Cited by 201Regulation and Compliance StudiesArtificial Intelligence in LawData Mining and Machine Learning Applications

The DOI matches its Crossref record; 16 of the 17 references it cites were found in a registry or an index.

Machine learning for environmental monitoring

Hino, M., Benami, E., Brooks, N.·2018·Nature Sustainability

DOI 10.1038/s41893-018-0142-9

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1038/s41893-018-0142-9

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Untitled work
(2016) · Cited by 2,334
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040
Cognitive Biases and Heuristics in Medical Decision Making
Blumenthal-Barby, J. S. S., & Krieger, H. (2014) · Medical Decision Making · doi:10.1177/0272989x14547740 · Cited by 598
Prediction Policy Problems
Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584
Beyond prediction: using big data for policy problems
Athey, S. (2017) · Science · doi:10.1126/science.aal4321 · Cited by 575
An introduction to statistical finance
Bouchaud, J. (2002) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)01039-7 · Cited by 355
Enforcement and over-compliance
Shimshack, J. P., & Ward, M. (2007) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2007.05.003 · Cited by 207

Show all 17 references

Research works printed but not linked: 1

Zliobaite, I. Fairness-aware machine learning: a perspective. Preprint at https://arxiv.org/abs/1708.00754 (2017). · arXiv

Sorted by citations

Items 101–200 of 200

Ensuring Fairness in Federated Learning Services: Innovative Approaches to Client Selection, Scheduling, and Rewards
Zhang, M., Zhao, H. Q., Ebron, S. C., Xie, R., & Yang, K. (2024) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs60910.2024.00076 · Cited by 8
“Due to the privacy-preserving nature and the decentralized structure, the FL framework can be widely applied in many AI-driven applications where data are sensitive or legally restricted, such as smart healthcare [3], smart transportation [4], smart finance [5], and smart life [6].”
Large Language Modeling to Assist Natural Polyphenols as Green Precipitants for Recycling Spent Batteries
Huang, H., Chen, M., Zhang, Y., Wang, X., Xie, Q., Pu, Y., He, Y., Zhu, L., He, Y., & Guo, J. (2025) · Langmuir · doi:10.1021/acs.langmuir.4c04262 · Cited by 8
The use of big data in environmental quality monitoring and its impact on policy development: a bibliometric analysis
Lubis, S., Nurmandi, A., Ahmad, J., Purnomo, E. P., & Purwaningsih, T. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07265-x · Cited by 11
“For example, Hino, M, Benami, E, And Brooks N 2018 [26] focused on using artificial intelligence in environmental monitoring, but their research was not exhaustive enough from a bibliometric point of view.”
Machine Learning Based Approach for Sustainable Social Protection Policies in Developing Societies
Mumtaz, Z., & Whiteford, P. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01696-z · Cited by 7
C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing
Bhattacharya, A., Singha, M., Jha, A., & Banerjee, B. (2023) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3627631.3627669 · Cited by 7
Lenders’ Environmental Monitoring: Evidence From Environmental Covenants in Private Loan Contracts
Lee, R., & Zakota, M. (2024) · Journal of Accounting Auditing & Finance · doi:10.1177/0148558x241257973 · Cited by 6
“Prior literature indicates that the high heterogeneity in firms’ environmental activities and the lack of information prevent conventional regulatory approaches from adequately addressing the externality problem (Andersson et al., 2016; Hackett, 2006; Hino et al., 2018; Stavins, 2011).”
Machine Learning-Based Identification of Animal Feeding Operations in the United States On a Parcel-Scale
Saha, A., Rashid, B., Liu, T., Miralha, L., & Muenich, R. L. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.178312 · Cited by 6
Data technologies and analytics for policy and governance: a landscape review
Asensio, O. I., Moore, C. E., Ulibarrí, N., Simsekler, M. C. E., Lan, T., & Rivero, G. (2025) · Data & Policy · doi:10.1017/dap.2024.49 · Cited by 9
“Similar algorithms have been proposed for an increasing range of policy-relevant applications, such as environmental monitoring (Hino et al., 2018), preventing malfeasance in public procurement (Gallego et al., 2021), and restaurant hygiene inspections (Glaeser et al., 2016).”
Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act
Chugg, B., Rothbacher, N., FENG, A. S., Long, X., & Ho, D. E. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557104 · Cited by 9
“hope to inspire the community to help solve these challenging problems [13, 25].”
Background
RS 3 Lip: Consistency for remote sensing image classification on part embeddings using self-supervised learning and CLIP
Jha, A., Singha, M., Bhattacharya, A., & Banerjee, B. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104254 · Cited by 6
Predicting groundwater contamination to protect the storm-exposed vulnerable
Hochard, J., Abashidze, N., Bawa, R., Carr, G., Kirkland, B., Li, Y., Matlock, K., & Siu, W. Y. (2023) · Climate Risk Management · doi:10.1016/j.crm.2023.100499 · Cited by 6
22. Deploying Machine Learning for a Sustainable Future
Coglianese, C. (2019) · Yale University Press eBooks · doi:10.2307/j.ctvqc6gcq.26 · Cited by 6
A Systematic Review of Greenhouse Gas Emissions Derived From Combined Sewer Overflows and Synergistic Control Strategies Toward Carbon Neutrality
Xu, Y., Ye, C., Xu, Z., & Chu, W. (2025) · Engineering · doi:10.1016/j.eng.2025.03.027 · Cited by 7
Review on Artificial Intelligence in the Environmental Monitoring
Chauhan, B. V. S., Vedrtnam, A., Wyche, K. P., & Verma, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-6863-2_2 · Cited by 6
FrogDogNet: Fourier Frequency Retained Visual Prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing
Gunduboina, H., Khan, M. H., & Banerjee, B. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00222 · Cited by 5
“Remote Sensing (RS) image analysis underpins a diverse range of tasks including Earth observation [18], environmental monitoring [35, 90], urban planning [62], and disaster management [39, 104], to name a few.”
Big Data and Cloud Computing for Climate-Oriented Remote Sensing
Ravikumar, R. N., Aarthi, S., Nadarajan, S., Masharipova, G., Mamadiyarov, Z., & Tamilselvi, S. (2025) · Advances in geospatial technologies book series · doi:10.4018/979-8-3373-3501-8.ch006 · Cited by 5
Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges
Borgomeo, E., Holmes, L., Billari, C. G., Bitsios, I., Brown, S., Dickson, D. J., Ford, E., Fry, M., Gaffney, J., Garg, S., et al. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c15991 · Cited by 5
Machine Learning-Based Prediction of Ecosystem-Scale CO2 Flux Measurements
Leland, J., Uyekawa, J., Bergl, D. D., Liu, Y., Richardson, A. D., & Lucas, B. (2025) · Land · doi:10.3390/land14010124 · Cited by 5
Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
Lokman, A., Ismail, W. Z. W., & Aziz, N. A. A. (2025) · Algorithms · doi:10.3390/a18080494 · Cited by 5
Assessing Automated Administration
Coglianese, C., & Lai, A. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197579329.013.30 · Cited by 5
On the social-ecological systems (SES) diagnostic approach of the commons: Sharing, cooperation, and maintenance
Yan, Z. (2023) · PLOS Sustainability and Transformation · doi:10.1371/journal.pstr.0000057 · Cited by 5
Embedded Machine Learning for Machine Condition Monitoring
Grethler, M., Marinov, M. B., & Klumpp, V. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-78459-1_16 · Cited by 6
Banning wildlife trade can boost demand for unregulated threatened speciesPreprint
Kubo, T., Mieno, T., Uryu, S., Terada, S., & Veríssimo, D. (2022) · doi:10.31235/osf.io/s6gwu · Cited by 5
Smart control of oxychlorine species using reinforcement learning in saline electrochemical oxidation
Shin, Y.-U., Kim, D.-W., Yu, S. I., Bae, H., & Jang, A. (2025) · npj Clean Water · doi:10.1038/s41545-025-00530-x · Cited by 4
Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models
Majumder, R., Pollard, J., Salek, M. S., Werth, D. W., Comert, G., Gale, A., Khan, S. M., Darko, S., & Chowdhury, M. A. (2024) · Environmental Health Insights · doi:10.1177/11786302241227307 · Cited by 4
“According to recent studies [8] [9], the average time to perform mitigating procedures for fugitive CH 4 emissions will be significantly reduced by employing large-scale autonomous environmental emission detection systems requiring minimal human involvement.”
Background
Airborne particulate matter measurement and prediction with machine learning techniques
