Abstract
Modeling techniques can be powerful predictors of soil salinity across various scales, ranging from local landscapes to global territories. This study was aimed to examine the accuracy of soil salinity prediction model integrating ANNs (artificial neural networks) and topographic factors with different cell sizes. For this purpose, soil salinity was determined at 103 points in the east of Mashhad, Razavi Khorasan, Iran. The region was categorized into two distinct parts: study area (1) (with a steep topography) and study area (2) (with a flat topography). To explore the impact of terrain on salinity prediction accuracy, ANNs were trained using topographical factors as inputs across a range of cell sizes (30, 50, 90, 200, and 500 m). The model's effectiveness was evaluated based on their Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated variability in model performance, with RMSE ranging from 0.324 to 0.461 and R2 from 0.159 to 0.281 across the spectrum of cell sizes. Deeper analysis on different topographical influences showed that for the study area (1), a cell size of 30 m yielded the most accurate predictions (RMSE = 0.234 dS/m and R2 = 0.515), whereas for the study area (2), a cell size of 50 m was optimal (RMSE = 0.658 dS/m and R2 = 0.597). In general, the findings concluded that smaller cell sizes can enhance prediction accuracy in areas with complex and varied topography, while larger cell sizes can be more effective in flat areas. This study demonstrates the significance of incorporating terrain attributes and their optimal resolutions for accurate soil salinity prediction. Our findings underscore the importance of tailoring the resolution of input data to match the specific topographic features of the area, challenging the conventional notion that higher input resolution invariably yields better results in soil properties prediction. These insights provide valuable guidance for effective soil management and agricultural practices, as well as contribute to more informed decision-making in land management and environmental conservation.