Dust source susceptibility in the lower Mesopotamian floodplain of Iraq

The identification of susceptible dust sources (SDSs) based on the analysis of effective factors (i.e. dust drivers) is considered to be one of the primary and cost-effective solutions to deal with this phenomenon. Accordingly, this study aimed to identify SDSs and delineate their drivers using remote sensing data and machine learning (ML) algorithms in a hotspot area in the Lower Mesopotamian floodplain in southern Iraq. To model SDSs, a total of 15 environmental features based on remote sensing data such as topographic, climatic, land use/cover, and soil properties were considered as dust drivers and fed into the four well-known ML algorithms, including linear discriminant analysis (LDA), logistic model tree (LMT), extreme gradient boosting (XGB)-Linear, and XGB-Tree-based. Dust emission hotspots were identified by visual interpretation of sub-daily MODIS-Terra/Aqua true color composite imagery (2000–2021) to train (70%) and validate (30%) ML algorithms. Considering the variability of the spatial-temporal patterns of SDSs as a result of changes in dust drivers, the modeling process was carried out in four periods, including 2000–2004, 2005–2007, 2008–2012, and 2013–2021. Our results show that dust events in the study area occur most frequently in April, June, July, and August. Overall, all ML algorithms performed well and provided reliable results for identifying SDSs. However, the XGB-Linear provided the most reliable results with an average area under curve (AUC) of 0.79 for the study periods. Precipitation was determined as the most important dust driver. The SDS maps produced can be used as a basis for the development of rehabilitation plans in the study area to mitigate the adverse effects of dust storms.