Iwaszenko, S., Smoliński, A., Grzanka, M., & Skowronek, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-70152-9 · Cited by 4
“7, 8, 9, 10, 11 and 12, respectively.”
Potential Breakthroughs in Environmental Monitoring and Management
Abubakar, A. M., Zakarya, I. A., Hasnain, M., Sarkinbaka, Z. M., Mukwana, K. C., & Abdo, A. (2024) · Advances in geospatial technologies book series · doi:10.4018/979-8-3693-8104-5.ch011 · Cited by 4
Combining Environmental Variables and Machine Learning Methods to Determine the Most Significant Factors Influencing Honey Production
Ramírez-Díaz, J., Manunza, A., de Oliveira, T. A., Bobbo, T., Nutini, F., Boschetti, M., Iorio, M. G. D., Pagnacco, G., Polli, M., Stella, A., et al. (2025) · Insects · doi:10.3390/insects16030278 · Cited by 4
“In complex biological data, different ML methods have been successfully used in ecology [22,23], environmental monitoring [17], plant species identification [24,25], flowering time [26,27], and animal science [18,28,29].”
Analysis of Artificial Intelligence on Environmental Sustainability
Kaswan, K. S., & Kumar, A. (2025) · International Conference on Communication, Security and Artificial Intelligence (ICCSAI) · doi:10.1109/iccsai64074.2025.11063908 · Cited by 4
“If you externalize this, then do let me know if there are any changes on this artifact or any additional insights required [16].”
Exploring the Potential of 2D TMD‐Based Optical Biosensors: Bridging Nanotechnology and Smart Diagnostics
Thapa, S., Malviya, H., Verma, R., & Singh, J. (2025) · The Chemical Record · doi:10.1002/tcr.202500149 · Cited by 3
Very-large-scale mimetic optogenetic synapses for physical reservoir computing
Han, X., Qi, Z., Kundrát, V., Li, H., Li, Z., Guo, X., Mao, P., Zheng, W., Hou, S., Liu, R., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-68229-8 · Cited by 4
Implications of Artificial Intelligence in Environmental Engineering
Bachu, N., & McDuffie, J. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i3.4804 · Cited by 3
Encoding and decoding illegal wildlife trade networks reveals key airport characteristics and undetected hotspots
Murray, H., Gore, M. L., & Dilkina, B. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02371-5 · Cited by 3
“Sustainabilitysciences have helped advance understanding of the causes and consequences of the crisis, but knowledge of IWT remains embryonic, and what exists is highly siloed 9 .”
Data-Driven Decision Making in the Public Sector: A Systematic Review
da Silva, R. S. S., Dias, C. M., & Lacerda, R. T. D. O. (2022) · International Journal of Advanced Engineering Research and Science · doi:10.22161/ijaers.99.21 · Cited by 5
ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions
Ji, T., Lin, Y., & Yang, Y. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.106190 · Cited by 3
Planting trees at the right places: Recommending suitable sites for growing trees using algorithm fusionPreprint
Rana, P., & Varshney, L. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.08002 · Cited by 3
“2 Machine learning (ML)-based models can assist forest rangers in choosing socially and ecologically appropriate sites for planting trees (Handan-Nader & Ho, 2019; Hino et al., 2018).”
Background
A Machine-Learning-Based Data Science Framework for Effectively and Efficiently Processing, Managing, and Visualizing Big Sequential Data
Cuzzocrea, A., Belmerabet, I., Hafsaoui, A., & Leung, C. K.-S. (2025) · Computers · doi:10.3390/computers14070276 · Cited by 2
Predictive modeling of major polluters based on the German PRTR emissions data
Thimm, H. H., & Schmidt, V. (2025) · Discover Sustainability · doi:10.1007/s43621-025-01541-x · Cited by 2
“Future research directions include extending the dataset with additional variables to enable innovative applications, such as optimizing inspection schedules and identifying potential environmental violations [20].”
Basics of machine learning and artificial intelligence methods in toxicology and environmental health
Chou, W., Chen, W., & Chen, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30010-3.00002-7 · Cited by 2
Analytical Study of Efficiency of Smart Ports
Akhter, H. (2025) · American Journal of Traffic and Transportation Engineering · doi:10.11648/j.ajtte.20251005.12 · Cited by 2
Random forests for dichotomous choice contingent valuation
