Air pollution prediction with clustering-based ensemble of evolving spiking neural networks and a case study for London area. (August 2019)

• Record Type: Journal Article
• Authors: Maciąg, Piotr S.; Kasabov, Nikola; Kryszkiewicz, Marzena; Bembenik, Robert
• Is Part Of: Environmental modelling & software. Volume 118(2019)
• Page Start: 262
• Page End: 280
• Publication Date: 2019-08
• Subjects: Spiking neural network -- NeuCube -- Ozone pollution, PM10 pollution -- Pollution level prediction -- Clustering -- Spatio-temporal data; 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.2019.04.012
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: In this article, we propose a novel Clustering-based Ensemble model (CEeSNN) for air pollution prediction based on evolving Spiking Neural Networks (eSNN), where each eSNN network is trained on a separate set of time series. In our approach, we generate training sets by clustering an initial set of time series with respect to pollution values. Each obtained cluster of time series is used to build a single eSNN network. In the experiments, we forecasted ozone and PM10 pollution for Greater London Area for 1, 3, and 6 h ahead based on data from three monitoring sites located there. The prediction quality of the proposed CEeSNN model, as well as the singleton NeuCube model, an MLP network and the ARIMA model was assessed by means of several quality measures. The experimental results show that the proposed ensemble model is able to give significantly better forecasting results than the other three models. Highlights: A clustering-based ensemble of evolving spiking neural networks for air pollution prediction The air pollution data for the Greater London Area used in experiments Comparison with a single NeuCube instance of evolving spiking neural networks Comparison with multilayer perceptron neural network Comparison with autoregressive integrated moving average model