Abstract：
Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl-, Fe++, Ca++, Mg++, Na+, SO4--, Mn++, HCO3-, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.
摘要：
由于资源稀缺，评估干旱地区的水质至关重要。影响健康和可持续管理。这项研究考察了Assit省的地下水质量，埃及,使用主成分分析，GIS,和机器学习技术。分析了来自12个参数的217口井的数据，包括TDS,EC,Cl-,Fe++,Ca++,Mg++,Na+,SO4--,Mn++,HCO3-,K+,和pH。计算了水质指数(WQI)，ArcGIS绘制了其空间分布图。机器学习算法，包括岭回归,XGBoost,决策树,随机森林,和K-最近的邻居,用于预测分析。更高浓度的钠，K,Ca,Mg,Mn,和铁与工业和人口稠密地区相关。大多数样品表现出优异或良好的质量，一小部分不适合消费。岭回归显示出最低的MAPE率(0.22%的训练，测试中的0.26%）。这项研究强调了先进的机器学习对干旱地区可持续地下水管理的重要性。因此，我们的结果可以为参与水管理决策的国家和地方当局提供宝贵的帮助，特别是水资源管理者和决策者。这些信息可以帮助制定旨在保护和可持续管理地下水资源的法规，这对国家的全面繁荣至关重要。
