Evaluation of Machine-Learning Models for Predicting Aeolian Dust: A Case Study over the Southwestern USA

Abstract

Aeolian dust has widespread consequences on health, the environment, and the hydrology over a region. This study investigated the performance of various machine-learning (ML) models including Multiple Linear Regression (MLR), Support Vector Machines (SVM), Random Forests (RF), Bayesian Regularized Neural Networks (BRNN), and Cubist (Cu) in predicting dust emissions over the Southwestern United States (US). Six meteorological and climatic variables (precipitation, air temperature, wind speed, ENSO, PDO, and NAO) were used to predict dust emissions. The correlation (r) and root mean square error (RMSE) for fine dust vary from 0.67 to 0.80, and 0.40 to 0.52 µg/m3, respectively. For coarse dust, the r and RMSE vary from 0.69 to 0.73, and 2.01 to 2.34 µg/m3, respectively. The non-linear ML models outperformed linear regression for both fine and coarse dust. ML models underestimated high concentrations of dust. Machine-learning models better predict fine dust than coarse dust over the Southwestern USA. Air temperature was found to be the most important predictor, followed by precipitation, for both fine- and coarse- dust-prediction over the region. These results improve our understanding of the predictability of Southwestern US dust.