Long-Term Retrospective Predicted Concentration of PM_2.5 in Upper Northern Thailand Using Machine Learning Models.

ABSTRACT: This study aims to build, for the first time, a model that uses a machine learning (ML) approach to predict long-term retrospective PM_2.5 concentrations in upper northern Thailand, a region impacted by biomass burning and transboundary pollution. The dataset includes PM₁₀ levels, fire hotspots, and critical meteorological data from 1 January 2011 to 31 December 2020. ML techniques, namely multi-layer perceptron neural network (MLP), support vector machine (SVM), multiple linear regression (MLR), decision tree (DT), and random forests (RF), were used to construct the prediction models. The best ML prediction model was selected considering root mean square error (RMSE), mean prediction error (MPE), relative prediction error (RPE) (the lower, the better), and coefficient

SUBMITTER: Kawichai S 

PROVIDER: S-EPMC11946178 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature