Machine learning methods for modelling and predicting dust storms in Iraq

Abstract

Dust storms are a significant problem that impacts humans, the environment, and the economy in Iraq, especially in the Baghdad, Nineveh, and Basra provinces, which have the most vital and substantial urban agglomerations in Iraq. These areas are heavily affected by dust storms. The monthly dust storm data and factors based on temperature, surface pressure, wind speed, wind direction, humidity, and precipitation were sourced from the Iraqi Meteorological Organization and Seismology and NASA from January 1981 to December 2022.In this study, we used the principal components intended to reduce the interrelated variables and capture the components that account for at least 80\% of the total variance in the data set. Various supervised machine learning algorithms created a model to analyze and predict the monthly frequency of dust storms in the three provinces until March 2027. Our findings indicate that the Additive Regression model, employing the IBk lazy algorithm for the Basrah and Nineveh regions and the KStar lazy algorithm for Baghdad, outperformed other methods in terms of accuracy. The results suggest a reduction in the occurrence of dust storms in the three provinces, with this downward trend projected to persist over the next 40 months.