A comparative study of different LSTM neural networks in predicting air pollutant concentrations

Abstract

Aim/Objective: This study aims to identify the key trends among different types of LSTM networks and their performance and usage for air pollutants (PM 2.5 and PM 10) concentrations prediction. Methods: In this study, the extensive research efforts were made for Particulate Matters (i.e., PM 10 and PM 2.5) prediction using several LSTM networks, namely Vanilla, Stacked, and Bidirectional. These are trained and tested using air quality data, retrieved from the Central Pollution Control Board (CPCB) of the town Bawana, Delhi. Real-time hourly a data from 2018 to 2020 with nine air pollutants are considered for experimental analysis. We conducted data preparation strategy to select the best features, which improve the quality of the data. An adequate number of experiments are conducted to choose the best hyperparameters using Python package TensorFlow. Findings: MSE, MAE, RMSE, and R 2 parameters are used as the statistical criteria for evaluating the model's performances. The numerical experiments revealed that deep neural networks could predict the Particulate Matters (µg/m 3) with high accuracy. We found that Stacked LSTM with minimum MSE, MAE, RMSE, and maximum R 2 works better than the other two methods, i.e., Vanilla LSTM and Bidirectional LSTM for PM 2.5 and PM 10 concentrations prediction. The empirical, experimental analysis also shows that Vanilla, Stacked, and Bidirectional LSTM models have comparatively minimum MSE, MAE, RMSE, and maximum R 2 for PM 2.5 than PM 10 concentration prediction. Applications: With the help of a predictive model, one can find reliable fine concentration prediction information for a particular area. The resultant information on relative performance can help researchers in the selection of an appropriate LSTM algorithm for their studies.