A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction

Mohammed G. Ragab, Said J. Abdulkadir, Norshakirah Aziz, Qasem Al-Tashi, Yousif Alyousifi, Hitham Alhussian and Alawi Alqushaibi
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Mohammed G. Ragab: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
Said J. Abdulkadir: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
Norshakirah Aziz: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
Qasem Al-Tashi: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
Yousif Alyousifi: Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan, Bangi 43600, Malaysia
Hitham Alhussian: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia
Alawi Alqushaibi: Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak 32610, Malaysia

Sustainability, 2020, vol. 12, issue 23, 1-22

Abstract: Air pollution is one of the world’s most significant challenges. Predicting air pollution is critical for air quality research, as it affects public health. The Air Pollution Index (API) is a convenient tool to describe air quality. Air pollution predictions can provide accurate information on the future pollution situation, effectively controlling air pollution. Governments have expressed growing concern about air pollution due to its global effect on human health and sustainable growth. This paper proposes a novel forecasting model using One-Dimensional Deep Convolutional Neural Network (1D-CNN) and Exponential Adaptive Gradients (EAG) optimization to predict API for a selected location, Klang, a city in Malaysia. The proposed 1D-CNN–EAG exponentially accumulates past model gradients to adaptively tune the learning rate and converge in both convex and non-convex areas. We use hourly air pollution data over three years (January 2012 to December 2014) for training. Parameter optimization and model evaluation was accomplished by a grid-search with k-folds cross-validation. Results have confirmed that the proposed approach achieves better prediction accuracy than the benchmark models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and the Correlation Coefficient (R-Squared) with values of 2.036, 2.354, 4.214 and 0.966, respectively, and time complexity.

Keywords: air pollution index; artificial intelligence; deep learning; grid search; one dimensional convolutional neural networks; optimization; urban air pollution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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