation of ECD management.
“Lastly, disaster AI and disaster data science (disaster AIDS for short) emerge as a new AIDS area and a new generation of ECD management”
Quote in the paperNot a substantial claim
- Rests on an assumption: ECD events and management produce large and complex data.
- Rests on an assumption: ECD data present various data characteristics and complexities.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationreasoning
Smart emergency, crisis, and disaster resilience enables proactive and early contingency planning, prevention, prediction, and warning before and during events; active real-time detection, identification, recognition, planning, and management; and strategic next-best action recommendation and decision-making.
“Smart ECD resilience enables proactive and early contingency planning, prevention, prediction and warning during normal time and prior to ECD events; active and online real-time and evolving detection, identification, recognition, planning, and management; and strategic and next-best action and strategy recommendation and decision-making.”
Quote in the paperNot a substantial claim

32 of 32

The paper relies on this without support.
Successful AI and data science applications in other domains can be transferred to emergency, crisis, and disaster resilience.
“Taking the lessons learned from successful AIDS applications in other domains [5, 6], a new-generation smart ECD resilience framework emerges.”
The paper relies on this without support.
Multi-hazards often couple, co-exist, and co-evolve, resulting in coupled hazards that are very challenging to handle.
“Often, multi-hazards couple, co-exist and co-evolve, resulting in coupled hazards, making things very challenging to handle.”
The paper relies on this without support.
All emergency, crisis, and disaster events cause serious consequences and impacts and require immediate actions.
“All E, C and D events cause serious consequences and impacts, requiring immediate actions.”
The paper relies on this without support.
New emergencies, crises, and disasters emerge every day and become increasingly common, uncertain, and devastating.
“New ECDs emerge every day and become increasingly common, uncertain but devastating.”
The paper relies on this without support.
Technological disasters and other emerging events such as geopolitical unrest and financial crises further diversify the classic emergency, crisis, and disaster spectrum.
“Technological disasters such as cyberattacks, AI weapons, and information disorder, and other emerging events such as geopolitical unrests (e.g., the Ukraine war), and financial crises as the global recession further diversify the classic ECD spectrum.”
The paper relies on this without support.
Some hazards or disasters may be coupled or associated with each other, forming multi-hazards or coupled hazards.
“In reality, some hazards or disasters may be coupled or associated with each other, forming multi-hazards or coupled hazards.”
The paper relies on this without support.
Multiple disasters may present sequentially under certain conditions.
“Multiple disasters may present sequentially under certain conditions.”
The paper relies on this without support.
Emergency, crisis, and disaster management has involved broad-reaching communities, authorities, practitioners, and the public.
“From operational perspectives, ECD management has involved broad-reaching communities, authorities, practitioners, and the public.”
The paper relies on this without support.
An emergency or crisis may escalate into a disaster.
“an emergency or crisis may escalate into a disaster.”
The paper relies on this without support.
The mitigation, preparedness, response, and recovery framework has also been widely applied in non-health disaster management, particularly natural disaster management.
“This MPRR framework has also been widely applied in non-health disaster management, in particular, natural disaster management.”
The paper relies on this without support.
Human-made crises and disasters have become increasingly uncertain, frequent, and devastating, forming a serious threat or concern in contemporary societies.
“In recent years, human-made crises and disasters have been diversified and concerning, such as civil wars, invasion wars, cyberattacks, refugee crises, machine failures, technological misuses, and AI weapons. They have become increasingly uncertain, frequent, and devastating, forming a serious threat or concern in contemporary societies.”
The paper relies on this without support.
ECD events and management produce large and complex data.
“ECD events and management generate large, and complex data.”
The paper relies on this without support.
Different ECD events involve their own data and data characteristics and complexities.
“Different ECD events involve their respective data and data characteristics and complexities.”
The paper relies on this without support.
Understanding and quantifying ECD data characteristics and complexities is foundational for enabling smart digital ECD resilience.
“Understanding and quantifying ECD data characteristics and complexities is foundational for enabling smart digital ECD resilience.”
The paper relies on this without support.
ECD data present various data characteristics and complexities.
“ECD data present various data characteristics and complexities.”
The paper relies on this without support.
The random and outlying nature of ECDs makes their data both general and special.
“The random and outlying nature of ECDs makes their data both general and special.”
The paper relies on this without support.
