ABSTRACT Public agencies aiming to enforce environmental regulation have limited resources to achieve their objectives. We demonstrate how machine-learning methods can inform the efficient

use of these limited resources while accounting for real-world concerns, such as gaming the system and institutional constraints. Here, we predict the likelihood of a facility failing a

water-pollution inspection and propose alternative inspection allocations that would target high-risk facilities. Implementing such a data-driven inspection allocation could detect over

seven times the expected number of violations than current practices. When we impose constraints, such as maintaining a minimum probability of inspection for all facilities and accounting

manipulation and unintentional error propagation, the principled use of predictive analytics can extend the beneficial reach of limited resources. Access through your institution Buy or

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UNTREATED SEWAGE DISCHARGES TO WATERCOURSES USING MACHINE LEARNING Article Open access 11 March 2021 DETERMINANTS OF EFFICIENT WATER USE AND CONSERVATION IN THE COLOMBIAN MANUFACTURING

INDUSTRY USING MACHINE LEARNING Article Open access 02 January 2024 ADDRESSING GAPS IN DATA ON DRINKING WATER QUALITY THROUGH DATA INTEGRATION AND MACHINE LEARNING: EVIDENCE FROM ETHIOPIA

Article Open access 08 September 2023 DATA AVAILABILITY The raw data used in this analysis can be downloaded from the EPA’s ECHO website (https://echo.epa.gov/). The processed datasets are

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https://echo.epa.gov/trends/comparative-maps-dashboards/state-compliance-monitoring-expectations Download references ACKNOWLEDGEMENTS We thank S. Athey, M. Burke, F. Burlig, K. Mach, A.

D’Agostino, C. Anderson, K. Green, S. Hasan, D. Jiménez, H. Kim, A. R. Siders and A. Stock for comments. E.B. receives funding from the National Science Foundation Graduate Research

Fellowship Program (DGE-114747), M.H. from the Department of Earth System Science at Stanford University, and N.B. from the Stanford Graduate Fellowship/David and Lucile Packard Foundation.

AUTHOR INFORMATION Author notes AUTHORS AND AFFILIATIONS * Stanford University, Stanford, CA, USA M. Hino, E. Benami & N. Brooks Authors * M. Hino View author publications You can also

search for this author inPubMed Google Scholar * E. Benami View author publications You can also search for this author inPubMed Google Scholar * N. Brooks View author publications You can

also search for this author inPubMed Google Scholar CONTRIBUTIONS All three authors collaboratively designed the study, developed the methodology, assembled the data, wrote the code,

performed the analysis, interpreted the results, and wrote the manuscript. E.B. and M.H. conducted the final analysis, with substantial input from N.B. CORRESPONDING AUTHOR Correspondence to

E. Benami. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE: Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional affiliations. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Note 1, Supplementary Figures 1–6, Supplementary

Tables 1–6, Supplementary References 1–4 RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Hino, M., Benami, E. & Brooks, N. Machine learning for

environmental monitoring. _Nat Sustain_ 1, 583–588 (2018). https://doi.org/10.1038/s41893-018-0142-9 Download citation * Received: 02 March 2018 * Accepted: 23 August 2018 * Published: 01

October 2018 * Issue Date: October 2018 * DOI: https://doi.org/10.1038/s41893-018-0142-9 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get

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