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Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting

Zhou, Yanlai, Chang, Fi-John, Chang, Li-Chiu, Kao, I-Feng, Wang, Yi-Shin, Kang, Che-Chia
The Science of the total environment 2019 v.651 pp. 230-240
air quality, data collection, humans, model validation, models, monitoring, particulates, risk, support vector machines, time series analysis, traffic, urbanization, Taiwan
Air quality deteriorates fast under urbanization in recent decades. Reliable and precise regional multi-step-ahead PM2.5 forecasts are crucial and beneficial for mitigating health risks. This work explores a novel framework (MM-SVM) that combines the Multi-output Support Vector Machine (M-SVM) and the Multi-Task Learning (MTL) algorithm for effectively increasing the accuracy of regional multi-step-ahead forecasts through tackling error accumulation and propagation that is commonly encountered in regional forecasting. The Single-output SVM (S-SVM) is implemented as a benchmark. Taipei City of Taiwan is our study area, where three types of air quality monitoring stations are selected to represent areas imposed with high traffic influences, high human activities and commercial trading influences, and less human interventions close to nature situation, respectively. We consider forecasts of PM2.5 concentrations as a function of meteorological and air quality factors based on long-term (2010–2016) observational datasets. Firstly, the Kendall tau coefficient is conducted to extract key spatiotemporal factors from regional meteorological and air quality inputs. Secondly, the M-SVM model is trained by the MTL to capture non-linear relationships and share correlation information across related tasks. Lastly, the MM-SVM model is validated using hourly time series of PM2.5 concentrations as well as meteorological and air quality datasets. Regarding the applicability of regional multi-step-ahead forecasts, the results demonstrate that the MM-SVM model is much more promising than the S-SVM model because only one forecast model (MM-SVM) is required, instead of constructing a site-specific S-SVM model for each station. Moreover, the forecasts of the MM-SVM are found better consistent with observations than those of any single S-SVM in both training and testing stages. Consequently, the results clearly demonstrate that the MM-SVM model could be recommended as a novel integrative technique for improving the spatiotemporal stability and accuracy of regional multi-step-ahead PM2.5 forecasts.