Integrative Deep Learning Forecasting of Air Quality Index in India: A Fusion of Bidirectional LSTM and Sensor Data
Authors: 
Bala Murugan Mudhanai Sanjeevirayar, Gopinath Mudhana, Manoj Kumar Rajagopal, Paventhan Arumugam, Vijayan K, Vijayakumar VaradarajanAuthors Info & Claims
Bala Murugan Mudhanai Sanjeevirayar, Gopinath Mudhana, Manoj Kumar Rajagopal, Paventhan Arumugam, Vijayan K, Vijayakumar VaradarajanAuthors Info & ClaimsPages 194 - 199
Abstract
AQI as a vital metric for evaluating pollution levels, which directly impacts the health and well-being of the population. We have devised a hybrid deep learning (DL) framework that combines the Bi-directional LSTM with a sensor fusion approach. Our integrated model combines the strengths of sensor fusion and Bi-LSTM, enhancing both spatial and temporal dependencies in the data. Additional training of the data from independent sensors prior to the AQI calculation and training of the proposed method provided much better AQI prediction capability due to the added information on the spatial variation of the data. Empirical validation with real-world data from the Chennai city in India, demonstrates superior accuracy, achieving a Root Mean Square Error (RMSE) of 21.7 for AQI prediction.
References
[1]
Xu, Hanqing, "Environmental pollution, a hidden culprit for health issues." Eco-Environment & Health 1.1 (2022): 31-45.
[2]
Schluger, Neil W., and Ram Koppaka. "Lung disease in a global context. A call for public health action." Annals of the American Thoracic Society 11.3 (2014): 407-416.
[3]
Kaur, Rajveer, and Puneeta Pandey. "Air pollution, climate change, and human health in Indian cities: a brief review." Frontiers in Sustainable Cities 3 (2021): 705131.
[4]
Sharma, Rohit, "Inferring air pollution from air quality index by different geographical areas: case study in India." Air Quality, Atmosphere & Health 12 (2019): 1347-1357.
[5]
Tan, Xiaorui, "A review of current air quality indexes and improvements under the multi-contaminant air pollution exposure." Journal of environmental management 279 (2021): 111681.
[6]
Castelli, Mauro, "A machine learning approach to predict air quality in California." Complexity 2020 (2020).
[7]
Turner, Michelle C., "Outdoor air pollution and cancer: An overview of the current evidence and public health recommendations." CA: a cancer journal for clinicians 70.6 (2020): 460-479.
[8]
Raes, Lieven, "DUET: A framework for building interoperable and trusted digital twins of smart cities." IEEE Internet Computing 26.3 (2021): 43-50.
[9]
Chen, Mei, and Michel Decary. "Artificial intelligence in healthcare: An essential guide for health leaders." Healthcare management forum. Vol. 33. No. 1. Sage CA: Los Angeles, CA: SAGE Publications, 2020.
[10]
Yasmin, Farhana, "AQIPred: A Hybrid Model for High Precision Time Specific Forecasting of Air Quality Index with Cluster Analysis." Human-Centric Intelligent Systems 3.3 (2023): 275-295.
[11]
Alahi, Md Eshrat E., "Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends." Sensors 23.11 (2023): 5206.
[12]
Montanaro, Teodoro, "An iot-aware solution to support governments in air pollution monitoring based on the combination of real-time data and citizen feedback." Sensors 22.3 (2022): 1000.
[13]
Pandya, Aum, "A Comparative and Systematic Study of Machine Learning (ML) Approaches for Particulate Matter (PM) Prediction." Archives of Computational Methods in Engineering (2023): 1-20.
[14]
Seddik, S., H. Routaib, and A. Elhaddadi. "Multi-variable time series decoding with Long Short-Term Memory and mixture attention." Acadlore Trans. Mach. Learn 2.3 (2023): 154-169.
[15]
Roy, Dilip Kumar, "Daily prediction and multi-step forward forecasting of reference evapotranspiration using LSTM and Bi-LSTM models." Agronomy 12.3 (2022): 594.
[16]
Reichstein, Markus, "Deep learning and process understanding for data-driven Earth system science." Nature 566.7743 (2019): 195-204.
[17]
Karavas, Zissis, Vayos Karayannis, and Konstantinos Moustakas. "Comparative study of air quality indices in the European Union towards adopting a common air quality index." Energy & Environment 32.6 (2021): 959-980.
[18]
Sarkar, Mohan, Anupam Das, and Sutapa Mukhopadhyay. "Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India." Environment, Development and Sustainability 23 (2021): 8613-8642.
[19]
Kingsy Grace, R., and S. Manju. "A comprehensive review of wireless sensor networks based air pollution monitoring systems." Wireless Personal Communications 108 (2019): 2499-2515.
[20]
Central Pollution Control Board, Ministry of Environment, Forest and Climate Change, Government of India. Air quality monitoring, emission inventory and source apportionment study for Indian cities—national summary report. New Delhi: CPCB. February, 2011. http://cpcb.nic.in/displaypdf.php?id=RmluYWxOYXRpb25hbFN1bW1hcnkucGRm (accessed September 19, 2023).
[21]
Weerakody, Philip B., "A review of irregular time series data handling with gated recurrent neural networks." Neurocomputing 441 (2021): 161-178.
[22]
Alkabbani, Hanin, "An improved air quality index machine learning-based forecasting with multivariate data imputation approach." Atmosphere 13.7 (2022): 1144.
[23]
Li, Hongmin, Jianzhou Wang, and Hufang Yang. "A novel dynamic ensemble air quality index forecasting system." Atmospheric Pollution Research 11.8 (2020): 1258-1270.
[24]
Jadhav, Anil, Dhanya Pramod, and Krishnan Ramanathan. "Comparison of performance of data imputation methods for numeric dataset." Applied Artificial Intelligence 33.10 (2019): 913-933.
[25]
Umar, Nura, and Alison Gray. "Comparing single and multiple imputation approaches for missing values in univariate and multivariate water level data." Water 15.8 (2023): 1519.
[26]
Manyol, Moïse, "Preprocessing Approach for Power Transformer Maintenance Data Mining Based on k-Nearest Neighbor Completion and Principal Component Analysis." International Transactions on Electrical Energy Systems 2022 (2022).
[27]
Masood, Adil, and Kafeel Ahmad. "A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance." Journal of Cleaner Production 322 (2021): 129072.
[28]
Song, Zhenyu, "A simple dendritic neural network model-based approach for daily PM2. 5 concentration prediction." Electronics 10.4 (2021): 373.
[29]
Chang, