使用机器学习方法进行地下水盐度建模和绘制:阿尔及利亚西迪奥克巴地区的案例研究
Samir Boudibi1, Haroun Fadlaoui2, Fatima Hiouani3
1Centre de Recherche Scientifique et Technique sur les Régions Arides, CRSTRA, Biskra, Algeria. Samir.boudhibi@gmail.com.
概括
机器学习模型有效地预测了阿尔及利亚的地下水盐度 (GWS). 随机森林模型非常出色,识别了关键因素,并绘制了高GWS区域,以更好地管理水资源.
科学领域:
- 水文地质学 水文地质学
- 环境科学 环境科学
- 机器学习应用 机器学习应用
背景情况:
- 地下水的化是复杂的,对控制因素的数据有限,这阻碍了准确的预测和绘制地图.
- 准确地绘制地下水盐度 (GWS) 对水资源管理至关重要,特别是在干旱和半干旱地区.
- 现有的方法往往忽视了输入变量组合对模型准确性的影响.
研究的目的:
- 在阿尔及利亚西迪奥克巴地区的Mio-Pliocene含水层中使用机器学习建模和绘制地下水盐度 (GWS).
- 确定影响GWS的关键因素,并评估它们对预测模型准确性的影响.
- 为了比较各种机器学习模型的性能,用于GWS预测.
主要方法:
- 使用有限的电导率 (EC) 测量数据集和数字升高模型 (DEM) 衍生品.
- 应用特征选择方法 (RFE,FFS,BFS) 来确定最佳的输入变量组合.
- 训练和评估了五种机器学习模型:随机森林 (RF),HyFIS,KNN,CRM和SVM.
主要成果:
- 随机森林 (RF) 模型在训练和测试阶段都表现出卓越的性能 (例如,R=0.854训练,R=0.831测试).
- 特性选择方法确定了关键的,经常被忽视的,输入变量组合,显著提高了模型准确性.
- 生成的GWS地图显示,远离水源的低海拔地区的盐分水平令人担忧.
结论:
- 机器学习,特别是射频模型,在最小的输入变量下为GWS提供了增强的预测性能.
- 该研究强调了考虑输入变量相互作用对于准确的GWS建模的重要性.
- 调查结果为管理地下水资源和解决西迪奥克巴地区盐化问题提供了关键的见解.
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