Dust Source and Susceptibility Map in Iran and Iraq: Application of Remote Sensing and Machine Learning Techniques

The dust storm is one of the major environmental problems that has affected many arid regions of the world. This research aimed to investigate the factors affecting the dust source area (DSA) and to prepare a sensitivity map in Khuzestan province in Iran and Al-Basrah and Maysan provinces in Iraq. For this research, Remote Sensing (RS) techniques and machine learning techniques, including Multivariate Adaptive Regression Spline (MARS), Random Forest (RF), and Logistic Regression (LR), were used for dust source identification and susceptibility map. One hundred fifty-two DSA for a period of 2005–2020 were identified in the study area. Seventy percent of sources were selected for the Dust Source Susceptibility Mapping (DSSM) (training dataset), and thirty percent of sources were used for model validation. Consequently, six factors including soil, lithology, slope, and normalized vegetation differential index, geomorphology, and land use units were prepared as independent and effective variables on the DSA. The results of all three models indicated that land use had the most impact on the creation of DSA. The validation results of these models using the training data showed sub-curves of 0.92, 0.86, and 0.76 for the RF, MARS, and LR models, respectively. Also, the outcome showed that the RF model had the best performance in comparison with MARS (AUC = 0.89) and LR (AUC = 0.78) methods. The results showed that in all three models, high and very high susceptibility classes generally covered a large percentage of the case study. The highest percentage of dust source points was also in this susceptibility category. The results of this study can be useful for planners and managers to control and reduce the risk of negative dust consequences.