ECD data often involve multi-domain, multi-modal, multi-scale, multi-grain, multi-source, or multi-view ECD sources.
“Multiplicity ECD data often involve multi-domain, multi-modal, multi-scale, multi-grain, multi-source, or multi-view ECD sources”
The paper relies on this without support.
ECD-sensitive factors and data share multi-scale, hierarchical or multi-grain differences.
“Granularity ECD-sensitive factors and data share multi-scale, hierarchical or multi-grain differences.”
The paper relies on this without support.
ECD data often involve significant diversity in their types, formats, structures, distributions, or relations.
“Heterogeneity ECD data often involve significant diversity in their types, formats, structures, distributions, or relations; or differences in recording frequency, format, timing, granularity, meaning, semantics, or period.”
The paper relies on this without support.
Diverse ECD factors, events and objects may interact or couple with each other.
“Interaction and coupling diverse ECD factors, events and objects may interact or couple with each other; they may be associated, correlated, dependent, or causally related.”
The paper relies on this without support.
ECD data may be decentralized or distributed.
“Decentralization ECD data may be decentralized, or distributed; they may involve distributed or decentralized factors, individuals, or facilities widely spread across the affected regions or communities.”
The paper relies on this without support.
ECD data include lifelong data about past, present and future ECD activities, or specific data such as high-frequency and real-time streaming imagery.
“Timing lifelong data about past, present and future ECD activities, or specific data such as with high-frequency and real-time streaming imagery about a landslide.”
The paper relies on this without support.
ECD factors are associated with different frequencies in recording or acquisition.
“Frequency ECD factors are associated with different frequencies in recording or acquisition.”
The paper relies on this without support.
ECD events and their factors and effects are often uncertain, showing high randomness, unpredictability, or unreliability.
“Uncertainty ECD events and their factors and effects are often uncertain; showing high randomness, unpredictability, or unreliability.”
The paper relies on this without support.
ECD events, effects and impacts are often progressive, evolving, or changing, presenting nonstationarity or variance over time, frequency, or value domain.
“Dynamics ECD events, effects and impacts are often progressive, evolving, or changing; presenting nonstationarity or variance over time, frequency, or value domain.”
The paper relies on this without support.
Real-world ECD events and their associated entities are often rare, random, and outlying, which present specific outlying data characteristics and complexities.
“On the other hand, real-world ECD events and their associated entities are often rare, random, and outlying, which present specific outlying data characteristics and complexities.”
The paper relies on this without support.
ECD events may be blind, unknown, or novel.
“First, ECD events may be blind, unknown, or novel.”
The paper relies on this without support.
Unusual ECDs may involve changing, outlying, sparse, infrequent, insufficient, incomplete, inconsistent, or irregular properties in their systems and data.
“Second, unusual ECDs may involve changing, outlying, sparse, infrequent, insufficient, incomplete, inconsistent, or irregular properties in their systems and data.”
The paper relies on this without support.
ECD data may involve quality issues, such as bias, error, or missing values.
“Lastly, ECD data may also involve quality issues, such as bias, error, or missing values.”
The paper relies on this without support.
ECD events may present little to no forewarning signals or indications, and are unable to be perceived or forecasted.
“Blindness ECD events may present little to no forewarning signals or indications, and are unable to be perceived or forecasted; resulting in lack of awareness or judgment.”
The paper relies on this without support.
ECD factors, events or effects may be unseen, unknown, unrecognizable, open, invisible, or hidden.
“Unknownness ECD factors, events or effects may be unseen, unknown, unrecognizable, open, invisible, or hidden; resulting in no, hidden, or unknown observations or no sensors capable to monitor or detect them.”
The paper relies on this without support.
Crisis and disaster management is experiencing significant paradigm shifts.
“First, crisis and disaster management is experiencing significant paradigm shifts”
The paper relies on this without support.
AISDR is critical for saving lives and reducing losses.
“The criticality of AISDR in saving lives and losses requires its high system usability and actionability.”
The paper relies on this without support.
AIDS is increasingly important for reducing domain-specific knowledge and capability gaps and for complementing disciplinary sciences in smart ECD resilience.
“AIDS is playing an increasing important role in mitigating the domain-specific knowledge and capability gaps and complementing the domain-specific disciplinary sciences for smart ECD resilience.”

