Machine learning for environmental monitoring
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring.
“Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring.”
From Artificial intelligence, systemic risks, and sustainability (2021) · cites this paper as [27]
Cites
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 · see citing works
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 · see citing works
(2016) · Cited by 2,334 · see citing works
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 · see citing works
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 · see citing works
Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015) · American Economic Review · doi:10.1257/aer.p20151023 · Cited by 584 · see citing works
Beyond prediction: using big data for policy problems
Athey, S. (2017) · Science · doi:10.1126/science.aal4321 · Cited by 575 · see citing works
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 554 · see citing works
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 · see citing works
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 · see citing works
Does Machine Learning Automate Moral Hazard and Error?
Mullainathan, S., & Obermeyer, Z. (2017) · American Economic Review · doi:10.1257/aer.p20171084 · Cited by 156 · see citing works
Targeting enforcement to improve compliance with environmental regulations
Friesen, L. (2003) · Journal of Environmental Economics and Management · doi:10.1016/s0095-0696(02)00033-5 · Cited by 138 · see citing works
Predicting and Preventing Shootings among At-Risk Youth
Chandler, D. R., Levitt, S. D., & List, J. A. (2011) · American Economic Review · doi:10.1257/aer.101.3.288 · Cited by 61 · see citing works
Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online ReviewsPreprint
Kang, J. R. W., Kuznetsova, P., Choi, Y., & Luca, M. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2293165 · Cited by 42 · see citing works
Technological Innovation, Data Analytics, and Environmental Enforcement
Glicksman, R. L. (2017) · Ecology law quarterly · doi:10.15779/z38c53f16c · Cited by 14 · see citing works
Rivers, L., Dempsey, T. D., Mitchell, J., & Gibbs, C. (2015) · Journal of Environmental Assessment Policy and Management · doi:10.1142/s1464333215500337 · Cited by 9 · see citing works
Flagged first, then by citations
1 of the 17 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Our future in the Anthropocene biosphere
Folke, C., Polasky, S., Rockström, J., Galaz, V., Westley, F. R., Lamont, M., Scheffer, M., Österblom, H., Carpenter, S. R., Chapin, F. S., et al. (2021) · AMBIO · doi:10.1007/s13280-021-01544-8 · Cited by 770 · see citing works
Artificial intelligence, systemic risks, and sustainability
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., et al. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101741 · Cited by 497 · see citing works
“Examples include AI applications in climate and Earth system modeling [25,26]; AI-augmented environmental monitoring [27]; autonomous underwater marine conservation interventions and data collection [28,29]; AI-supported tracking of illegal wildlife trade [30]; and “ smart ” urban planning for…”
BackgroundMachine learning for hydrologic sciences: An introductory overview
Xu, T., & Liang, F. (2021) · Wiley Interdisciplinary Reviews Water · doi:10.1002/wat2.1533 · Cited by 269 · see citing works
“…has been used in environmental monitoring applications such as predicting recreational water quality advisories (Brooks et al., 2016), estimating groundwater nitrate concentration (Nolan et al., 2015), and identifying facilities likely to violate environmental regulations (Hino et al., 2018).”
Fascista, A. (2022) · Sensors · doi:10.3390/s22051824 · Cited by 259 · see citing works
“As a fundamental enabler for most monitoring tasks, predictive analytics are required to combine big data and machine learning/deep learning and predict future evolution and impacts of environmental phenomena using both data-driven and model-based approaches [450].”
BackgroundRole of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions
Bhaiyya, M. L., Panigrahi, D., Rewatkar, P., & Haick, H. (2024) · ACS Sensors · doi:10.1021/acssensors.4c01582 · Cited by 246 · see citing works
“12,13 This enhanced sensitivity is essential for the early detection of diseases, enabling medical professionals to identify issues at the best moment for intervention.”
