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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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サポートベクトルマシンを使用して,リアルタイムでの洪水深さの予測のための急速なシミュレーション

Beom-Jin Kim1, Minkyu Kim1, Jaehwan Yoo2

  • 1Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea.

Scientific reports
|August 29, 2025
PubMed
まとめ
この要約は機械生成です。

この研究では,洪水に弱い都市部で,サポートベクトルマシン (SVM) を使用した急速な洪水深さの予測モデルを開発しました. 物理的なシミュレーションデータで訓練されたSVMモデルは,迅速かつ信頼できる災害対応の予測を提供します.

キーワード:
洪水の深さLIP (ローカル・インテンシブ・プレシピテーション)急速シミュレーションリアルタイムSVM (サポートベクトルマシン)

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科学分野:

  • 環境科学
  • 水学
  • 都市計画

背景:

  • 気候変動は地方の激しい降水 (LIP) を増幅し,深刻な都市洪水につながります.
  • 伝統的な水力学モデル (SWMM,FLO-2D) は正確ですが,計算が密集しており,リアルタイムでの洪水予測を制限しています.
  • カンナムやソウルのような都市部では 洪水の危険性があります

研究 の 目的:

  • 都市部における急速な洪水深度予測モデルを開発する.
  • 機械学習と物理シミュレーションを統合して 洪水予報を強化します
  • 洪水が起こりやすい都市部での 災害時対応を支援する.

主な方法:

  • 急速な洪水深さの予測のためにサポートベクターマシン (SVM) モデルが開発されました.
  • SVMモデルは,1D-2D結合水力シミュレーション (SWMM-FLO-2D) から生成されたデータを使用して訓練されました.
  • 入力変数には,1〜5時間のシナリオにおける累積的な降雨量と排水口溢出量が含まれていた.

主要な成果:

  • 統合されたSVMモデルはR2=0.988,NSE=0.987,%差=1.080,RMSE=0.098mで高い性能を示した.
  • 1D-2D水力学モデル (SWMM-FLO-2D) は,観測された洪水記録に対して64%の一致で検証された.
  • SVMモデルは,FLO-2Dシミュレーション結果と比較して,洪水深さを正確に予測しました.

結論:

  • 機械学習と物理シミュレーションを統合することで 洪水予測に迅速かつ信頼性の高いアプローチができます
  • 開発されたSVMモデルは,リアルタイムでの都市洪水リスク管理に大きく役立ちます.
  • このアプローチは,気候変動の影響に直面している都市部における災害対応システムの効率を高めます.