Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. This study explores the accuracy of Artificial Intelligence (AI) models: adaptive-network-based fuzzy inference system (ANFIS) and multi-layered perceptron artificial neural network (mlp-NN) over the southwestern United States (SWUS) based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations at monthly and seasonal timescale from 1990-2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from North American Regional Reanalysis (NARR). The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BISA). ANFIS model generally performs better than mlp-NN model in predicting regional dustiness over the SWUS region with r of 0.77 and 0.83 for monthly and seasonal fine dust respectively. AI models perform better in predicting regional dustiness at a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust at both monthly and seasonal timescales. Compared to precipitation, the near-surface average temperature is the more important predictor of the regional dustiness at both monthly and seasonal timescale. However, compared to the monthly timescale, air temperature is less more important predictor than precipitation at the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models have a good potential to predict monthly and seasonal fine and coarse dust at acceptable accuracy based on basic climatic data.