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.