该研究的目的是基于机器学习算法和气候变化情景来估计未来的地下水潜力区。十四个参数(即,曲率,排水密度,斜坡,粗糙度,降雨,温度,相对湿度,线条密度,土地利用和土地覆盖,一般土壤类型,地质学,地貌学,地形位置指数(TPI),地形湿度指数(TWI))用于开发机器学习算法。三种机器学习算法(即,人工神经网络(ANN),逻辑模型树(LMT),和逻辑回归(LR))用于确定地下水潜力区。基于ROC曲线选择最佳拟合模型。2.5、4.5、6.0和8.5气候情景的代表性浓度途径(RCP)用于模拟未来的气候变化。最后,根据最佳机器学习模型和未来RCP模型,确定了2025年、2030年、2035年和2040年的未来地下水潜力区。根据调查结果,ANN显示出比其他两个模型更好的准确性(AUC:0.875)。ANN模型预测,23.10%的土地处于非常高的地下水潜力区,而33.50%的人在地下水潜力极高的地区。该研究使用ANN模型预测了2025年,2030年,2035年和2040年不同气候变化情景(RCP2.6,RCP4.5,RCP6和RCP8.5)下的降水值,并显示了每种情景的空间分布图。最后,为未来的地下水潜力区生成了16种情景。政府官员可以利用研究结果为国家一级的水资源管理和规划提供基于证据的选择。
The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study\'s results to inform evidence-based choices on water management and planning at the national level.