预测季节性管理地下水层补充的地下水储量:来自机器学习和可解释的人工智能技术的见解
Valdrich J Fernandes1, Perry G B de Louw1,2, Coen J Ritsema1
1Soil Physics and Land Management Group, Wageningen University & Research, Wageningen, The Netherlands.
Environmental earth sciences
|February 23, 2026
概括
机器学习模型通过简化地下水储存动态,准确地预测短暂的管理下水层补充 (MAR) 效应. 这种方法提高了可持续水资源管理的规划和优化,即使是在干旱敏感的地区.
科学领域:
- 水文学的水文学
- 水文地质学 水文地质学
- 水资源管理 水资源管理
背景情况:
- 管理下水层补充 (MAR) 对地下水储存和可持续用水至关重要.
- 机器学习 (ML) 模型为 MAR 站点选择和规划提供了传统数值模型的高效替代方案.
- 准确模拟短暂的MAR反应,对于了解干旱期间的水保持至关重要,仍然是当前ML模型面临的挑战.
研究的目的:
- 开发和验证ML模型,用于预测短暂的管理下水层补充 (MAR) 效应.
- 为了简化使用指数衰减的充电后地下水储存动态的表示.
- 利用可解释AI (XAI) 来识别影响MAR有效性的关键因素.
主要方法:
- 地下水储存时间序列的分解为MAR响应和衰变系数组件.
- 应用U-Net和XGBoost ML模型来预测这些组件.
- 使用SHAP值 (可解释AI) 来解释模型预测和识别影响因素.
主要成果:
- 在预测过渡MAR组件方面,U-Net和XGBoost模型实现了高精度 (R2>0.82).
- 简化的指数衰变模型有效地捕捉了长期存储动态.
- SHAP分析确定了影响MAR性能的关键场地管理和地表水特性.
结论:
- 机器学习的替代模型,特别是U-Net和XGBoost,显示出模拟过渡式MAR动态的显著前景.
- 可解释的AI技术提高了ML模型在MAR规划中的解释性和实际应用.
- 这些计算高效的模型有助于大规模的场景测试和优化,以改善水资源管理.
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