Training data from: Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and over half of wetlands; under a 2020 White House rule, it protects under half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems. 

Methods
This dataset contains data used to train the models.

本研究旨在评估《清洁水法》（Clean Water Act）所保护的水域范围，以及美国最高法院与白宫出台的相关规则如何调整该监管规则体系。本研究利用航空影像与地球物理数据训练深度学习模型，以预测美国陆军工程兵团（Army Corps of Engineers）出具的15万份管辖权限认定文书，每份文书均针对一处水资源作出监管判定。根据2006年美国最高法院的裁决，《清洁水法》可覆盖美国三分之二的溪流与超过半数的湿地的保护范围；而依据2020年白宫出台的监管规则，其保护的溪流不足半数、湿地仅占四分之一，这意味着约69万英里长的溪流、3500万英亩湿地以及饮用水源周边30%的水域将被解除监管。本研究框架可为许可审批、政策制定以及机器学习在监管执行问题中的应用提供支撑。
研究方法
本数据集包含用于模型训练的相关数据。