Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks

Abstract

The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.Index Terms-Air quality, air pollution, prediction, environment, deep learning, recurrent neural networks, long short term memory.

Machine Learning Based on Kernel Function Controlled Gaussian Process Regression Method for In-depth Extrapolative Analysis of Covid-19 Daily Cases Drift Rates

“…In [14,15,23,24], the authors explored the long short-term memory model (LSMM) to tackle the non-stationary long-term data with time series analysis. But the LSMM only predicts well when the data is large [25][26][27][28].…”

Air quality prediction using CT-LSTM

“…2. These three parts are the forget gate, the input gate and the output gate [44,45].C tÀ1 is the cell state at time t-1, h tÀ1 is the final output value of LSTM neural unit at time t-1, x t is the input at time t,r is the activation function of sigmoid, f t is the output of forget gate at time t, i t is the output of input gate at time t,C t is the candidate cell state at time t, and o t is the output of the output gate at time t, C t is the cell state at time t, and h t is the output at time t. LSTM network realizes the protection and control of information through such a structure. The detailed process of updating the LSTM neural unit is as follows:…”

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

“…Another interesting study was undertaken by Pardo and Malpica [ 26 ], who proposed different LSTM models to predict NO 2 levels for t + 8, t + 16 and t + 24 prediction horizons in Madrid (Spain). Finally, Rao et al [ 27 ] compared LSTM based recurrent neural networks and SVR applied to air quality prediction. The results showed how the LSTM approach obtained better forecasting performances than the remaining method employed for all the pollutants considered.…”