Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application and Comparison of MLR, ANN and CART Models for Predicting PM10 Concentration Level of Guwahati City (India)

Version 1 : Received: 2 October 2020 / Approved: 7 October 2020 / Online: 7 October 2020 (08:31:21 CEST)

How to cite: Dutta, A.; Jinsart, W. Application and Comparison of MLR, ANN and CART Models for Predicting PM10 Concentration Level of Guwahati City (India). Preprints 2020, 2020100146 (doi: 10.20944/preprints202010.0146.v1). Dutta, A.; Jinsart, W. Application and Comparison of MLR, ANN and CART Models for Predicting PM10 Concentration Level of Guwahati City (India). Preprints 2020, 2020100146 (doi: 10.20944/preprints202010.0146.v1).

Abstract

Indian cities are increasingly becoming susceptible to PM10 induced health effects which have become a matter of concern for the policymakers of the country. Air pollution is engulfing the comparatively smaller cities as the rapid pace of urbanization, and economic development seems not to lose steam ever. A review of air pollution of 28 cities of India, which includes tier-I, II, and III cities of India, found to have grossly violated both WHO and NAAQS standards in respect of acceptable daily average PM10 concentrations by a wide margin. Predicting the city level PM10 concentrations in advance and accordingly initiate prior actions is an acceptable solution to save the city dwellers from PM10 induced health hazards. Predictive ability of three models, linear MLR, nonlinear MLP (ANN), and nonlinear CART, for one day ahead PM10 concentration forecasting of tier-II Guwahati city, were tested with 2016-2018 daily average observed climate data, PM10, and gaseous pollutants. The results show that the non-linear algorithm MLP with feedforward backpropagation network topologies of ANN class, giving the best prediction value when compared with linear MLR and nonlinear CART model. ANN (MLP) approach, therefore, may be useful to effectively derive a predictive understanding of one day ahead PM10 concentration level and thus provide a tool to the policymakers for improving decision-making associated with air pollution and public health.

Subject Areas

particulate matter; prediction; model comparison; artificial neural network; multi-variate linear regression; small city

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