Spatiotemporal prediction of nitrogen dioxide concentration: an interval type-2 intuitionistic fuzzy neural network approach

Abstract

The concentration of nitrogen dioxide (NO2) in the air significantly affects environmental protection and public health. Current methods for NO2 concentration prediction lack sufficient characterization of spatiotemporal correlations. Therefore, this study proposes a novel approach using interval type-2 intuitionistic fuzzy neural networks (IT2IFNNs) for spatiotemporal prediction of NO2 concentrations. Firstly, the framework of IT2IFNNs is elucidated, incorporating variable coefficient weighting for its membership and non-membership outputs, and employing a random vector functional-link neural network (RVFLNN) as the rule consequent. Secondly, a hierarchical clustering algorithm is employed to determine the fuzzy rule base and optimize the output weight values of the network consequents using least squares estimation. Finally, numerical validation is conducted using real NO2 concentration data collected in Beijing from January to March 2018. Experimental results demonstrate that compared to existing methods, the proposed approach achieves superior prediction accuracy and efficiency in both short-term and long-term spatiotemporal prediction tasks.

Keywords

• nitrogen dioxide concentration spatiotemporal prediction;interval type-2 intuitionistic fuzzy neural networks;structural identification;parameter optimization;least squares method