Incorporating land-use regression into machine learning algorithms in estimating the spatial-temporal variation of carbon monoxide in Taiwan. (May 2021)

• Record Type: Journal Article
• Authors: Wong, Pei-Yi; Hsu, Chin-Yu; Wu, Jhao-Yi; Teo, Tee-Ann; Huang, Jen-Wei; Guo, How-Ran; Su, Huey-Jen; Wu, Chih-Da; Spengler, John D.
• Is Part Of: Environmental modelling & software. Volume 139(2021)
• Publication Date: 2021-05
• Subjects: Carbon monoxide (CO) -- Land-use regression (LUR) -- Deep neural network (DNN) -- Random forest (RF) -- Extreme gradient boosting (XGBoost); 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.2021.104996
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper is the first of its kind to use machine learning algorithms in conjunction with a Land-use Regression (LUR) model for predicting the spatiotemporal variation of CO concentrations in Taiwan. We used daily CO concentration from 2000 to 2016 to develop model and data from 2017 to 2018 as external data to verify the model reliability. Location of temples was used as a predictor to account for Asian culturally specific sources. With the ability to capture nonlinear relationship between observations and predictions, three LUR-based machine learning algorithms were used to estimate CO concentrations, including deep neural network (DNN), random forest (RF), and extreme gradient boosting (XGBoost). The results showed that LUR-based machine-learning model (LUR-XGBoost) has the best computation efficiency and improved adjusted R 2 from 0.69 to 0.85. Our studies demonstrate the ability of the LUR-based machine learning algorithms to estimate long-term spatiotemporal CO concentration variations in fine resolution. Graphical abstract: Image 1 Highlights: Long-term daily CO concentrations were estimated with LUR-machine learning models. Land-use patterns were included in machine learning models by using land-use regression. The most contributed predictors were identified by stepwise variable selection. Explanatory power of daily CO concentration was increased from 0.69 to 0.85. XGboost outperformed RF and DNN machine learning algorithms.