Predicting the dust events frequency around a degraded ecosystem and determining the contribution of their...

This study was aimed to evaluate the performance of gradient boosting machine (GBM) and extreme gradient boosting (XGB) models with linear, tree, and DART boosters to predict monthly dust events frequency (MDEF) around a degraded wetland in southwestern Iran. The monthly required data for a long-term period from 1988 to 2018 were obtained through ground stations and satellite imageries. The best predictors were selected among the eighteen climatic, terrestrial, and hydrological variables based on the multicollinearity (MC) test and the Boruta algorithm. The models' performance was evaluated using the Taylor diagram. Game theory (i.e., SHAP values: SHV) was used to determine the contribution of factors controlling MDEF in different seasons. Mean wind speed, maximum wind speed, rainfall, standardized precipitation evapotranspiration index (SPEI), soil moisture, erosive winds frequency, vapor pressure, vegetation area, water body area, and dried bed area of the wetland were confirmed as the best variables for predicting the MDEF around the studied wetland. The XGB-linear and XGB-tree showed a higher capability in predicting the MDEF variations in the summer and spring seasons. However, the XGB-Dart yielded better than XGB-linear and XGB-tree models in predicting the MDEF during the autumn and winter seasons. Rainfall (SHV = 1.6), surface water discharge (SHV = 2.4), mean wind speed (SHV = 10.1), and erosive winds frequency (SHV = 1.6) had the highest contribution in predicting the target variable in winter, spring, summer, and autumn, respectively. These findings demonstrate the effectiveness of the gradient boosting-based approaches and game theory in determining the factors affecting MDEF around a destroyed international wetland in southwestern Iran and the findings may be used to diminish their impacts on residents of this region of Iran.