季節的な管理涵養からの地下水貯留量の予測:機械学習と説明可能なAI技術からの洞察
Valdrich J Fernandes1, Perry G B de Louw1,2, Coen J Ritsema1
1Soil Physics and Land Management Group, Wageningen University & Research, Wageningen, The Netherlands.
まとめ
機械学習モデルは、地下水貯留ダイナミクスを単純化することにより、過渡的な管理涵養(MAR)効果を正確に予測します。このアプローチは、干ばつに敏感な地域でさえ、持続可能な水管理のための計画と最適化を強化します。
背景:
- 管理涵養(MAR)は、地下水貯留と持続可能な水利用にとって重要です。
- 機械学習(ML)モデルは、MARサイトの選択と計画のための従来の数値モデルの効率的な代替手段を提供します。
- 乾燥期間中の水の保持を理解するために不可欠な、過渡的なMAR応答の正確なシミュレーションは、現在のMLモデルにとって依然として課題です。
主な方法:
- 地下水貯留時系列をMAR応答および減衰係数成分に分解すること。
- これらの成分を予測するためにU-NetおよびXGBoost MLモデルを適用すること。
- モデル予測を解釈し、影響力のある要因を特定するためにSHAP値(説明可能なAI)を利用すること。
結論:
- MLサロゲートモデル、特にU-NetおよびXGBoostは、過渡的なMARダイナミクスのシミュレーションに大きな可能性を示しています。
- 説明可能なAI技術は、MAR計画におけるMLモデルの解釈可能性と実用的な応用を強化します。
- これらの計算効率の高いモデルは、水資源管理の改善のための大規模なシナリオテストと最適化を容易にします。
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