Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran

Predicting the dust events frequency around a degraded ecosystem and determining the contribution of their controlling factors using gradient boosting-based approaches and Game theory

“…Differences in the results related to the performance of the XGB model with past dust studies may be due to differences in the criteria considered for measuring dust occurrences and the studied years. By comparing the outcomes of this study with the Ebrahimi- Khus et al (2020b) ndings, it may also be concluded that the XGB model has a greater ability to predict the frequency of dust events, while it has less ability to predict the dust storm index in the desert areas.…”

“…In addition to these factors, the importance of monthly changes in the surface water discharge to Shadegan wetland was also con rmed to predict and analyze the in uential factors in winter and spring seasons. In the study conducted by Ebrahimi- Khus et al (2020b), the most important parameters used to predict the seasonal dust storm index in arid regions of Iran were wind speed, air temperature, rainfall, evapotranspiration, and vegetation cover. Of note, these factors were only chosen by the MC test, while the factors selected in this work were selected after performing two techniques of Boruta and MC test.…”

“…In other words, these results indicate that the linear booster in the XGB model causes weak generalization capability in winter, spring and autumn. Although the capability of the XGB model is poorly evaluated to predict dust storms in arid regions of Iran (Ebrahimi- Khus et al 2020b), its high e ciency has been proven in forecasting PM2.5 (Ma et al 2020) and other natural hazards, including the gully erosion (Chen et al 2021) and landslide studies (Sahin 2020). Differences in the results related to the performance of the XGB model with past dust studies may be due to differences in the criteria considered for measuring dust occurrences and the studied years.…”

“…The ability to reproduce and the ability to quantitatively analyze the contribution of factors affecting natural hazards and their potential for continuous updating are among the capabilities of machine learning models (Youssef and Pourghasemi 2021). Although the good performance of various machine learning models in spatial predicting of dustsensitive areas and their relatively good performance in temporal predicting of dust storm index (DSI) has been proven in some previous works (Chen et al 2019;Ebrahimi-Khus et al 2020b;Gholami et al 2020), the e ciency of XGB model with different boosters and GBM model has not been evaluated to forecast the MDEF in different seasons around degraded wetlands in the world. Thus, this evaluation was performed in this study.…”

Prediction of river suspended sediment load using machine learning models and geo-morphometric parameters

“…Reducing the number of variables is a useful tool for decreasing the volume of input data when modeling large datasets (Ebrahimi-Khusfi et al 2021). PCA was used to decrease the complexity of computation and determine which were the most important variables (Taghizadeh-Mehrjardi et al 2016).…”