BackgroundMore than half of data deficient species predicted to be threatened by extinction
Borgelt, J., Dorber, M., Høiberg, M. A., & Verones, F. (2022) · Communications Biology · doi:10.1038/s42003-022-03638-9 · Cited by 236 · see citing works
Miller, T. I., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzińska, A., Sokołowska, S., & Nowy, A. (2025) · Electronics · doi:10.3390/electronics14040696 · Cited by 244 · see citing works
Recent applications of AI to environmental disciplines: A review
Konya, A., & Nematzadeh, P. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.167705 · Cited by 190 · see citing works
Schneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., GRIFFITH, D. W. T., Hase, F., et al. (2019) · Atmospheric measurement techniques · doi:10.5194/amt-12-6771-2019 · Cited by 158 · see citing works
“As widely used, 20 % of the training data is randomly drawn and retained as test data (e.g. Suthaharan, 2016; Hino et al., 2018).”
MethodBublitz, F. M., Oetomo, A., Sahu, K. S., Kuang, A., Fadrique, L. X., Velmovitsky, P. E., da Nobrega, R. M., & Morita, P. P. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16203847 · Cited by 156 · see citing works
“Clean Water Act as current detection methods [7].”
MethodLy, Q. V., Truong, V. H., Ji, B., Nguyen, X. C., Cho, K. H., Ngo, H. H., & Zhang, Z. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154930 · Cited by 121 · see citing works
Nguyen, X. C., Ly, Q. V., Nguyen, T. T. H., Ngo, H. T. T., Hu, Y., & Zhang, Z. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.132203 · Cited by 95 · see citing works
Sustainable strategies to treat urban runoff needed
Lapointe, M., Rochman, C. M., & Tufenkji, N. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00853-4 · Cited by 92 · see citing works
Yin, H., Wen, J., & Chang, C. (2023) · Oeconomia Copernicana · doi:10.24136/oc.2023.032 · Cited by 91 · see citing works
“We also reveal that technological advances in the field of environmental monitoring as an important mechanism linking AI software development and the energy transition, which provides testimony for the advocation of Hino et al .”
BackgroundArtificial intelligence: the silver bullet for sustainable materials development
Hardian, R., Liang, Z., Zhang, X., & Székely, G. (2020) · Green Chemistry · doi:10.1039/d0gc02956d · Cited by 86 · see citing works
Machine Learning-Driven Multidomain Nanomaterial Design: From Bibliometric Analysis to Applications
Wang, H., Cao, H., & Yang, L. (2024) · ACS Applied Nano Materials · doi:10.1021/acsanm.4c04940 · Cited by 85 · see citing works
Jiang, J., Lopez‐Ruiz, J. A., Bian, Y., Sun, D., Yan, Y., Chen, X., Zhu, J., May, H. D., & Ren, Z. J. (2023) · Water Research · doi:10.1016/j.watres.2023.120139 · Cited by 81 · see citing works
Artificial intelligence in sustainable development research
Gohr, C., Aboytes, J. G. R., Belomestnykh, S., Berg-Moelleken, D., Chauhan, N., Engler, J., Heydebreck, L. V., Hintz, M. J., Kretschmer, M.-F., Krügermeier, C., et al. (2025) · Nature Sustainability · doi:10.1038/s41893-025-01598-6 · Cited by 82 · see citing works
“(3) Supervised learning limitations: many AI methods require large, labelled datasets that are sparse and context-dependent in political and social domains 33 .”
Prabhudesai, S., Yang, L., Asthana, S., Huan, X., Liao, Q. V., & Banovic, N. (2023) · Proceedings of the 28th International Conference on Intelligent User Interfaces · doi:10.1145/3581641.3584033 · Cited by 82 · see citing works
“Recently, ML-based DSS [84] have seen deployment in high-stakes decision-making scenarios, such as production [44], policy [54], welfare [32], and healthcare [89].”
BackgroundGao, F., Shen, Y., Sallach, J. B., Li, H., Liu, C., & Li, Y. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c02376 · Cited by 78 · see citing works
Kida, M., POCHWAT, K. B., & Ziembowicz, S. (2023) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2023.132565 · Cited by 67 · see citing works
Deciphering urban traffic impacts on air quality by deep learning and emission inventory
Du, W., Chen, L., Wang, H., Shan, Z., Zhou, Z., Li, W., & Wang, Y. (2022) · Journal of Environmental Sciences · doi:10.1016/j.jes.2021.12.035 · Cited by 65 · see citing works
“Some researchers think that deep learning is suitable for analyzing air pollution ( Hino et al., 2018 ; Xing et al., 2020 ), such as PM 2.5 concentration prediction via interpretable convolutional neural networks ( Park et al., 2020 ; Zhou, Zhang, Du and Liu, 2021 ), assessing traffic impacts by…”
InfluentialDeep Learning Approach for Forecasting Water Quality in IoT Systems
Thai-Nghe, N., Thanh-Hai, N., & Chi, N. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110883 · Cited by 63 · see citing works
“In [12], the authors presented the Public agencies aiming to enforce environmental regulation have limited resources to achieve their objectives.”
Frontistis, Z., Lykogiannis, G., & Sarmpanis, A. (2023) · Environments · doi:10.3390/environments10070127 · Cited by 62 · see citing works
Detection of untreated sewage discharges to watercourses using machine learning
Hammond, P., Suttie, M., Lewis, V. T., Smith, A. P., & Singer, A. C. (2021) · npj Clean Water · doi:10.1038/s41545-021-00108-3 · Cited by 61 · see citing works
Using Machine Learning to make nanomaterials sustainable
Scott-Fordsmand, J. J., & Amorim, M. J. B. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.160303 · Cited by 61 · see citing works
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
Johnson, M. S., Levine, D. I., & Toffel, M. W. (2019) · American Economic Journal Applied Economics · doi:10.1257/app.20200659 · Cited by 55 · see citing works
Applications of artificial intelligence in the field of air pollution: A bibliometric analysis
Guo, Q., Ren, M., Wu, S., Sun, Y., Wang, J., Wang, Q., Ma, Y., Song, X., & Chen, Y. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.933665 · Cited by 54 · see citing works
Kim, E. S. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101378 · Cited by 53 · see citing works
Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
Awais, M., Aslam, B., Maqsoom, A., Khalil, U., Ullah, F., Azam, S., & Imran, M. A. (2021) · Applied Sciences · doi:10.3390/app112110034 · Cited by 53 · see citing works
“In addition, it has been used for engineering risk assessment [56,57], prediction of bioconcentration factors in fish and invertebrates [58], and general monitoring of the environment [49,59].”
BackgroundHu, X. C., Dai, M., Sun, J., & Sunderland, E. M. (2022) · Current Environmental Health Reports · doi:10.1007/s40572-022-00389-x · Cited by 57 · see citing works
Scalable deep learning to identify brick kilns and aid regulatory capacity
Lee, J., Brooks, N. R., Tajwar, F., Burke, M., Ermon, S., Lobell, D. B., Biswas, D., & Luby, S. P. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018863118 · Cited by 46 · see citing works
“First, we matched modelidentified kilns (6,978 total) to government-mapped kilns (7,271) by identifying the nearest neighbor within 2 km.”
MethodSelf-citationUnleashing the power of artificial intelligence for climate action in industrial markets
Akter, S., Babu, M. M., Hani, U., Sultana, S., Bandara, R. J., & Grant, D. (2024) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.12.011 · Cited by 52 · see citing works
Singha, M., Jha, A., Solanki, B. S., Bose, S., & Banerjee, B. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00196 · Cited by 44 · see citing works
“Remote Sensing (RS) images play a vital role in numerous applications, including those mentioned in [15, 44, 60, 67].”
BackgroundArtificial intelligence and climate risk: A double machine learning approach
Yin, H., Yin, X., & Wen, F. (2025) · International Review of Financial Analysis · doi:10.1016/j.irfa.2025.104169 · Cited by 42 · see citing works
A comprehensive systematic literature review of ML in nanotechnology for sustainable development
Rehman, I. U., Ullah, I., Khan, H. U., Guellil, M. S., Koo, J., Min, J., Habib, S., Islam, M., & Lee, M. Y. (2024) · Nanotechnology Reviews · doi:10.1515/ntrev-2024-0069 · Cited by 42 · see citing works
IoT-based edge computing (IoTEC) for improved environmental monitoring
Roostaei, J., Wager, Y. Z., Shi, W., Dittrich, T. M., Miller, C. J., & Gopalakrishnan, K. K. (2023) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2023.100870 · Cited by 40 · see citing works
Safaei, M., & Longo, J. (2023) · Digital Government Research and Practice · doi:10.1145/3604570 · Cited by 44 · see citing works
“Sousa et al. [ 140 ] find increasing use of AI in public ser vice deliver y [ 32 ], economic affairs [ 96 ], and environmental protection [ 49 , 50 ] and sustainability [ 118 , 123 ], with India and the US the most active countries.”
BackgroundZhou, Y., Zhao, J., Chen, R., Lu, P., Zhao, W., Ma, R., Xiao, T., Dong, Y., Zheng, W., Huang, X., et al. (2024) · Nano Today · doi:10.1016/j.nantod.2024.102238 · Cited by 40 · see citing works
Machine Learning and Data Analytics for Environmental Science: A Review, Prospects and Challenges
Tharsanee, R. M., Senthilkumar, S., & Balakrishnan, V. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/955/1/012107 · Cited by 38 · see citing works
Zhang, S. E., Nwaila, G. T., Tolmay, L. C. K., Frimmel, H. E., & Bourdeau, J. E. (2020) · Natural Resources Research · doi:10.1007/s11053-020-09750-z · Cited by 36 · see citing works
AI chatbots contribute to global conservation injustices
Urzedo, D., Sworna, Z. T., Hoskins, A. J., & Robinson, C. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02720-3 · Cited by 35 · see citing works
de Jesus, K. L. M., Senoro, D. B., Cruz, J. C. D., & Chan, E. B. (2022) · Toxics · doi:10.3390/toxics10020095 · Cited by 34 · see citing works
Alvarez-Mora, I., Arturi, K., Béen, F. M., Buchinger, S., Mais, A. E. R. E., Gallampois, C. M. J., Hahn, M., Hollender, J., Houtman, C. J., Johann, S., et al. (2024) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-024-05424-4 · Cited by 32 · see citing works
“This also brings with it a major challenge, which is the high dimensionality of the input features (i.e., number of bits in molecular fingerprints, often several thousand) [133–135] that can slow down the data evaluation.”
Refining public policies with machine learning: The case of tax auditing
Battaglini, M., Guiso, L., Lacava, C., Miller, D. L., & Patacchini, E. (2024) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105847 · Cited by 32 · see citing works
Bridging adaptive management and reinforcement learning for more robust decisions
Chapman, M. S., Xu, L., Lapeyrolerie, M., & Boettiger, C. (2023) · Philosophical Transactions of the Royal Society of London. Biological Sciences · doi:10.1098/rstb.2022.0195 · Cited by 31 · see citing works
“For example, the exponentially increasing amount of monitoring data from automated sensors [63] quickly becomes impractical for humans to integrate into a heuristic decision process.”
BackgroundDewika, M., Markandan, K., Ruwaida, J. N., Sara, Y. Y., Deb, A., Irfan, N. A., & Khalid, M. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176063 · Cited by 37 · see citing works
“Furthermore, the application of machine learning techniques enables the identification of environmental patterns and potential hazards, significantly enhancing the efficacy of environmental monitoring (Hino et al., 2018).”
Huang, Z. T., Jiang, Y. P., He, W., Yu, J., Deng, S., Jiang, Y., Yang, C., Zhu, W., Wu, Y., & Liu, X. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.108113 · Cited by 30 · see citing works
Selim, A., Shuvo, S. N. A., Islam, M. M., Moniruzzaman, M., Shah, S., & Ohiduzzaman, M. (2023) · Total Environment Research Themes · doi:10.1016/j.totert.2023.100066 · Cited by 28 · see citing works
Liu, W., Liu, C., Luo, J., & Liu, F. (2024) · Managerial and Decision Economics · doi:10.1002/mde.4155 · Cited by 29 · see citing works
Using a deep learning model to quantify trash accumulation for cleaner urban stormwater
Conley, G., Zinn, S. C., Hanson, T., McDonald, K., Beck, N. G., & Wen, H. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101752 · Cited by 26 · see citing works
Data Science and Plant Metabolomics
Kisiel, A., Łobodzińska, A., Cembrowska-Lech, D., & Miller, T. I. (2023) · Metabolites · doi:10.3390/metabo13030454 · Cited by 26 · see citing works
“This knowledge can inform the development of sustainable agriculture and land use practices, helping to mitigate the negative impact of plant metabolites on the environment [150].”
