Developing a GMDH-type neural network model for spatial prediction of NOx : A case study of Çerkezköy, Tekirdağ

Authors : Can Burak ÖZKAL, Özkan ARSLAN

Pages : 56-71

Doi:10.35208/ert.1000739

View : 22 | Download : 13

Publication Date : 2022-03-31

Article Type : Research Paper

Abstract :Air pollution-induced issues involve public health, environmental, agricultural and socio-economic aspects. Therefore, decision-makers need low-cost, efficient tools with high spatiotemporal representation for monitoring air pollutants around urban areas and sensitive regions. Air pollution forecasting models with different time steps and forecast lengths are used as an alternative and support to traditional air quality monitoring stations insert ignore into journalissuearticles values(AQMS);. In recent decades, given their eligibility to reconcile the relationship between parameters of complex systems, artificial neural networks have acquired the utmost importance in the field of air pollution forecasting. In this study, different machine learning regression methods are used to establish a mathematical relationship between air pollutants and meteorological factors from four AQMS insert ignore into journalissuearticles values(A-D); located between Çerkezköy and Süleymanpaşa, Tekirdağ. The model input variables included air pollutants and meteorological parameters. All developed models were used with the intent to provide instantaneous prediction of the air pollutant parameter NOx within the AQMS and across different stations. In the GMDH insert ignore into journalissuearticles values(group method of data handling);-type neural network method insert ignore into journalissuearticles values(namely the self-organizing deep learning approach);, a five hidden layer structure consisting of a maximum of five neurons was preferred and, choice of layers and neurons were made in a way to minimize the error. In all models developed, the data were divided into a training insert ignore into journalissuearticles values(%80); and a testing set insert ignore into journalissuearticles values(%20);. Based on R 2 , RMSE, and MAE values of all developed models, GMDH provided superior results regarding the NOx prediction within AQMS insert ignore into journalissuearticles values(reaching 0.94, 10.95, and 6.65, respectively for station A); and between different AQMS. The GMDH model yielded NOx prediction of station B by using station A input variables insert ignore into journalissuearticles values(without using NOx data as model input); with R2, RMSE and MAE values 0.80, 10.88, 7.31 respectively. The GMDH model is found suitable for being employed to fill in the gaps of air pollution records within and across-AQMS.
Keywords : NOx prediction, GMDH, self organizing deep learning, artificial intelligence, air pollutant, NOx