期刊
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
卷 136, 期 -, 页码 15-33出版社
ELSEVIER
DOI: 10.1016/j.future.2022.05.020
关键词
Spatio-temporal forecasting; Air pollution; Multi-objective optimization; Evolutionary algorithms; Machine learning; Ensemble learning
资金
- SITSUS project by the Spanish Ministry of Science, Innovation and Universities (MCIU) [RTI2018-094832-B-I00]
- Spanish Agency for Re-search (AEI)
- European Fund for Regional Development (FEDER)
This paper proposes a novel spatio-temporal approach based on multi-objective evolutionary algorithms for air pollution forecasting, showing promising results in the prediction of NO2 in southeastern Spain and opening up a new field in air pollution engineering.
Nowadays, air pollution forecasting modeling is vital to achieve an increase in air quality, allowing an improvement of ecosystems and human health. It is important to consider the spatial characteristics of the data, as they allow us to infer predictions in those areas for which no information is available. In the current literature, there are a large number of proposals for spatio-temporal air pollution forecasting. In this paper we propose a novel spatio-temporal approach based on multi-objective evolutionary algorithms for the identification of multiple non-dominated linear regression models and their combination in an ensemble learning model for air pollution forecasting. The ability of multi-objective evolutionary algorithms to find a Pareto front of solutions is used to build multiple forecast models geographically distributed in the area of interest. The proposed method has been applied for one-week NO2 prediction in southeastern Spain and has obtained promising results in statistical comparison with other approaches such as the union of datasets or the interpolation of the predictions for each monitoring station. The validity of the proposed spatio-temporal approach is thus demonstrated, opening up a new field in air pollution engineering. (C) 2022 The Author(s). Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据