Prediction of Ozone Concentration using Feed Forward Back Propagation Neural Network (FFBP-NN)
Norhazlina Suhaimi1, Nurul Adyani Ghazali2, Ahmad Zia Ul-Saufie Mohamad Japeri3
1Nurul Adyani Ghazali*, Faculty of Ocean Engineering Technology & Informatics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia.
2Norhazlina Suhaimi, Faculty of Ocean Engineering Technology & Informatics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia.
3Ahmad Zia Ul-Saufie Mohamad Japeri, Faculty of Computer and Mathematical Science, Universiti Teknologi MARA, Permatang Pauh, Pulau Pinang, Malaysia.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9257-9260 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9406118419/2019©BEIESP | DOI: 10.35940/ijrte.D9406.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Air pollution has been an ongoing problem in Malaysia. One of the major air quality issue in Malaysia is high concentrations of ozone in urban area. Rapid increase in vehicles number and fossil fuel consumption in Malaysia cause the emission of ozone and their precursors especially nitrogen oxides increasing sharply. This research focus on daytime and nighttime ozone concentration at Kuala Terengganu, Malaysia. The aim of this study is to predict ozone concentration using feed forward back propagation neural network (FFBP-NN) with two hidden layers. Five performance indicators were used to evaluate the models performances which are normalized absolute error (NAE), root mean squared error (RMSE), index of agreement (IA), prediction accuracy (PA) and coefficient of determination (R2). Result show that FFBP-NN with 2 hidden layers model gives good performance for prediction of ozone concentration with high accuracy measures (IA=0.9551, PA=0.8453, R2=0.8402) and small error measures (NAE=0.1642, RMSE=4.4958) for daytime and nighttime (IA=0.9541, PA=0.8429, R2=0.8358, NAE=0.2160, RMSE=3.2485). The result from this study provides a reference for city council to improve the existing guidelines and to plan an effective mitigation measures to monitor the status of air quality towards a sustainable environment. Keywords: Prediction, Ozone, Feed Forward Back Propagation Neural Network, Two Hidden Layers, Performance Indicators.
Scope of the Article: Measurement & Performance Analysis.