Incorporating Light Gradient Boosting Machine to land use regression model for estimating NO2 and PM2.5 levels in Kansai region, Japan. (September 2022)

• Record Type: Journal Article
• Authors: Thongthammachart, Tin; Araki, Shin; Shimadera, Hikari; Matsuo, Tomohito; Kondo, Akira
• Is Part Of: Environmental modelling & software. Volume 155(2022)
• Publication Date: 2022-09
• Subjects: Light gradient boosting machine -- Extreme gradient boosting -- Random forests -- Community multiscale air quality model -- Land use regression -- Air quality forecasting model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105447
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study incorporates Light Gradient Boosting Machine (LightGBM) to a land use regression (LUR) model for estimating NO2 and PM2.5 levels. The predictions were compared with LUR-based machine learnings models of Extreme Gradient Boosting (XGBoost) and Random Forests (RF). Weather Research and Forecasting (WRF) model-simulated meteorological parameters, Community Multiscale Air Quality modeling system (CMAQ)-simulated NO2 /PM2.5 concentrations, land use variables, and population data were used as predictor variables. The model performances were evaluated through spatial and temporal cross-validations (CV). The CV results indicated that the LightGBM model was moderately superior in NO2 and PM2.5 predictions compared to the RF and XGBoost models. Moreover, the LightGBM model had high performance in NO2 and PM2.5 predictions at high concentrations, which is essential for risk assessment. Our findings demonstrate that LightGBM can greatly improve the accuracy of NO2 and PM2.5 estimates. Highlights: Light Gradient Boosting Machine algorithm was added to Land use regression model. Daily average NO2 and PM2.5 levels are estimated. Light Gradient Boosting Machine model processes faster than other models. Light Gradient Boosting Machine model shows superior prediction accuracy. The developed model has high predictability at high concentration.