Incorporating land-use regression into machine learning algorithms in estimating the spatial-temporal variation of carbon monoxide in Taiwan

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 R2 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.