SHAP分析による説明可能なブーストアルゴリズムを使用して,化学酸素需要の正確で解釈可能な予測
Khaled Merabet1, Sungwon Kim2, Salim Heddam1
1Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955 Skikda, Route El Hadaik, BP 26, Skikda, Algeria.
Scientific reports
|February 13, 2026
まとめ
化学酸素需要 (COD) の予測は,水の質にとって極めて重要です. NGBoostとCatBoostのモデルは,水質パラメータを使用してCODの推定において高い精度を示し,NGBoostはモデルの不確実性を定量化しました.
科学分野:
- 環境科学 環境科学
- データサイエンス データサイエンス
- 水資源の管理 水資源の管理
背景:
- 効果的な水質管理は,正確な化学酸素需要 (COD) の予測に依存しています.
- CODは水体の有機汚染の重要な指標である.
研究 の 目的:
- COD推定のための6つのアンサンブルブースティングモデルのパフォーマンスを比較する.
- COD予測のための最も影響力のある水質パラメータを特定する.
- モデルの解釈可能性と不確実性を評価する.
主な方法:
- 6つのアンサンブルブースティングモデル (AdaBoost,CatBoost,XGBoost,LightGBM,HistGBRT,NGBoost) が採用されました.
- モデルは,韓国の2つのモニタリングステーションからの水質データを使用して訓練され,検証されました.
- 性能は,RMSE,MAE,R,NSE,PBIAS,SHAPを用いて評価され,解釈可能性が確認されました.
主要な成果:
- NGBoostは,Toilchun駅 (R=0.979,NSE=0.958) で最も高い精度を示しました.
- CatBoostは,Hwangji駅 (R=0.861,NSE=0.733) で最高のパフォーマンスを発揮しました.
- SHAPの分析は,CODの主要な予測要因として,総有機炭素 (TOC),生化学酸素需要 (BOD5) と懸浮固体 (SS) を明らかにした.
結論:
- アンサンブルブースティングモデル,特にNGBoostとCatBoostは,CODの予測に有効です.
- 確率分布を提供するNGBoostの能力は,CODの変動性とモデル不確実性の定量化を強化します.
- TOC,BOD5,SSなどの特定のパラメータの影響を理解することは,水汚染の制御に不可欠です.
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