Abstract

Artificial-intelligence and machine-learning algorithms are gaining the attention of researchers in the field of groundwater modeling. This study explored and assessed a new approach based on Gini, Entropy, and Ratio based classification trees to predict spatial patterns of groundwater potential in a mountainous region of Iran. To do this, a springs inventory was undertaken, and 362 springs were identified in the study area. A set of geo-environmental and topo-hydrological factors (slope, aspect, elevation, topographic wetness index, distance from fault, distance from river, precipitation, land use, lithology, plan curvature, and roughness index) were used as predictors of groundwater. Results showed that Gini (AUC =0.865) achieved the best results, followed by entropy (AUC =0.847) and ratio (AUC =0.859). Lithology was determined to be the variable with the best association with groundwater in the study area. These results indicate that all three algorithms provide robust models of groundwater potential in this mountainous region.

• Highlights

• Gini, Entropy, and Ratio were investigated for groundwater potential mapping.

• Eleven groundwater-affecting factors were considered.

• Lithology is the most important factor for groundwater potential mapping

• Gini based decision tree is the best, followed by entropy and Ratio models

摘要

人工智能和机器学习算法正在地下水建模领域引起研究人员的关注。这项研究探索并评估了一种基于基尼，熵和比率的分类树的新方法，以预测伊朗山区的地下水潜力空间格局。为此，进行了一次弹簧清点，并在研究区域内确定了362个弹簧。一组地质环境和地形水文因素（坡度，坡向，海拔，地形湿度指数，到断层的距离，到河流的距离，降水，土地利用，岩性，平面曲率和粗糙度指数）被用作预测地下水的指标。 。结果显示，基尼（AUC = 0.865）取得了最佳结果，其次是熵（AUC = 0.847）和比率（AUC = 0.859）。在研究区域，岩性被确定为与地下水关系最好的变量。这些结果表明，所有三种算法均提供了该山区地下水潜力的可靠模型。

• 强调

• 研究了基尼，熵和比率，以绘制地下水势图。

• 考虑了11个影响地下水的因素。

• 岩性是绘制地下水潜力的最重要因素

• 基于基尼的决策树是最好的，其次是熵和比率模型