BackgroundDecNet: Decomposition network for 3D gravity inversion
Zhang, S., Yin, C., Cao, X., Sun, S., Liu, Y., & Ren, X. (2022) · Geophysics · doi:10.1190/geo2021-0744.1 · Cited by 25 · see citing works
Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow
Dhanwani, R., Prajapati, A., Dimri, A., Varmora, A., & Shah, M. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-14481-6 · Cited by 24 · see citing works
Yu, S. I., Jeon, J., Shin, Y.-U., & Bae, H. (2024) · ACS ES&T Engineering · doi:10.1021/acsestengg.4c00142 · Cited by 22 · see citing works
Man, J., Zeng, L., Luo, J., Gao, W., & Yao, Y. (2021) · ACS ES&T Engineering · doi:10.1021/acsestengg.1c00224 · Cited by 23 · see citing works
Sangani, M. F., Owens, G., Nazari, B., Astaraei, A. R., Fotovat, A., & Emami, H. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.345 · Cited by 23 · see citing works
Recasting China’s urban wastewater management standards
Li, W., & Yu, H. (2024) · Nature Sustainability · doi:10.1038/s41893-024-01487-4 · Cited by 22 · see citing works
Givisis, I., Kalatzis, D., Christakis, C. E., & Kiouvrekis, Y. (2025) · Electronics · doi:10.3390/electronics14234766 · Cited by 21 · see citing works
“In the era of rapidly advancing machine learning, numerous studies have been conducted within IoT frameworks to enhance prediction [37], automation, and intelligent environmental monitoring [27,38–43].”
Sliding Window-Based Machine Learning for Environmental Inspection Resource Allocation
Zhou, Q., Qu, S., Wang, Q., She, Y., Yu, Y., & Bi, J. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c05088 · Cited by 22 · see citing works
Artificial intelligence and machine learning in optics: tutorial
Yadav, K., Bidnyk, S., & Balakrishnan, A. (2024) · Journal of the Optical Society of America B · doi:10.1364/josab.525182 · Cited by 22 · see citing works
Lu, D., Mao, W., Zheng, L., Xiao, W., Zhang, L., & Wei, J. (2021) · Remote Sensing · doi:10.3390/rs13081423 · Cited by 21 · see citing works
“, multiple linear regression, failed to detect non-linear relations [22,34,35], given that the formation, diffusion, migration, and transformation of PM2.”
BackgroundSingh, S., Singh, M., & Attri, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127243 · Cited by 20 · see citing works
Jamei, M., Ali, M., Bateni, S. M., Jun, C., Karbasi, M., Malik, A., Jamei, M., & Yaseen, Z. M. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108609 · Cited by 21 · see citing works
Savitha, C., & Talari, R. (2025) · Environmental Monitoring and Assessment · doi:10.1007/s10661-025-13880-3 · Cited by 19 · see citing works
“…have found their place in various sectors, such as health care (Javaid et al., 2022), agriculture (Meshram et al., 2021), environmental monitoring (Hino et al., 2018; Mandvi et al., 2024), and energy and power generation (Mala-kouti, 2023a, 2023b, 2023c, 2023d; Malakouti et al., 2023, 2024; Seyed…”
Parra, R., Chicaiza-Ortiz, C., Herrera-Feijoo, R. J., Arellano-Yasaca, D. V., Lee, L.-C., Tulcan, R. X. S., & Martí-Herrero, J. (2025) · Sci · doi:10.3390/sci7020052 · Cited by 19 · see citing works
Machine Learning Analysis for Phenolic Compound Monitoring Using a Mobile Phone-Based ECL Sensor
