Novel deep learning hybrid models (CNN-GRU and DLDL-RF) for the susceptibility classification of dust sources in the Middle East: a global source.

ABSTRACT: Dust storms have many negative consequences, and affect all kinds of ecosystems, as well as climate and weather conditions. Therefore, classification of dust storm sources into different susceptibility categories can help us mitigate its negative effects. 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. The Dragonfly algorithm (DA) was used to identify the critical features controlling dust sources. Game theory was used for the interpretability of the DL model's output. Predictive DL models were constructed by dividing datasets randomly into train (70%)

SUBMITTER: Gholami H 

PROVIDER: S-EPMC9652306 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature