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Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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...
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機能選択の最適化と解釈可能な機械学習に基づく都市水浸し感受性の評価

Chenxi Li1, Xiaofan Yang2, Xiaoyong Ni3

  • 1School of National Safety and Emergency Management, Beijing Normal University, Zhuhai, 519087, China.

Journal of environmental management
|August 29, 2025
PubMed
まとめ

この研究は,特性の選択とモデルの訓練を改善し,洪水感受性のマッピングのための新しい枠組みを導入します. 標高とGDPのような重要な要因は,中国深の浸水リスクに大きな影響を及ぼします.

キーワード:
チ2テスト機械学習相互情報提供脆弱な施設浸水感度

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科学分野:

  • 環境科学
  • 地理空間分析
  • 機械学習アプリケーション

背景:

  • 洪水感受性研究における機械学習 (ML) は,しばしば解釈性と一般化が不十分である.
  • 特徴の選択とモデルのトレーニングの間の断絶は,既存のMLモデルの有効性を妨げています.

研究 の 目的:

  • 特徴の選択と洪水感受性の評価におけるモデルトレーニングの同時最適化のためのC2MI-MLフレームワークを開発し,検証する.
  • 中国 深の水浸し感受性に影響を与える主要な自然および社会経済的要因を特定する.

主な方法:

  • Chi-squareテスト,相互情報,C2MI-MLフレームワーク内のMLアルゴリズムの統合
  • 訓練と検証のための多様なデータセットを利用した,中国の深のケーススタディアプリケーションです.
  • 曲線下の面積 (AUC) とカッパ係数を用いた性能評価

主要な成果:

  • C2MI-LightGBMモデルは0.965の高いAUCを達成し,すべてのアンサンブルモデルは0.95のAUCを超えました.
  • 標高と国内総生産 (GDP) は,水浸水に影響を与える最も重要な要因として特定されました.
  • 空間分析により,シェンジェンの西部で高い感受性が明らかになり,特定の地区や学校などの脆弱な場所が特定されました.

結論:

  • C2MI-MLフレームワークは,洪水感受性モデリングの改善された解釈性と一般化を提供します.
  • 都市部に浸水する際に 自然要因と社会経済要因の両方が 重要な役割を担っていることが 明らかになりました
  • この研究は,深の標的型洪水リスク管理と早期警告システムについて,実用的な洞察を提供します.