Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1038/s41893-018-0142-9 |
Machine learning for environmental monitoring | |
Hino, M.; Benami, E.; Brooks, N. | |
2018-10-01 | |
发表期刊 | NATURE SUSTAINABILITY |
ISSN | 2398-9629 |
出版年 | 2018 |
卷号 | 1期号:10页码:583-588 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | 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 for state-level differences in inspection budgets, our reallocation regimes double the number of violations detected through inspections. Leveraging increasing amounts of electronic data can help public agencies to enhance their regulatory effectiveness and remedy environmental harms. Although employing algorithm-based resource allocation rules requires care to avoid manipulation and unintentional error propagation, the principled use of predictive analytics can extend the beneficial reach of limited resources. |
领域 | 资源环境 |
收录类别 | SSCI |
WOS记录号 | WOS:000447322300013 |
WOS关键词 | POLICY PROBLEMS ; ENFORCEMENT |
WOS类目 | Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies |
WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/289686 |
专题 | 资源环境科学 |
作者单位 | Stanford Univ, Stanford, CA 94305 USA |
推荐引用方式 GB/T 7714 | Hino, M.,Benami, E.,Brooks, N.. Machine learning for environmental monitoring[J]. NATURE SUSTAINABILITY,2018,1(10):583-588. |
APA | Hino, M.,Benami, E.,&Brooks, N..(2018).Machine learning for environmental monitoring.NATURE SUSTAINABILITY,1(10),583-588. |
MLA | Hino, M.,et al."Machine learning for environmental monitoring".NATURE SUSTAINABILITY 1.10(2018):583-588. |
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