亜熱帯モンスーン流域における日々の流量予測のための機械学習モデルの比較評価
Zhi Zhang1, Yusha Xiao2, Runting Chen3
1Tourism and Historical Culture College, Zhaoqing University, Zhaoqing, 526061, Guangdong, China. zhangzhi@zqu.edu.cn.
Scientific reports
|February 5, 2026
まとめ
Long Short-Term Memory (LSTM) モデルは、モンスーン流域における流量予測において、特に極端な高流量イベントや洪水ピーク時に他の機械学習手法よりも優れたパフォーマンスを発揮します。
背景:
- 正確な流量予測は、亜熱帯モンスーン地域における水資源管理と洪水警報の発令に不可欠です。
- 流量予測に最適な機械学習モデルの選択は、依然として大きな課題です。
主な方法:
- 7つの機械学習モデル(線形回帰(LR)、勾配ブースティング回帰、人工ニューラルネットワーク(ANN)、ランダムフォレスト、エクストラツリー回帰、XGBoost(XGB)、およびLong Short-Term Memory(LSTM))の比較分析。
- Nash-Sutcliffe効率(NSE)およびKling-Gupta効率(KGE)などの指標を用いた評価。
- モデルの挙動と水文学的影響を理解するための特徴量重要度と残差パターンの分析。
結論:
- LSTMで実装されているような時間的記憶メカニズムは、特に極端な水文学的条件下での流量予測において大きな利点をもたらします。
- この調査結果は、運用上の洪水予測システムで適切なモデルを選択するための貴重なガイダンスを提供します。
- 正確な流量予測のためには、流域の記憶と水文学的条件を理解することが重要です。
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