Faccioli, M., & Moeltner, K. (2026) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2026.103303 · Cited by 2
Influence of Multi-source Precipitation Forcing on Deep Learning-based Runoff Prediction: A Multi-basin Comparative and Explainable Analysis
Tian, Y., Xiang, S., Tan, W., Wang, G., & Zhou, B. (2026) · Water Resources Management · doi:10.1007/s11269-026-04668-2 · Cited by 2
“(Hino et al. 2018 ).”
Privacy Meets Conservation: Federated Learning’s Revolution in Deforestation Detection
Poudel, S., Sainju, A. M., & Upadhyay, K. (2025) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot65859.2025.11105254 · Cited by 2
“Paper 1 [4] Paper 2 [5] Paper 3 [6] Paper 4 [7] Paper 5 [8] Paper 6 [9] Paper 7 [10] focusing on communication efficiency, client fairness, and robustness to noisy data.”
Using big data to accomplish a huge job
Rivers, L. (2018) · Nature Sustainability · doi:10.1038/s41893-018-0157-2 · Cited by 2
Smart Air-Quality Detection Using Regression Models
Abualrous, R., Zgheib, R., Mashnouq, A., Menon, P. S., Tamimi, R. A., & Takshe, A. A. (2024) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics63486.2024.10638296 · Cited by 2
“Criteria such as RMSE, MAE, Rˆ2, accuracy, precision, recall, and F1 score are employed to identify the most effective algorithm, with additional consideration given to how preprocessing strategies, algorithmic complexities, and dataset characteristics influence model performance [5].”
Method
Digital Technologies and Dynamic Resource Management
Bakker, K., Knight, R. J., Leape, J., Mackworth, A. K., Ng, R. T., & Ritts, M. (2020) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp50058.2020.00079 · Cited by 1
“The development of these decision-support applications is interdisciplinary, and draws on computer science, data science, ecology, economics, engineering, geography, law, oceanography, and political science [12, 13, 14, 15).”
Background
Assessing the Impact of Aviation Emissions on Air Quality at a Regional Greek Airport Using Machine Learning
Stefanis, C., Manisalidis, I., Stavropoulou, E., Stavropoulos, A., Tsigalou, C., Voidarou, C. C., Constantinidis, T. C., & Bezirtzoglou, E. (2025) · Toxics · doi:10.3390/toxics13030217 · Cited by 1
Utilizing Performance Parameters in Wastewater Treatment Plants for the Implementation of Wastewater-Based Epidemiology
Mora, E. M., Mora, E. J. C., Barrios-Hernández, M. L., & Chacón, L. (2025) · Water science and technology library · doi:10.1007/978-3-031-87461-1_8 · Cited by 1
Why Cumulative Environmental Problems Are Difficult and Implications for Law
Nelson, R. L. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009091930.004 · Cited by 1
Analytical Study of History and Development of Smart Port
Akhter, H. (2025) · Automation Control and Intelligent Systems · doi:10.11648/j.acis.20251303.15 · Cited by 1
Applications of Machine Learning and Artificial Intelligence in Soil and Environmental Sciences: Methods, Challenges, and Future Perspectives
Masmoudi, Y. A., Tallou, A., Ouaissa, M., & Ouaissa, M. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-032-06791-3_12 · Cited by 2
A Digital Twin Approach Integrating IoT and AI for Monitoring and Assessing Roof Degradation in Historic Buildings
Valentini, M., Brotto, P., Campana, P., Capponi, M., Colli, M., Rapuzzi, A., Rosso, P., Zani, S., & Vecchiattini, R. (2026) · Intelligent infrastructure and construction · doi:10.3390/iic2010002 · Cited by 1
“Data processing is carried out using artificial intelligence algorithms, particularly machine learning techniques, which learn to recognize recurring patterns during the training phase [7].”
AGPL-KEM : Attribute-guided prompt learning with knowledge experts mixture for few-shot remote sensing image classification
Wu, C., Zhu, C., Xu, Q., Liu, X., Zhang, Y., Wang, L., & Wu, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115375 · Cited by 1
Incorporating artificial intelligence into the future of stormwater management
Rahman, M., Smith, V. B., Kremer, P., Wadzuk, B., Jiao, X., Amur, A., & Arabi, S. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08488-2 · Cited by 2
Self-Organization Cooperative Output Regulation
Duan, P., Qian, J., Lv, Y., & Liu, T. (2022) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus55513.2022.9986537 · Cited by 1