Taylor, J. W., Rivera, E. C., Kim, S., Campbell, R., Summerscales, R., & Kwon, H. J. (2021) · Sensors · doi:10.3390/s21186004 · Cited by 18 · see citing works
Xie, L., Shen, Y., Zhang, M., Zhong, Y., Lu, Y., Yang, L., & Li, Z. (2022) · Optics Express · doi:10.1364/oe.472726 · Cited by 18 · see citing works
Jonathan, H., Magd, H. A. E., & Khan, S. A. (2024) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-272-720241005 · Cited by 18 · see citing works
Rapid detection of perfluorooctanoic acid by surface enhanced Raman spectroscopy and deep learning
Huang, C., Zhang, Y., Zhang, Q., He, D., Dong, S., & Xiao, X. (2024) · Talanta · doi:10.1016/j.talanta.2024.126693 · Cited by 18 · see citing works
Deep Learning-Based quantifications of methane emissions with field applications
Jahan, I., Mehana, M. Z., Matheou, G., & Viswanathan, H. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104018 · Cited by 17 · see citing works
Karasaki, S., Morello-Frosch, R. A., & Callaway, D. S. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.175730 · Cited by 17 · see citing works
Simarmata, N., Wikantika, K., Tarigan, T. A., Aldyansyah, M., Tohir, R. K., Fauzi, A. I., & Fauzia, A. R. (2025) · The Egyptian Journal of Remote Sensing and Space Science · doi:10.1016/j.ejrs.2025.02.002 · Cited by 16 · see citing works
A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science
An, H., Li, X., Huang, Y., Wang, W., Wu, Y., Liu, L., Ling, W., Li, W., Zhao, H., Lü, D., et al. (2024) · Eco-Environment & Health · doi:10.1016/j.eehl.2024.01.006 · Cited by 16 · see citing works
“For instance, ML algorithms have been widely used for improving the ef fi ciency of environmental monitoring and policy-making [27], accounting carbon budget [33,34], decoupling the meteorological impact on air pollution [9,35], screening the new pollutants from a tremendous number of chemicals…”
Li, Q., Cui, X. Y., Gao, X., Chen, X., & Zhao, H. (2024) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2024.105662 · Cited by 16 · see citing works
Exploring limits to tree planting as a natural climate solution
Rana, P., & Varshney, L. R. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.135566 · Cited by 16 · see citing works
Isolation Forest for Environmental Monitoring: A Data-Driven Approach to Land Management
Binetti, M. S., Uricchio, V. F., & Massarelli, C. (2025) · Environments · doi:10.3390/environments12040116 · Cited by 15 · see citing works
Chugg, B., Anderson, B., Eicher, S., Lee, S., & Ho, D. E. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102463 · Cited by 15 · see citing works
“This has lead to increasing interest in leveraging machine learning to augment regulatory capacity (Glicksman et al., 2017; Hino et al., 2018).”
BackgroundMarques, M. D. C., Mohamed, A. A., & Feitosa, P. H. A. (2024) · Cleaner Production Letters · doi:10.1016/j.clpl.2024.100088 · Cited by 16 · see citing works
Wang, F., Pang, Y., Bai, L., & Godin, M. (2025) · ENVIRONMENTAL SYSTEMS RESEARCH · doi:10.1186/s40068-025-00403-9 · Cited by 14 · see citing works
“Hino et al. (2018) demonstrated that machine learning can predict the likelihood of failing inspections based on the historical data of the facility and provide the optimal allocation of inspection resources.”
