Assessing Groundwater Salinization Using Spatial Machine Algorithm Techniques

作者：Khalifa M. Al‐Kindi, Hamed S. Al Dhuhli, N. K. Al-Mezini, Mohammed S. Al Nadabi, Dawood S. Alqasmi, Hamood S. Al-Hashmi, Muhammad İ̇rfan Ahamad, Siqi Lu · 发表于：Earth Systems and Environment · 年份：2025 · DOI：10.1007/s41748-025-00888-5 · 被引用次数：4 · 研究领域：Remote-Sensing Image Classification、Hydrological Forecasting Using AI、Water Quality Monitoring Technologies

Abstract Groundwater underpins livelihoods in arid regions yet remains vulnerable to climate variability and intensive abstraction. This study evaluates groundwater salinity susceptibility in the Wilayat of Barka, Oman, using ensemble learning—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—applied to a multi-decadal dataset (1985–2021). Inputs combine Landsat-derived land-use/land-cover (LULC) change, precipitation and temperature records, groundwater level and salinity observations, and LiDAR-based elevation, slope, and hydrological connectivity. Anthropogenic and geomorphological controls are represented by well density and distance to drainage. Model performance was strong: XGBoost achieved R² = 0.99 and MAPE = 0.011, while RF achieved R² = 0.97 and MAPE = 0.084. We produced five electrical-conductivity classes, from freshwater to very high salinity, and mapped spatial hotspots. XGBoost emphasized vulnerability in the north and northeast; RF highlighted the northwest, central, and southeastern sectors. Variable importance consistently favored groundwater level, followed by elevation, temperature, and rainfall. The integrated remote-sensing, GIS, and machine-learning workflow is reproducible and scalable, enabling routine salinity surveillance where monitoring networks are sparse. Findings provide actionable evidence for prioritizing protection, guiding well licensing, and targeting recharge and demand-management interventions. More broadly, the framework suppor...