气候变化领域集成服务门户(CCPortal): Machine learning for environmental monitoring

CCPortal

DOI	10.1038/s41893-018-0142-9
	Machine learning for environmental monitoring
	Hino M.; Benami E.; Brooks N.
发表日期	2018
ISSN	2398-9629
起始页码	583
结束页码	588
卷号	1 期号:10
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. © 2018, The Author(s), under exclusive licence to Springer Nature Limited.
语种	英语
scopus关键词	Budget control; Inspection; Machine learning; Predictive analytics; Water pollution; Current practices; Electronic data; Environmental Monitoring; Error propagation; Inspection allocation; Institutional constraints; Level difference; Machine learning methods; Environmental regulations
来源期刊	Nature Sustainability
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/163246
作者单位	Stanford University, Stanford, CA, United States
推荐引用方式 GB/T 7714	Hino M.,Benami E.,Brooks N.. Machine learning for environmental monitoring[J],2018,1(10).
APA	Hino M.,Benami E.,&Brooks N..(2018).Machine learning for environmental monitoring.Nature Sustainability,1(10).
MLA	Hino M.,et al."Machine learning for environmental monitoring".Nature Sustainability 1.10(2018).