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Signal attenuation due to dust and sand storms is one of the major problems in the utilization of microwave frequency bands for terrestrial and space communication especially in arid regions such as Northern Africa and Middle Eastern regions. In this paper, a machine learning (ML) model is developed to predict microwave signal attenuation due to atmospheric conditions recorded during dust and sand storms. The model utilizes recorded meteorological data, particularly optical visibility,doi:10.1109/access.2021.3132700 fatcat:lvp6ighu6feudgqaiwjryyci64