![]() ![]() Predictive DL models were constructed by dividing datasets randomly into train (70%) and test (30%) groups, six statistical indicators being then applied to assess the DL hybrid model performance for both datasets (train and test). Game theory was used for the interpretability of the DL model’s output. ![]() The Dragonfly algorithm (DA) was used to identify the critical features controlling dust sources. This study aimed to classify the susceptibility of dust sources in the Middle East (ME) by developing two novel deep learning (DL) hybrid models based on the convolutional neural network–gated recurrent unit (CNN-GRU) model, and the dense layer deep learning–random forest (DLDL-RF) model. Therefore, classification of dust storm sources into different susceptibility categories can help us mitigate its negative effects. Dust storms have many negative consequences, and affect all kinds of ecosystems, as well as climate and weather conditions.
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