Prediction of air pollution using LSTM-based recurrent neural networks
by Akshat Jain; Ashim Bhasin; Varun Gupta
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 8, No. 4, 2019

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.

Online publication date: Fri, 15-Nov-2019

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