Abstract
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.
Highlights
Wetlands are considered as the transitional area between aquatic and terrestrial ecosystems (Gokce 2018)
One of the appropriate strategies for sustainable development in areas prone to dust storms, such as degraded wetlands, is to predict the frequency of these events and to detect the parameters that control them in different seasons
The MC test and Boruta algorithm helped us to choose the best combination of input data to forecast the monthly DEF (MDEF) in different seasons
Summary
Wetlands are considered as the transitional area between aquatic and terrestrial ecosystems (Gokce 2018). These valuable ecosystems are divided into two categories of natural and man-made wetlands and provide a wide range of services to humanity, including water supply, climate regulation, and carbon sequestration (Jia et al 2020; Martins et al 2020). The water levels of these valuable ecosystems have declined due to climate change, meteorological droughts, and overuse of surface and groundwater resources (Cao et al 2012; Ebrahimi-Khusfi et al 2020a). Using the spectral indices derived from remotely-sensed data, it was demonstrated that the water level of many wetlands has decreased in many countries, including China (Jiang et al 2017; Song et al 2014), Nigeria (Ayanlade and Proske 2016), Ethiopia (Gebresllassie et al 2014), India (Chatterjee et al 2015), and Iran (Ebrahimi-Khusfi et al 2020a)
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