34 of 34

Applications of artificial intelligence for disaster management
Sun, W., Bocchini, P., & Davison, B. D. (2020) · Natural Hazards · doi:10.1007/s11069-020-04124-3 · Cited by 491 · cited 2× · see citing works
“The applications of the above methods can be seen in various ECD problems, objectives, and developments over the lifespan of ECD [20–22].”
Method
The disaster of misinformation: a review of research in social media
Muhammed Sadiq, T., & Mathew, S. K. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00311-6 · Cited by 347 · cited 1× · see citing works
“Technological disasters include informational hazards such as cyberattacks, information disorder, infodemic, or fake news [3]; technical facility disasters such as machine disasters, information infrastructure attacks, critical infrastructure failures, network and communication failures, robotic attacks, or ergonomic…”
Background
Machine Learning in Disaster Management: Recent Developments in Methods and Applications
Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4020020 · Cited by 319 · cited 1× · see citing works
“…modeling, simulation and deep learning methods, have been widely applied to all phases of the MPRR framework for disaster management [20, 21]: (cid:129) mitigation: e.g., for assessing vulnerability, forecast risks, estimating impacts, and analyzing strategies; (cid:129) preparedness:…”
Method
Disaster management: findings from a systematic review
Lettieri, E., Masella, C., & Radaelli, G. (2009) · Disaster Prevention and Management: An International Journal · doi:10.1108/09653560910953207 · Cited by 284 · cited 2× · see citing works
“Domain-specific and disciplinary methods have been widely applied to disaster management [1], which are not focused in this article.”
BackgroundMethod
Data science and AI in FinTech: an overview
Cao, L., Yang, Q., & Yu, P. S. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00278-w · Cited by 238 · cited 2× · see citing works
“Taking the lessons learned from successful AIDS applications in other domains [5, 6], a new-generation smart ECD resilience”
BackgroundSelf-citation
Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities, Challenges, and Future Directions
Imran, M., Ofli, F., Caragea, D., & Torralba, A. B. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102261 · Cited by 227 · cited 1× · see citing works
“• Natural language processing techniques applied to disaster monitoring through online news or social media posts [24]; sentiment analysis by classifying sentiment into positive, neutral or negative; estimating socio-economic disaster impact from social media data; and crisis mapping in real-time social media.”
Background
Resilience against crises: COVID-19 and lessons from natural disasters
Sakurai, M., & Chughtai, H. (2020) · European Journal of Information Systems · doi:10.1080/0960085x.2020.1814171 · Cited by 191 · cited 1× · see citing works
“COVID-19 have been not only a global health emergency, crisis and disaster [7, 8], it has also overwhelmingly caused, intensified and created other ECD events, such as supply chain disconnection, misinformation, conspiracy [9], and geopolitical crises [10].”
Background
AutoML: state of the art with a focus on anomaly detection, challenges, and research directions
Bahri, M., Salutari, F., Putina, A., & Sozio, M. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00309-0 · Cited by 132 · cited 1× · see citing works
“…resilience management developing autonom ous monitoring, investigation, planning and recommendation systems and automated learning and analytics [30]; enabling autonomous sample collection and detection of water and air pollution; and enabling real-time outlier detection and behavior…”
Background
A New Age of AI: Features and Futures
Cao, L. (2022) · IEEE Intelligent Systems · doi:10.1109/mis.2022.3150944 · Cited by 76 · cited 1× · see citing works
“able planning, recommendation, and decision [29] are essential for AISDR enabling digital smart ECD resilience.”
BackgroundSelf-citation
Geo-Spatial Multimedia Sentiment Analysis in Disasters
Alfarrarjeh, A., Agrawal, S., Kim, S., & Shahabi, C. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.77 · Cited by 69 · cited 1× · see citing works
“• Multimedia and multi-modal modeling applied to multimodal disaster classificationbycombining satellite images with news or social media text or image posts; and multimedia sentiment analysis [25].”
