Machine learning-driven scenario-based models for predicting desert dust sources in central playas of Iran

Monitoring and controlling dust sources under climate changes and developing suitable prediction approaches is very important, since they have direct impacts on the environment and human health. The current study was performed with the aim of predicting the dust emission of Yazd province, located in the central playas of Iran, by combining a machine learning ensemble model and the global climate model IPSL-CM6A-LR. The key variables of dust emission including physiographic characteristics, climatic variables and human factors were mapped in order to model occurrence data against background data. After removing 15 autocorrelated points, from 120 pixels located in dust sources, 75 % and 25 % of them were randomly selected as training and test datasets, respectively. To evaluate modeling, in addition to sensitivity, specificity, TSS and Kappa indices, the Partial ROC approach was used. For the final mapping of land susceptibility to dust emission, individual models based on the weighted average of AUC(ratio) values were used to create the ensemble model. The relative importance of dust emission controlling factors was obtained with the consensus of seven machine learning models: distance to dirt roads, altitude, distance to mines and mean temperature of driest quarter. MARS outperformed the other individual models, so that it showed no significant difference with the ensemble model at the thresholds of E = 10 and E = 5 (p-value < 0.05). Statistical analysis of the ensemble model with AUC(ratio) equal to 1.933 showed that currently 28.56 percent of the province, is prone to dust emission and it will increase linearly in the future for scenarios SSP1-2.6 and SSP5-8.5. In both time periods (2041-2070 and 2071-2100), dust emissions will be higher under the SSP5-8.5 scenario compared to the SSP1-2.6 scenario. The results can be used by policymakers for the sustainable management of deserts and thus reduce the impacts of dust on current and future ecosystem health.