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可解释的ML建模盐水入侵控制与地下障碍在沿海斜坡含水层
Asaad M Armanuos1, Martina Zeleňáková2, Mohamed Kamel Elshaarawy3
1Irrigation and Hydraulics Engineering Department, Faculty of Engineering, Tanta University, Tanta, 31733, Egypt. asaad.matter@f-eng.tanta.edu.eg.
Scientific reports
|August 10, 2025
概括
精确的盐水入侵 (SWI) 建模对于沿海水管理至关重要. 贝叶斯优化的光梯度增强 (LGB) 模型有效预测SWI长,SHAP分析揭示了实际应用的关键影响因素.
科学领域:
- 水文地质学 水文地质学
- 机器学习在环境科学中的应用
- 沿海地下水管理
背景情况:
- 盐水入侵 (SWI) 威胁到沿海淡水含水层和水质,需要可靠的预测模型.
- 了解带有地下障碍的斜坡含水层中的SWI动态对于可持续的资源管理至关重要.
研究的目的:
- 评估贝叶斯优化的梯度增强模型,用于预测沿海含水层中的盐水入侵 (SWI) 形长度比 (L/La).
- 为了识别影响SWI的关键水文地质因素,使用SHapley添加式扩张 (SHAP).
主要方法:
- 使用SEAWAT数值模拟生成了456个样本的数据集,其中包括床的斜率和屏障深度等变量.
- 训练并测试了四种贝叶斯优化的梯度增强模型:轻梯度增强 (LGB),随机梯度增强 (SGB),分类梯度增强 (CGB) 和极端梯度增强 (XGB).
- 使用RMSE和R2评估模型性能;使用SHAP进行特征重要性分析,并开发了一个交互式GUI.
主要成果:
- 光梯度增强 (LGB) 显示了最高的预测准确性 (RMSE:0.016培训,0.037测试) 和R2.
- 相对屏障墙距离和床的斜率被确定为影响SWI预测的最重要因素.
- 经过验证的LGB模型与阿克罗蒂里沿海含水层 (RMSE:0.04) 的参考结果强烈一致.
结论:
- 贝叶斯优化的LGB模型为预测盐水入侵 (SWI) 提供了一个高度准确和可解释的方法.
- 开发的交互工具和特征重要性分析支持在沿海含水层管理中的实际决策.
- 优化的机器学习模型显示了加强沿海地下水资源的可持续管理的巨大潜力.
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