Title: Prediction of air pollution using LSTM-based recurrent neural networks

Authors: Akshat Jain; Ashim Bhasin; Varun Gupta

Addresses: CSE Department, Chandigarh College of Engineering and Technology (CCET), Chandigarh, India ' CSE Department, Chandigarh College of Engineering and Technology (CCET), Chandigarh, India ' CSE Department, Chandigarh College of Engineering and Technology (CCET), Chandigarh, India

Abstract: This paper proposes a system that predicts the pollution level at some hour at a place. It also infers about the various parameters associated with the increasing pollution across the globe, its ill effects and the future scenario of the same. An air quality dataset reporting level of pollution and weather every hour for five years is taken and long short-term memory (LSTM) network-based recurrent neural networks (RNNs) using Keras library with Tensorflow as back-end were applied in a python environment. The paper studies all 13 parameters affecting the weather and air pollution conditions and forecasts the pollution for an hour given the weather conditions and pollution value for the previous hour.

Keywords: air pollution prediction; long short-term memory; LSTM; recurrent neural networks; RNNs; artificial neural networks; deep learning.

DOI: 10.1504/IJCISTUDIES.2019.103620

International Journal of Computational Intelligence Studies, 2019 Vol.8 No.4, pp.299 - 308

Received: 07 Jan 2018
Accepted: 08 Oct 2018
Published online: 15 Nov 2019 *

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