相关实验视频
Updated: Jul 5, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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机器学习预测清洁水法规定的河流,溪流和湿地
Simon Greenhill1,2, Hannah Druckenmiller3,4, Sherrie Wang2,5,6
1Department of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.
概括
清洁水法保护的水域比2006年最高法院的裁决少, 机器学习有助于评估水资源保护的监管变化.
科学领域:
- 环境法
- 遥感技术
- 机器学习
背景情况:
- 清洁水法案 (CWA) 规范了美国的水资源.
- 美国最高法院的裁决和白宫的规定改变了CWA的保护范围.
- 评估这些监管变化对水资源管辖权的影响至关重要.
研究的目的:
- 根据不同的监管制度,确定"清洁水法"所保护的水资源范围.
- 量化2006年最高法院裁决和2020年白宫裁决对CWA管辖权的影响.
- 通过机器学习开发评估监管变化的框架.
主要方法:
- 一个深度学习模型使用空中图像和地质数据进行训练.
- 该模型预测了陆军工程师部队的15万个管辖区.
- 该研究比较了2006年最高法院裁决和2020年白宫规则下的CWA保护.
主要成果:
- 2006年最高法院的裁决保护了美国三分之二的河流和一半以上的湿地.
- 2020年白宫规则将保护范围减少到不到一半的河流和四分之一的湿地.
- 这意味着69万英里的河流,35万英的湿地和30%的饮用水源将被放松管制.
结论:
- 监管变化对"清洁水法"保护的范围产生重大影响.
- 机器学习为分析和实施环境法规提供了强大的工具.
- 开发的框架可以帮助制定水资源政策和监管许可.
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