Background
Combining probabilistic forecasts of COVID-19 mortality in the United States
Taylor, J. W., & Taylor, K. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.06.044 · Cited by 56 · cited 1× · see citing works
“…(cid:129) mathematical and statistical methods and models, such as time series analysis, Bayesian models, and stochastic compartmental models [18]; (cid:129) data-driven methods and models, including classic (such as kernel methods and text mining) and modern (such as large language models,…”
Method
COVID-19 Modeling: A Review
Cao, L., & Liu, Q. (2024) · ACM Computing Surveys · doi:10.1145/3686150 · Cited by 49 · cited 3× · see citing works
“Examples include [10]: (cid:129) diagnosis and treatment of COVID-19 diseases and its SARS-CoV-2 viruses, including through medical imaging analysis [11]; (cid:129) quantiﬁcation and understanding of the COVID-19 disease complexities, virus mutations, and disease resur-gences [12]; (cid:129)…”
BackgroundMethodSelf-citation
COVID-19 Modeling: A Review
Cao, L., & Liu, Q. (2022) · doi:10.1101/2022.08.22.22279022 · Cited by 49
Self-citation
Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic
Batzdorfer, V., Steinmetz, H., Biella, M., & Alizadeh, M. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00298-6 · Cited by 38 · cited 1× · see citing works
“COVID-19 have been not only a global health emergency, crisis and disaster [7, 8], it has also overwhelmingly caused, intensified and created other ECD events, such as supply chain disconnection, misinformation, conspiracy [9], and geopolitical crises [10].”
Background
Data Science Thinking: The Next Scientific, Technological and Economic Revolution
Cao, L. (2018) · Data Analytics · doi:10.1007/978-3-319-95092-1 · Cited by 38 · see citing works
Self-citation
Metasynthetic Computing and Engineering of Complex Systems
Cao, L. (2015) · Advanced Information and Knowledge Processing · doi:10.1007/978-1-4471-6551-4 · Cited by 36 · see citing works
Self-citation
A computational drug repurposing approach in identifying the cephalosporin antibiotic and anti-hepatitis C drug derivatives for COVID-19 treatment
Kumar, R., Kumar, V., & Lee, K. W. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104186 · Cited by 35 · see citing works
Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis
Liu, X., Hasan, M. R., Ahmed, K. A., & Hossain, M. Z. (2023) · BMC Bioinformatics · doi:10.1186/s12859-022-05127-6 · Cited by 33 · see citing works
COVID-19 forecasting using new viral variants and vaccination effectiveness models
Rashed, E. A., Kodera, S., & Hirata, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105986 · Cited by 31 · cited 1× · see citing works
“…mutations, and disease resur-gences [12]; (cid:129) efﬁcacy modeling of COVID-19 medical treatment, vaccinations and pharmaceutical interventions [13]; (cid:129) discovery of drugs, vaccines, and biomedical products [14]; (cid:129) effectiveness modeling of non-pharmaceutical interventions and…”
Background
MT-nCov-Net: A Multitask Deep-Learning Framework for Efficient Diagnosis of COVID-19 Using Tomography Scans
Ding, W., Abdel‐Basset, M., Hawash, H., et al. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3123173 · Cited by 24 · cited 1× · see citing works
“…as kernel methods and text mining) and modern (such as large language models, deep neural networks, and deep reinforcement learning) technologies [19]; (cid:129) domain-driven methods and models, such as epidemio-logicalcompartmentalmodels,medicalanalysis,biomedical analysis, and social…”
Modelling the epidemic dynamics of COVID-19 with consideration of human mobility
Du, B., Zhao, Z., Zhao, J., Yu, L., Sun, L., & Lv, W. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00271-3 · Cited by 23 · cited 1× · see citing works
“…imaging analysis [11]; (cid:129) quantiﬁcation and understanding of the COVID-19 disease complexities, virus mutations, and disease resur-gences [12]; (cid:129) efﬁcacy modeling of COVID-19 medical treatment, vaccinations and pharmaceutical interventions [13]; (cid:129) discovery of drugs,…”
Background
AI in Combating the COVID-19 Pandemic
Cao, L. (2022) · IEEE Intelligent Systems · doi:10.1109/mis.2022.3164313 · Cited by 22 · cited 1× · see citing works
“COVID-19 have been not only a global health emergency, crisis and disaster [7, 8], it has also overwhelmingly caused, intensified and created other ECD events, such as supply chain disconnection, misinformation, conspiracy [9], and geopolitical crises [10].”
BackgroundSelf-citation
An analysis of the impact of policies and political affiliation on racial disparities in COVID-19 infections and deaths in the USA
Hamilton, M. A., Hamilton, D., Soneye, O., Ayeyemi, O., & Jaradat, R. M. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00284-y · Cited by 13 · cited 7× · see citing works
“AI and data science have played a profound role in ﬁghting COVID-19 disasters.”
BackgroundInfluential
How Have Global Scientists Responded to Tackling COVID-19?Preprint
Cao, L., & Hou, W. (2022) · medRxiv · doi:10.1101/2022.08.16.22278871 · Cited by 13 · cited 1× · see citing works
“In the massive literature on modeling COVID-19 [10] and the global scientiﬁc response to COVID-19 [17], we can ﬁnd that comprehensive AI and data science technologies have been applied to address the above COVID-19 issues.”
MethodSelf-citation
Multi-domain and multi-view networks model for clustering hospital admissions from the emergency department
Albarakati, N., & Obradovic, Z. (2019) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0147-5 · Cited by 12 · cited 1× · see citing works
“A typical ECD management framework for managing nat-ural hazards and health emergencies consists of four phases: mitigation, preparedness, response, and recovery (MPRR) [4].”
Background
A survey on event and subevent detection from microblog data towards crisis management
Chowdhury, S. R., Basu, S., & Maulik, U. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-022-00335-y · Cited by 12 · cited 1× · see citing works
“Examples of AISDR applications include: (cid:129) Unsupervised learning applied to assess and detect damages, damage areas or damage severity; event or subevent detection [23] in disaster.”
Background
Supercharging Crowd Dynamics Estimation in Disasters via Spatio-Temporal Deep Neural Network
Jiang, F., Zhong, L., Thilakarathna, K., et al. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.11 · Cited by 8 · cited 1× · see citing works
“• Spatio-temporal signal analysis applied to estimate crowd dynamics in spatio-temporal data by DNNs [26]; and financial crisis contagion by cross-market couplings and interactions [27].”
Background
Financial crisis and global market couplings
Cao, W., Demazeau, Y., Cao, L., et al. (2015) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2015.7344854 · Cited by 6 · cited 1× · see citing works
“• Spatio-temporal signal analysis applied to estimate crowd dynamics in spatio-temporal data by DNNs [26]; and financial crisis contagion by cross-market couplings and interactions [27].”
BackgroundSelf-citation
How Varying Intervention, Vaccination, Mutation and Ethnic Conditions Affect COVID-19 ResurgencePreprint
Cao, L., & Liu, Q. (2021) · medRxiv · doi:10.1101/2021.08.31.21262897 · Cited by 5 · cited 1× · see citing works
“…vaccinations and pharmaceutical interventions [13]; (cid:129) discovery of drugs, vaccines, and biomedical products [14]; (cid:129) effectiveness modeling of non-pharmaceutical interventions and policies [15]; (cid:129) characterization of social, economic and political impact of COVID-19 [16].”
BackgroundSelf-citation

Flagged first, then by citations

Sorted by citations

A Review of Practical AI for Remote Sensing in Earth Sciences
Janga, B., Asamani, G. P., Sun, Z., & Cristea, N. C. (2023) · Remote Sensing · doi:10.3390/rs15164112 · Cited by 219 · see citing works
COVID-19 Modeling: A Review
Cao, L., & Liu, Q. (2024) · ACM Computing Surveys · doi:10.1145/3686150 · Cited by 49 · see citing works
Self-citation
Sustainability benefits of AI-based engineering solutions for infrastructure resilience in arid regions against extreme rainfall events
Habib, M., Habib, A., Albzaie, M., et al. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00500-2 · Cited by 46 · see citing works
“Cao [61] discussed the broader applications of AI and data science in smart emergency, crisis, and disaster resilience.”
AI-enhanced healthcare management during natural disasters: conceptual insights
Abdul, S., Adeghe, E. P., Adegoke, B. O., et al. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i5.1155 · Cited by 44 · see citing works
Virtual Cities: From Digital Twins to Autonomous AI Societies
Nechesov, A. V., Dorokhov, I., & Ruponen, J. (2025) · IEEE Access · doi:10.1109/access.2025.3531222 · Cited by 40 · see citing works
Can small towns survive climate change? Assessing economic resilience and vulnerability amid major storms
Nazari, R., Karimi, M. E., Nikoo, M. R., et al. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145158 · Cited by 32 · see citing works
A Survey on anomaly detection in IoT: Techniques, challenges, and opportunities with the integration of 6G
Haider, Z. A., Zeb, A., Rahman, T., Singh, S. K., Akram, R., Arishi, A., & Ullah, I. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111484 · Cited by 39 · see citing works
Application of artificial intelligence in agri-tech, environmental and biodiversity conservation
Chatrabhuj, Meshram, K., Mishra, U., & Rathnayake, U. (2025) · Array · doi:10.1016/j.array.2025.100412 · Cited by 29 · see citing works
Measuring community resilience inequality to inland flooding using location aware big data
Qian, J., Du, Y., Liang, F., et al. (2024) · Cities · doi:10.1016/j.cities.2024.104915 · Cited by 24 · see citing works
Enhancing Pandemic Preparedness With AI-Driven Risk Prediction Models
Tariq, M. U. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-6577-9.ch005 · Cited by 19 · see citing works
Transforming Disaster Risk Reduction With <scp>AI</scp> and Big Data: Legal and Interdisciplinary Perspectives
Chun, K. P., Octavianti, T., Dogulu, N., Tyralis, H., Papacharalampous, G., Rowberry, R., Fan, P., Everard, M., Francesch‐Huidobro, M., Migliari, W., et al. (2025) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.70011 · Cited by 19 · see citing works
“Deploying AI systems in disaster-affected areas with resource-constrained environments is also challenging due to the need for substantial computational power, energy consumption, and adequate infrastructure (Cao 2023).”
Harnessing Mobile Technology for Flood Disaster Readiness and Response: A Comprehensive Review of Mobile Applications on the Google Play Store
Kangana, N., Kankanamge, N., Silva, C. K. D., et al. (2025) · Urban Science · doi:10.3390/urbansci9040106 · Cited by 17 · see citing works
“Investing in these technologies can lead to more sophisticated and reliable disaster management solutions [65,66].”
A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
Do, T.-T.-T., Thắng, H. Q., Kim, K., & Nguyen, V.-Q. (2025) · Applied Sciences · doi:10.3390/app15148089 · Cited by 23 · see citing works
A Logical Remote Sensing Based Disaster Management and Alert System Using AI-Assisted Internet of Things Technology
Nagaiah, K., Kalaivani, K., Radhakrishnan, P., et al. (2024) · Remote Sensing in Earth Systems Sciences · doi:10.1007/s41976-024-00139-7 · Cited by 14 · see citing works
“Compare the applications of AI in Internet of Things (IoT) they are used across various sectors [4, 5].”
AI for Smart Disaster Resilience among Communities
Zolkafli, A., Mansor, N. S., Omar, M. B., Ahmad, M. B., Ibrahim, H., & Yasin, A. B. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-67317-7_22 · Cited by 14 · see citing works
AI for Sustainable Emergency Preparedness and Response in Schools
Aldous, S. M., & Ismail, G. A. (2025) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3373-1917-9.ch003 · Cited by 13 · see citing works
Enabling people-centric climate action using human-in-the-loop artificial intelligence: a review
Debnath, R., Tkachenko, N., & Bhattacharyya, M. (2025) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2025.101482 · Cited by 12 · see citing works
Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review
Domfeh, E. A., & Dancy, C. L. (2025) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas65609.2025.11269624 · Cited by 21 · see citing works
“Cao [24] introduced the Smart Disaster Resilience (SDR) framework, expanding the traditional MPRR model with AI-driven capabilities such as early warning, forecasting, and multi-hazard management.”
Artificial Intelligence and Its Application in Disaster Risk Reduction in the Agriculture Sector
Neog, D. R., Singha, G., Dev, S., et al. (2024) · Disaster risk reduction · doi:10.1007/978-981-97-6671-0_15 · Cited by 12 · see citing works
Intelligent Method for Classifying the Level of Anthropogenic Disasters
Lipianina-Honcharenko, K., Wolff, C., Sachenko, A., Kit, I., & Zahorodnia, D. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7030157 · Cited by 12 · see citing works
Overcoming social and ethical challenges of AI through organizational resilience. A PLS-SEM approach
Sienkiewicz-Małyjurek, K., & Zyzak, B. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102210 · Cited by 14 · see citing works
An In-Depth Analysis of Approaches for Forecasting Extreme Rainfall Events Leveraging Artificial Intelligence, Machine Learning, and Deep Learning Techniques
Afandi, G. E., Moustafa, A., Ibrahim, S., & Irfan, M. H. (2025) · Earth Systems and Environment · doi:10.1007/s41748-025-00813-w · Cited by 8 · see citing works
“…Reinforcement Learning (RL), while still emerging in this field, shows potential for adaptive decision-making and dynamic alert generation, particularly in data-sparse dry and continental zones where environmental variability demands flexible response strategies (Materia et al. 2024; Cao 2023).”
БУХГАЛТЕРСЬКИЙ ОБЛІК ТА ФІНАНСОВА ЗВІТНІСТЬ В ІТ-СФЕРІ УКРАЇНИ: МОЖЛИВОСТІ ШТУЧНОГО ІНТЕЛЕКТУ
Онешко, С. В., Назаренко, О., Яремко, І., et al. (2023) · Financial and credit activity problems of theory and practice · doi:10.55643/fcaptp.5.52.2023.4151 · Cited by 8 · see citing works
Artificial intelligence in disaster management: achievements, challenges, and prospects
Xu, J., Nair, D. J., & Waller, S. T. (2025) · Natural Hazards · doi:10.1007/s11069-025-07667-5 · Cited by 12 · see citing works
Multi-hazard risk assessment and management: pathways for the Sendai Framework and beyond
Tiggeloven, T., Raymond, C., de Ruiter, M., Sillmann, J., Thieken, A. H., Buijs, S., Ciurean, R. L., Cordier, E., Crummy, J. M., Cumiskey, L., et al. (2026) · Geoscience Communication · doi:10.5194/gc-9-185-2026 · Cited by 8 · see citing works
“For example, AI, remote sensing, and digital modelling can uncertainty around risk characteristics and enhance the effectiveness of risk reduction strategies across multiple scales (Cao, 2023; Cheng et al., 2024; Rezvani et al., 2024).”
AI-Driven Smart Cities in France
-, R. E. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i03.21920 · Cited by 7 · see citing works
“By implementing comprehensive rules and inclusive policies, urban planners and politicians may guarantee that the advantages of AI are relatively dispersed, promoting sustainable development that benefits all members of society [6].”
Background
Assessing and Optimizing Urban Dynamic Resilience to Extreme Rainfall from Shock to Recovery
Zhuang, L., Wang, M., Liu, K., et al. (2026) · International Journal of Disaster Risk Science · doi:10.1007/s13753-025-00683-w · Cited by 6 · see citing works
“…provide powerful predictive capabilities, a significant challenge remains in untangling the complex, nonlinear interactions between resilience drivers and in translating these “black-box” findings into actionable, interpretable policy, a gap which this study aimed to address (Cao 2023).”
The use of artificial intelligence in public administration: Bibliometric analysis
Rekunenko, І., Kobushko, I., Dzydzyguri, O., Balahurovska, І., Yurynets Oksana, V., & Жук, О. (2025) · Problems and Perspectives in Management · doi:10.21511/ppm.23(1).2025.16 · Cited by 6 · see citing works
UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning
Liu, Y., Li, X., He, B., et al. (2024) · Drones · doi:10.3390/drones8110647 · Cited by 5 · see citing works
An Analysis of Resilience in Digital Business Ecosystems
Krauze, B. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-92471-2_13 · Cited by 5 · see citing works
Design of mTCN framework for disaster prediction a fusion of massive machine type communications and temporal convolutional networks
Umadevi, M., Kumar, J. A., Priyan, S. V., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-15397-8 · Cited by 4 · see citing works
Efficient AI Models for Extreme Edge Environments: A Comprehensive Review for Space, Underwater, and Disaster Zones
Kotian, A. L., Sheik, A., Akhilesh, H. R., B, A. R., & Amrutha, M. (2025) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca65293.2025.11089903 · Cited by 5 · see citing works
Artificial Intelligence for Enhancing Resilience
Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024) · Journal of Applied Artificial Intelligence · doi:10.2139/ssrn.4831911 · Cited by 232 · see citing works
“AI enhances disaster response primarily through improved predictive analytics [20-22].”
Background
Developing a social media firestorm scale: from conceptualization to AI-assisted validation
Nuortimo, K., Härkönen, J., Breznik, K., & Hannes, R. (2025) · Journal of Marketing Analytics · doi:10.1057/s41270-025-00439-x · Cited by 3 · see citing works
AI, Mindfulness, and Emotional Well-Being
Singha, R. (2023) · Advances in human and social aspects of technology book series · doi:10.4018/979-8-3693-0802-8.ch003 · Cited by 3 · see citing works
Being young and resilient in times of AI, disasters, and crises
Mitrović, V. (2025) · Stanovnistvo · doi:10.59954/stnv.659 · Cited by 3 · see citing works
“In other words, communication between actors in disasters massively moves to the communication between various stakeholders and AI (Cao 2023; Modgil et al. 2022; Modgil et al. 2022a; Pal and Funikul 2023; Rane, Choudhary and Rane 2024).”
AI and crisis leadership: Using the POP-DOC Loop to explore potential implications and opportunities for leaders
McNulty, E. J., Spisak, B. R., Marcus, L. J., Cheema, A. W., Dhawan, R., Hertelendy, A. J., & Novak, S. (2024) · Journal of Emergency Management · doi:10.5055/jem.0836 · Cited by 2 · see citing works
Governing AI Output in Autonomous Driving: Scalable Privacy Infrastructure for Societal Acceptance
Fujii, Y. (2025) · Future Transportation · doi:10.3390/futuretransp5030116 · Cited by 2 · see citing works
“Government agencies (such as police or disaster response centers) may utilize the data for public safety and emergency response [48].”
Advances in modelling the risk of benign and malignant lung nodules
Du, S., Wu, T., Wang, H., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1648548 · Cited by 2 · see citing works
“…backdrop of numerous data points, AI excels at extracting features from large-scale lung-nodule imaging data and building robust predictive models that help clinicians rapidly and accurately distinguish benign from malignant nodules, markedly improving diagnostic precision and ef ﬁ ciency (104).”
The effect of digitalization on disaster response in local government: Quasi-experimental evidence from smart city infrastructure in China
Gao, N., Lin, Z., & Yang, B. (2026) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2026.106043 · Cited by 2 · see citing works
Assessing the Impact of AI-Driven Systems on Automated Data Science
Sharma, B., Jayasudha, S., Dhabliya, D., et al. (2023) · doi:10.1109/icpsitiags59213.2023.10527638 · Cited by 2 · see citing works
“Chief among these is the possibility that the underlying algorithms or fashions used may be biased, leading to unreliable or wrong effects [2-5].”
Background
Hurricanes and Health: A Scoping Review of Recent Developments in Physical Injuries, Mental Health, and Emergency Interventions
Ngatuvai, M., Blaylock, T. C., Ngatuvai, R., et al. (2024) · Cureus · doi:10.7759/cureus.63973 · Cited by 2 · see citing works
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Quantitative Feasibility of Predictive Machine-Learning Optimization Algorithms for Refugee Routing
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Dynamic Evaluation of Digital Development Level of Emergency Management
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AI Implementation for Social Benefit. Use Cases in Public Sector
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Predictive Analytics in Mental Health: AI for Identifying Risk Factors and Preventing Crises
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US radiological disaster surveillance network using drone spectroscopy and cloud analytics: ensuring protection immediately post-incident
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“Cao (2023) highlighted the importance of AI and data science applications in smart emergency response systems, emphasizing the critical role of real-time data processing in effective disaster management.”
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Leveraging DNA Analysis in River Water: A Catalyst for Flood Prevention, Ecosystem Health, and Community Resilience
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Do Human-AI Teams Make Sense Under Pressure? A Systematic Review on Value of Information in Multi-Actor SystemsPreprint
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Afet Yönetiminde Açık Veri Yaklaşımı ve Kamu Yönetiminde Kapasitenin Etkin Kullanımı
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Authors: Cao, L.
Affiliations: University of Technology Sydney (AU)
Corresponding author: Cao, Longbing
ORCID: Cao, Longbing
Venue: International journal of data science and analytics
Publisher: Springer Science and Business Media LLC
Published: 2023-03-27
Volume: 15
Issue: 3
Pages: 231-246
ISSN: 2364-415X (ISSN-L)
2364-4168
DOI: 10.1007/s41060-023-00393-w
PMC: PMC10041487
OpenAlex: W4362552636
Cited by: 102
References: 29
Citations by year: 2026: 19 · 2025: 44 · 2024: 24 · 2023: 15
FWCI: 13.06
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: International Journal of Data Science and Analytics (publishedVersion)
PubMed (publishedVersion)
PubMed Central (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Keywords: artificial intelligence, disaster resilience, emergency management, crisis management, disaster management, data science, COVID-19 pandemic, Hurricane Ian

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