Air quality index prediction using multivariate deep neural networks: A case study of a proposed state capital in India

Venkata Siva Raja Prasad Sunku; Rambabu Mukkamala; Vishnu Namboodiri

doi:10.18502/japh.v8i3.13784

Venkata Siva Raja Prasad Sunku National Institute of Construction Management and Research (NICMAR), Hyderabad, India
Rambabu Mukkamala National Institute of Construction Management and Research (NICMAR), Hyderabad, India
Vishnu Namboodiri National Institute of Construction Management and Research (NICMAR), Hyderabad, India

DOI: https://doi.org/10.18502/japh.v8i3.13784

Keywords: Prediction; Air quality index (AQI); Pollutants; Deep neural networks (DNN)

Abstract

Introduction: Air pollution is a major environmental challenge worldwide and predicting air quality is key to regulating air pollution. The extent of air pollution is quantified by the Air Quality Index (AQI). Air quality forecasting has become an important area of research. Deep Neural Networks (DNN) are useful in predicting the AQI instead of traditional methods which involve numerous computations. The aim of this research paper is to investigate the use of the deep neural networks as a framework for predicting the air quality index based on time series data of pollutants.

Materials and methods: To resolve this problem, the study proposes a DNN to develop the best model for predicting the AQI. Long Short-Term Memory (LSTM) and Bi-directional LSTM have been introduced in the study to understand and predict the relationship between the pollutants affecting the AQI. The model’s performance is evaluated using the metrics, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient of determination (R²). To conduct the study, real-time hourly data for the period November 2017 to January 2020 from an air quality monitoring station was considered for the proposed capital region of the state of Andhra Pradesh in India.

Results: The multivariate modeling considers seven pollutants as independent variables and AQI as the target variable. After experimenting and training the algorithm on the dataset, Bi-directional LSTM was shown to have the lowest MAE and RMSE values and the highest R², indicating that it has the highest accuracy in AQI prediction.

Conclusion: The development of a capital city involves massive construction activity resulting in air pollution. The results are helpful to the authorities to monitor the quality of air of develop air quality management programs thus avoiding the impact of air pollution on health.