Hyrynsalmi, S. M., Baltes, S., Brown, C., Prikladnicki, R., Rodríguez-Pérez, G., Serebrenik, A., Simmonds, J., Trinkenreich, B., Wang, Y., & Liebel, G. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3711904 · Cited by 17 · see citing works
Bradbury, A. W., & Robertson, C. (1993) · PubMed · Cited by 14 · see citing works
Fonseca, G., & Vieira, D. C. (2023) · Ocean and Coastal Research · doi:10.1590/2675-2824071.22044gf · Cited by 13 · see citing works
Yan, H., Zheng, Q., & Zeng, L. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130900 · Cited by 13 · see citing works
On the EPA's Radar: The Role of Financial Reports in Environmental Regulatory Oversight
Li, B., & Wang, A. Y. (2024) · Journal of Accounting Research · doi:10.1111/1475-679x.12572 · Cited by 13 · see citing works
Predicting MHD mixed convection in a semicircular cavity with hybrid nanofluids using AI
Das, P., & Mamun, M. A. H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38303 · Cited by 13 · see citing works
Coglianese, C., & Dor, L. B. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3501067 · Cited by 13 · see citing works
Advances and future directions of environmental risk research: A bibliometric review
Geng, J., Fang, W., Liu, M., Yang, J., Ma, Z., & Bi, J. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176246 · Cited by 14 · see citing works
Du, J., Ma, W., Li, G., Wu, J., & Chang, W. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123668 · Cited by 12 · see citing works
Towards Utilitarian Online Learning -- A Review of Online Algorithms in Open Feature Space
He, Y., Schreckenberger, C., Stuckenschmidt, H., & Wu, X. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/745 · Cited by 12 · see citing works
Machine learning for data verification in emissions trading system
Yu, R., Zhang, D., Zhang, X., & Huang, X. (2023) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2023.107239 · Cited by 11 · see citing works
Flood detection using deep learning methods from visual images
Hussain, A., Latif, G., Alghazo, J. M., & Kim, E. (2024) · AIP conference proceedings · doi:10.1063/5.0194669 · Cited by 11 · see citing works
Banning Wildlife Trade Can Boost the Unregulated Trade of Threatened Species
Kubo, T., Mieno, T., Uryu, S., Terada, S., & Veríssimo, D. (2025) · Conservation Letters · doi:10.1111/conl.13077 · Cited by 11 · see citing works
“For example, computer science can be used to identify traded species (Di Minin et al. 2018; Hino, Benami, and Brooks 2018).”
Ding, Z.-G., Liu, S., Lv, X., Duan, C., Liu, F., Liu, R., & Chen, X. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2025.107411 · Cited by 11 · see citing works
Public environmental complaints and regulatory intensity
Zhang, J., Elliott, R. J., Zhang, B., & Liu, M. (2025) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2025.103221 · Cited by 10 · see citing works
Nadeem, W., Ashraf, A. R., Khan, H., & Kumar, V. (2025) · Technovation · doi:10.1016/j.technovation.2025.103390 · Cited by 11 · see citing works
Li, Y., Wang, X., Mou, X., Jia, B., Lian, J., Niu, Y., Gong, X., & Li, Y. (2025) · Geoderma · doi:10.1016/j.geoderma.2025.117311 · Cited by 10 · see citing works
The Distributive Effects of Risk Prediction in Environmental Compliance
Benami, E., Whitaker, R., La, V., Lin, H., Anderson, B., & Ho, D. E. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445873 · Cited by 9 · see citing works
“Serious noncompliance exists across environmental programs [19], and machine learning oers the promise to help predict sources of noncompliance and thereby target environmental compliance eorts [20, 22].”
BackgroundSelf-citationIdentifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment
Weir, M. J., & Sproul, T. W. (2019) · Sustainability · doi:10.3390/su11143934 · Cited by 9 · see citing works
“Recent applications of machine learning in policy prediction problems include environmental monitoring [13], judicial behavior [14], changes in household diet [15,16], poverty quantification [17–19], tax policy evaluation [20], restaurant hygiene inspections [21], and highway procurement auctions [22].”
BackgroundMachine Learning in Dam Water Research: An Overview of Applications and Approaches
Yahya, F. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/56922020 · Cited by 9 · see citing works
“Therefore, applying machines learning approach enable dam operators to improve efficiency in decision making even with limited data [23].”
Background
- Authors
- Hino, M., Benami, E., & Brooks, N.
- Affiliations
- Stanford University (US)
- Corresponding author
- Benami, E.
- Venue
- Nature Sustainability
- Published
- 2018-10-01
- Volume
- 1
- Issue
- 10
- Pages
- 583-588
- ISSN
- 2398-9629 (ISSN-L)
- 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
- Locations
- Nature Sustainability (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Regulation and Compliance Studies
- Social Sciences › Political Science and International Relations › 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
