Prediction of aerosol optical depth over Pakistan using novel hybrid machine learning model

Impact of aerosols on health includes both long-term chronic irritation and inflammation of the respiratory tract. Aerosol optical depth (AOD), a crucial optical parameter that assesses the extinction effect of atmospheric aerosols, is frequently used to estimate the extent of air pollution on large scales. So, the better prediction of AOD is crucial for understanding the health impacts of aerosols. The accurate prediction of AOD is difficult due to its nonlinear relationships with other climatic variables, uncertainties, and time series variable characteristics. In this paper, a machine learning (ML) model such as support vector regression (SVR), novel hybrid SVR-GWO model (SVR integrated with gray wolf optimizer (GWO)), and statistical model multi-linear regression (MLR) are used to predict AOD. Also, for SVR-GWO model, SVR hyper-parameters are optimized using meta-heuristic GWO algorithm. Satellite-based data of Pakistan is used for the prediction of AOD on monthly bases. In addition, preprocessing techniques of forward feature selection (FFS) is utilized to select the optimal input features for the SVR-GWO, SVR and MLR models to predict AOD. The performance of the novel hybrid SVR-GWO, SVR, and MLR model is analyzed using RMSE, MAE, RRMSE, R2 and Taylor diagram, and it is found that hybrid SVR-GWO model (RMSE = 0.07, MAE = 0.06, RRMSE = 0.22 and R2=0.60) is better than ordinary SVR model (RMSE = 0.10, MAE = 0.07, RRMSE = 0.29 and R2=0.18) and MLR model (RMSE = 0.11, MAE = 0.07, RRMSE = 0.32 and R2=0.03). Keynotes: (a) The study demonstrates the potential of ML models such as SVR-GWO for accurate prediction of AOD, which can aid in better understanding of the health impacts of aerosols. (b) The use of preprocessing techniques like FFS and optimization algorithms like GWO can significantly improve the performance of the ML (SVR-GWO) model in predicting AOD. (c) The findings of this study can be useful for policymakers and healthcare professionals in identifying regions and populations at risk of aerosol-induced respiratory health issues and designing effective interventions to mitigate them.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).