Forecasting Air Quality with Deep Learning
Keywords:
Air Pollution, Air Quality Index, Deep Learning, PredictionAbstract
Due to factors such as increased urbanization, growing populations, transportation, home activities, agricultural methods, and industrial processes, Air pollution has emerged as a major issue in the past several years. It is linked to several illnesses and has emerged as a substantial issue in several urban areas, particularly in developing nations such as India. As part of our research, we make use of the Air Quality Index (AQI) for assess the quality of the air in Mumbai, India. Our emphasis is on evaluating 13 different pollutants and 7 meteorological indicators for the period from July 2017 to September 2022 to be able to predict air pollution levels. We employed three deep learning models: LSTM, Bi-LSTM, and CNN-Bi-LSTM. Results show that the CNN-Bi-LSTM model had better accuracy compared to earlier models, as proven by a MAE of 0.45, a MSE of 0.58, a RMSE of 0.60, and RMSLE of 0.36. This study shows that deep learning model are effective in forecasting AQI and by using historic data and deep learning algorithms enable precise forecasts of urban air quality levels worldwide.
Downloads
References
Mohite, J., Sawant, S., Pandit, A., Pappula, S, "Impact of lockdown and crop stubble burning on air quality of India: a case study from wheat-growing region" Environ- mental Monitoring and Assessment, 194(2), 77. (2022).
Ghosh, Buddhadev, Barman, H. C., Padhy, P. K. "Analysis of spatiotemporal distribution of air quality index (AQI) in the state of West Bengal, India from 2016 to 2021". Discover Atmosphere, 1(1), 1. (2023).
Leong, W., Kelani, R., Ahmad, Z, "Prediction of air pollution index (API) using support vector machine (SVM). " Journal of Environmental Chemical Engineering, 8(3), 103208. (2020).
Ejaz, A., Malik, A. O., Lopez-Candales, A, "Ambient air pollution and cardiovascular disease: learnt from the COVID-19 pandemic." Postgraduate Medicine, 133(6), 587–588, (2021).
He, G., Pan, Y., Tanaka, T. "The short-term impacts of COVID-19 lockdown on urban air pollution in China". Nature sustainability, 3(12), 1005–1011 (2020).
Manisalidis, I., Stavropoulou, E., Stavropoulos, A., Bezirtzoglou, E. "Environmental and health impacts of air pollution: a review. "Frontiers in public health, 8-14 (2020).
Al-Janabi, Samaher, Mustafa Mohammad, and Ali Al-Sultan. "A new method for prediction of air pollution based on intelligent computation." Soft Computing 24.1 (2020): 661-680.
Cabaneros, Sheen Mclean, John Kaiser Calautit, and Ben Richard Hughes. "A review of artificial neural network models for ambient air pollution prediction." Environmental Modelling & Software 119 (2019): 285-304.
Saravanan, D., and K. Santhosh Kumar. "Improving air pollution detection accuracy and quality monitoring based on bidirectional RNN and the Internet of Things." Materials Today: Proceedings 81 (2023): 791-796.
Mao, Wenjing, et al. "A hybrid integrated deep learning model for predicting various air pollutants." GIScience & Remote Sensing 58.8 (2021): 1395-1412.
Maleki, Heidar, et al. "Air pollution prediction by using an artificial neural network model." Clean technologies and environmental policy 21 (2019): 1341-1352.
Castelli, Mauro, et al. "A machine learning approach to predict air quality in California." Complexity 2020 (2020).
Ge, Liang, et al. "Multi-scale spatiotemporal graph convolution network for air quality prediction." Applied Intelligence 51 (2021): 3491-3505.
Dun, Ao, Yuning Yang, and Fei Lei. "A novel hybrid model based on spatiotemporal correlation for air quality prediction." Mobile Information Systems 2022 (2022).
Shahzad, Farrukh. "Does weather influence investor behavior, stock returns, and volatility? Evidence from the Greater China region." Physica A: Statistical Mechanics and its Applications 523 (2019): 525-543.
Wang, Jingyang, et al. "Air quality prediction using CT-LSTM." Neural Computing and Applications 33 (2021): 4779-4792.
Muzaffar, Shahzad, and Afshin Afshari. "Short-term load forecasts using LSTM networks." Energy Procedia 158 (2019): 2922-2927.
Zhang, Bo, et al. "Constructing a PM2. 5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks." Environmental Modelling & Software 124 (2020): 104600.
Kavianpour, Parisa, et al. "A CNN-BiLSTM model with attention mechanism for earthquake prediction." The Journal of Supercomputing 79.17 (2023): 19194-19226.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