“Multiple unmanned systems (MUSs) have received increasing research interests in control society due to its wide applications, including collaborative operation of industrial manipulators [1], cooperative monitoring of environment sensors [2], formation of unmanned aerial vehicle [3], etc.”
Background
Analysis of Environmental Protection and Economic Sustainability by Integrating Machine Learning Algorithms
Jiang, J. (2023) · International Conference on Computer Science and Automation Technology (CSAT) · doi:10.1109/csat61646.2023.00031 · Cited by 1
Ecological health assessment of natural water bodies by plankton
Zheng, X., Chen, Y., Zheng, W., Chen, H., Zhao, T., Hou, S., Wang, A.-J., & Ren, N.-Q. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14170-6.00010-x · Cited by 1
Advancement of Data Analysis, Decision Support System, Data-Driven Modeling on the Eighteenth ICMSEM Proceedings
Xu, J. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-97-5098-6_1 · Cited by 1
Reflections on “Can AI Understand Our Universe?”
Wang, Y. (2024) · International Journal of Modern Physics D · doi:10.1142/s0218271825400103 · Cited by 1
Challenges and future scope
López-Flores, F. J., Ochoa-Barragán, R., Raya-Tapia, A. Y., Márquez, C. R., & Ponce‐Ortega, J. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29058-9.00017-3 · Cited by 1
A Predictive Framework for Marine Microplastic Pollution using Machine Learning and Spatial Analysis
Amora, E. N. O. (2025) · International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) · doi:10.1109/icssas66150.2025.11080971 · Cited by 1
Procedural Fairness in Antitrust Enforcement: The U.S. PerspectivePreprint
Yoo, C. S., & Wendland, H. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3330791 · Cited by 1
Health Information Campaigns and Genetically Modified Food Labels in the Seafood Market
Weir, M. J. (2019) · doi:10.23860/diss-weir-michael-2019 · Cited by 1
“Recent applications of machine learning in policy prediction problems include environmental monitoring (Hino, Benami, and Brooks 2018), judicial behavior (Kleinberg et al. 2017), changes in household diet (Hut and Oster 2018; Emily Oster Forthcoming), poverty quantification (Blumenstock 2016; Jean…”
Background
Consumption Stimulus with Digital CouponsPreprint
Chen, Y., Li, M., Mao, J., & Zhou, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5848152 · Cited by 1
Using Machine Learning to Find Environmentally At-Risk CommunitiesPreprint
Shen, S. V. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3329557
Machine Learning-Based Approach for Exploring the Household Survey Data
Mumtaz, Z. (2022) · Springer eBooks · doi:10.1007/978-981-19-6474-9_7 · Cited by 1
Authors’ response to evaluations of “Banning wildlife trade can boost demand for unregulated threatened species”
Kubo, T., Terada, S., Uryu, S., Mieno, T., & Veríssimo, D. (2023) · doi:10.21428/d28e8e57.c58e9bde
Ways to Improve China's Ecological Governance Effectiveness through Artificial Intelligence
Zhan, H., & Liao, W. (2024) · Environment Resource and Ecology Journal · doi:10.23977/erej.2024.080120
Developing an Innovative Regression Model to Predict Industrial Facilities’ Risk Scores by Leveraging Environmental Compliance Assessments Data
Rady, A. E.-S., Zahran, A. A., Beheary, M. S., & El‐Metwally, M. (2024) · Earth and environmental sciences library · doi:10.1007/978-3-031-62672-2_9
Discussion on “Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models” by Bonas et al.
Otto, P. (2025) · Environmetrics · doi:10.1002/env.2898
“…methods in an online manner, such as a pre-trained model continuously predicting an environmental process (e.g., for environmental monitoring, Hino, Benami, and Brooks 2018, or Google’s MetNet-2 for precipitation forecasts, Espeholt et al. 2022), we rely on the quality characteristics…”
Identifying the Drivers and Uncertainties for Rainfall-Runoff Prediction with Explainable AlPreprint
Tian, Y., Tan, W., Wu, J., Wang, G., Zhou, B., & Jin, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5263019
Predicting adolescents’ environmental action: From individual to national-level factors using an explainable machine learning approach
Hu, B., Gao, J., Feng, Y., Xia, Z., Wang, K., & Geng, L. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.126398
Consumption Stimulus with Digital CouponsPreprint
Chen, Y., Li, M., Mao, J., & Zhou, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5319913
