関連する実験動画
Updated: Jul 5, 2025
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
8.0K
機械学習は,クリーンウォーター法が規制する河川,小川,湿地を予測します
Simon Greenhill1,2, Hannah Druckenmiller3,4, Sherrie Wang2,5,6
1Department of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.
Science (New York, N.Y.)
|January 25, 2024
まとめ
清浄水法は2006年の最高裁判所の決定より 2020年のルールで少ない水域を保護し,何百万人もの川のマイルと湿地のエーカーに影響を与えています. 機械学習は水資源保護の 規制の変更を評価するのに役立ちます
科学分野:
- 環境法
- リモートセンシング
- 機械学習
背景:
- クリーンウォーター法 (CWA) は,米国の水資源を規制しています.
- 最高裁判所の判決とホワイトハウスの規制は CWA の保護の範囲を変えました
- これらの規制変更が水資源の管轄権に与える影響を評価することは極めて重要です.
研究 の 目的:
- 異なる規制制度の下でクリーンウォーター法で保護される水資源の範囲を決定する.
- 2006年の最高裁判所の判決と2020年のホワイトハウスの裁定がCWAの管轄権に与える影響を定量化します.
- 機械学習を用いた規制変更の評価のための枠組みを開発する.
主な方法:
- ディープラーニングモデルが 空からの画像と地球物理学的データを使って 訓練されました
- このモデルは15万件の 陸軍工兵隊による 管轄決定を予測しました
- この研究は,2006年の最高裁判所の判決と2020年のホワイトハウスの規則に基づくCWAの保護を比較した.
主要な成果:
- 2006年の最高裁判所の判決で アメリカの川の3分の2と 湿地の半分以上が保護されました
- 2020年のホワイトハウスの規則では 保護は川の半分未満と湿地の4分の1に削減されました
- これは69万マイルもの川, 3千5百万エーカーの湿地, そして飲料水の30%の規制緩和を意味しています.
結論:
- 規制の変更はClean Water Actの保護範囲に大きな影響を与えます.
- 機械学習は環境規制を分析し 実施するための強力なツールです
- 開発された枠組みは,水資源の政策設計と規制許可に役立ちます.
関連する概念動画
01:30Typical Model Studies
359
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
359
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
47
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
47
01:12Modeling and Similitude
267
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
267
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
177
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
177
01:27Laminar Flow
1.1K
Laminar flow represents a smooth, orderly fluid motion where particles move along parallel paths, resulting in minimal mixing between layers. Streamlined particle paths characterize this flow regime and occur under conditions where viscous forces dominate over inertial forces. The distinction between laminar, transitional, and turbulent flow is primarily determined by the Reynolds number, a dimensionless quantity calculated as:
1.1K
01:24Rapidly Varying Flow
62
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
62