Machine Learning approaches for identifying factors influencing dust sensitivity in the Gavkhouni Basin, Central Iran

Abstract

This study identifies key factors affecting dust susceptibility in Gavkhouni Basin, central Iran, using three feature selection algorithms and a perceptual neural network model. Accuracy assessment statistics were used to evaluate the prediction capabilities of the models. The aerosol optical depth dataset validated the dust-generating area map, with the permutation feature importance method prioritizing factors controlling dust events. Using the variables selected by the genetic algorithm improved the coefficient of explanation by 31% compared to relief, and 19% compared to ElasticNet algorithm. The genetic algorithm proved effective in identifying variables that significantly enhanced model accuracy in high-risk zones (precision = 0.75, recall = 0.71, and F1 = 0.73). The study found that topographic diversity, geology, soil sand content, precipitation, wind speed, soil salinity, soil subsidence, vegetation cover, slope, and soil moisture were key environmental factors. These findings are very important for the formulation of specific measures for improving air quality and limiting dust-related effects as a key factor in the sustainable management of vulnerable ecosystems.