Reem K. Alshammari This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Omer Alrwais1, Mehmet Sabih Aksoy1

1 Information Systems Department, King Saud University, Riyadh 11451, Saudi Arabia
2 Space and Aeronautics Research Institute, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia


Received: May 18, 2022
Revised: September 25, 2022
Accepted: October 26, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220183  


Cite this article:

Alshammari, R.K., Alrwais, O., Aksoy, M.S. (2022). Machine Learning Applications to Dust Storms: A Meta-Analysis. Aerosol Air Qual. Res. 22, 220183. https://doi.org/10.4209/aaqr.220183


HIGHLIGHTS

  • A Meta-analysis of machine learning applications on dust storm prediction conducted.
  • Applying machine learning to meteorological data provides daily dust storm prediction.
  • Machine learning techniques can detect dust aerosol from satellite image in real time.
  • Machine learning classification can distinguish dust aerosol from other features.
 

ABSTRACT


Dust storms are natural hazards that affect both people and properties. Therefore, it is important to mitigate their risks by implementing an early notification system. Different methods are used to predict dust storms, such as observing satellite images, analyzing meteorological data, and using numerical weather prediction model forecasts. However, recent studies have shown that machine learning algorithms have higher capacities to predict dust storms in less time and with fewer processing operations compared to numerical weather models. This paper conducted a meta-analysis review to examine studies that addressed the areas associated with the application of machine learning to dust storm prediction. It aims to compare the applied models and the types of data used in the literature under study. Given that the location of a dust storm event is essential, the properties of dust storms are discussed in relation to the region. The output classes and the various performance metrics observed in each reviewed paper are also summarized. Subsequently, the present paper offers a detailed analysis highlighting the capabilities of machine learning models in predicting dust storms. The analysis shows two main categories: early detection and dust storm prediction. Most models used for dust storm early detection from satellite images are support vector machines (SVM). In contrast, the most used models for dust storm prediction are SVM and random forests that predict the occurrence of dust storms from meteorological data. Finally, the paper highlights the challenges and future trends in the field, illustrating the potential directions for applying deep learning algorithms and providing long-range predictions with assessments of dust storm duration and intensity.


Keywords: Machine learning, Dust storm detection, Dust storm prediction, Deep learning algorithms




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