Evaluating land degradation and environmental hazards in North delta Egypt using machine learning and GIS approaches.

Soil degradation constitutes a critical challenge, particularly in arid and semi-arid regions, where its impacts are most severe. To mitigate these impacts, comprehensive strategies must be developed to rehabilitate and restore soil functionality. The interplay of physical, chemical, and biological characteristics jointly influences the evolution of soil processes and the restoration of its functions. This study aims to evaluate the soil quality, land degradation, and ecological risks using an integrated approach that combines GIS and remote sensing with machine learning (ML) models, specifically artificial neural network (ANN), random forest (RF), and decision tree (DT) methodologies. Utilizing Landsat ETM + images alongside a Digital Elevation Model (DEM), a geomorphological map was generated, revealing that the studied area consists of two distinct landscapes: floodplain and lacustrine plain. The results indicated that primary forms of soil degradation in the examined region included salinization, alkalization, compaction, and waterlogging. Remarkably, 90.61% of the study area was classified as high quality, whereas 9.39% was categorized as moderate quality. Furthermore, measurements of soil pollution showed considerable variation in the concentrations of trace elements throughout the area. The geo-accumulation index (I