Water Quality Modelling Using Multilayer Neural Network and Multivariate Statistical Techniques
Syulekchieva, D., Popov, S., Midyurova, B., Dimitrov, A., & Sotirov, S. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-97309-3_9
Evaluating Machine Learning Algorithms for Environmental Impact Prediction
Pradhan, D., Subhadarshini, S., Padhy, N., Mahapatro, P. K., & Arangi, D. (2025) · IEEE Wireless Antenna and Microwave Symposium (WAMS) · doi:10.1109/wams64402.2025.11159051
“M. Hino, E. et al.. [1] state that the model predicts the most likely features of a facility in a water pollution inspection where they propose alternative inspection allocations that would target the high-risk facilities and how to prevent them.”
Information
Nelson, R. L. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009091930.007
Evaluation of Bangladesh Shipbuilding State and Major Challenges
Akhter, H. (2025) · Automation Control and Intelligent Systems · doi:10.11648/j.acis.20251303.13
AI Based Methods for Water Treatment, Conservation and Management
Juneja, D., Singh, A., Singh, J., Manchanda, K., & Singh, M. (2025) · International Conference on Engineering Innovations and Technologies (ICoEIT) · doi:10.1109/icoeit63558.2025.11211644
“Through operation scheduling optimization, prior knowledge of peak energy use hours, and control of the use of energy intensive equipment, AI decreases the cost and carbon footprint of water treatment plants [5].”
Conceptual Models of Data Collection, Forecasting and Preparation Processes for Professional Analysis in Environmental Monitoring Information Systems
Vadurin, K., Kramek, A., & Перекрест, А. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-3-032-09806-1_7
Design a Novel Information Management Mechanism to Proactively Control Pollution Governance Cheating
Pan, J. (2025) · doi:10.1109/temsconglobal64363.2025.11238328
“For example, researchers [10] [11] proposed applying machine learning-based significant data approaches to improve pollution monitoring effectiveness.”
Local information, mismatch, and the cost of randomization in regulatory enforcement
Xiao, Y., Liu, M., Liu, M., & Zhang, B. (2026) · Journal of Public Economics · doi:10.1016/j.jpubeco.2026.105616
A systematic literature review of US environmental violation studies
Chen, X. (2026) · Journal of Accounting Literature · doi:10.1108/jal-06-2025-0321
A GeoAI framework for detecting risk zones from illegal dumping sites in Khulna, Bangladesh
Das, S., & Morshed, M. M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0345178
“These findings align with the growing recognition that ML models can play a transformative role in environmental risk detection and management [19].”
Blind spots in environmental permitting: An AI-assisted assessment of coverage gaps and environmental risk among large dairy farms in Wisconsin
Bhaskar, M., Haslund-Gourley, A., Treves, R. J., & Ho, D. E. (2026) · Environmental Challenges · doi:10.1016/j.envc.2026.101615
Computational Approaches for Air Pollution Assessment
Yadav, A. K., & Maity, S. (2026) · Springer eBooks · doi:10.1007/978-3-032-13953-5_1
Intelligent water environment diagnosis and treatment center: A machine learning framework for automated surface water pollution management
Zhang, X., Fu, D., Li, R., Li, K., Zhu, X., Chen, C., Deng, H., & Sun, L. (2026) · Water Research · doi:10.1016/j.watres.2026.126108
Machine Learning for Earth Observation
Roy, S., & Goyal, M. K. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-19935-5_3
Process-based quantification of accumulation and interaction-driven synergistic risks of microplastics and heavy metals in freshwater sedimentsPreprint
He, B., Ji, Y., Guo, J., Ayoko, G. A., McGree, J. M., Zeng, R. J., & Goonetilleke, A. (2026) · Research Square · doi:10.21203/rs.3.rs-9666922/v1
The potential of spatial machine learning to detect and map Escherichia coli and coliform bacteria concentrations in groundwater
Al‐Kindi, K. M., & Al-Thani, G. S. S. (2026) · Applied Water Science · doi:10.1007/s13201-026-02870-6
“Their algorithms can process large datasets to identify patterns and correlations that traditional statistical methods may overlook, thereby improving the prediction accuracy of coliform and E. coli concentration (Hino et al. 2018).”
Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures
Nguyen, T. H., Akdiş, C. A., Baccarelli, A., Banerjee, S., Bhowmik, R. T., Coull, B. A., Childs, M. L., Pinna, L. C., Golden, C. D. W., Härtung, T., et al. (2026) · Allergy · doi:10.1111/all.70401
“For example, ML has been used to predict water quality violations before they occur, serving as an early warning system [97–99].”
Introduction to Environmental Monitoring
Bhardwaj, V., Sandhu, H. A. S., & Setia, B. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898813789126010004
Classical and Quantum Artificial Intelligence and Blockchain Technologies for Next-Generation Biosensing Systems
Mpofu, K. T., Tsebesebe, N., & Manoto, S. L. (2026) · Biomedical engineering · doi:10.5772/intechopen.1016231
Application of Machine Learning in Environmental Monitoring
Kaur, C., Swarnapudi, S., Chaudhary, A., Kaur, P., Sharma, P., & Chandni, B. S. P. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898813789126010005
Interactive Web Platform for Biological Research Visualization
Zanaj, E., Capoj, R., Lushnjari, K., Cipi, E., & Nasto, I. (2026) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco70748.2026.11579174
“IoT-based sensor networks enable continuous real-time collection of environmental data such as temperature, salinity, and water quality, improving the accuracy and efficiency of ecosystem analysis [12], [13].”
Continuous Well-Being Monitoring Through Interpretable Machine Learning
Alomair, M., Tubadji, A., & Boy, F. (2026) · Oxford University Press eBooks · doi:10.1093/9780198945215.003.0190
Economic Sanctions: Ownership Regimes vs Formal Rights
Fedorov, S., & Shastitko, A. (2026) · World Economy and International Relations · doi:10.20542/0131-2227-2026-70-8-27-37
Using Machine Learning to Predict Corporate Environmental Violation: A Stakeholder Pressure Perspective
Chen, X., Ding, N., Jin, H., Xue, R., & Yang, Y. (2026) · Business Strategy and the Environment · doi:10.1002/bse.71430
Artificial intelligence technologies in the management of environmental educational projects: A pedagogical experiment
Shuppe, L., Kolіada, N., Stanislavenko, L., Lazarovych, N., Pliushch, V., Balanutsa, O., Володимирівна, О. Т., Semenova, O., Ostapets, Y., Liashenko, R., et al. (2026) · Multidisciplinary Reviews · doi:10.31893/multirev.2027085
“The present analysis demonstrates how machine learning approaches can guide the optimal distribution of these scarce resources, while explicitly accounting for practical obstacles such as potential framework manipulation and inherent structural constraints Hino et al. (2018).”
A quantum-inspired annealing algorithm for cost-efficient sampling and 3D characterization of soil heavy-metal contamination
Wu, X., Wang, S., Wang, Y., & Li, Y. (2026) · Environmental Technology & Innovation · doi:10.1016/j.eti.2026.105203
Integrating Machine Learning Into Environmental Engineering Education: Design, Implementation, and Outcomes of an Interdisciplinary Course
Gorgoglione, A., Otegui, X., & Goliatt, L. (2026) · Computer Applications in Engineering Education · doi:10.1002/cae.70270
“Approaches such as project‐based learning applied to authentic environmental challenges have additionally been associated with improved readiness for professional and research contexts [28, 29].”

Authors: Hino, M., Benami, E., & Brooks, N.
Affiliations: Stanford University (US)
Corresponding author: Benami, E.
ORCID: Hino, M.
Benami, E.
Brooks, N.
Venue: Nature Sustainability
Publisher: Springer Science and Business Media LLC
Published: 2018-10-01
Volume: 1
Issue: 10
Pages: 583-588
ISSN: 2398-9629 (ISSN-L)
DOI: 10.1038/s41893-018-0142-9
OpenAlex: W2894463230
Cited by: 201
References: 17
Citations by year: 2026: 27 · 2025: 55 · 2024: 41 · 2023: 22 · 2022: 19 · 2021: 16 · 2020: 8 · 2019: 11 · 2018: 1
FWCI: 15.04
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Sustainability (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Social Sciences › Public Administration › Regulation and Compliance Studies
Social Sciences › Law › Artificial Intelligence in Law
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: environmental law enforcement, Clean Water Act, resource allocation, gaming the system, regulatory compliance, environmental monitoring, big data, predictive analytics, Environmental Protection Agency

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Machine learning for environmental monitoring

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks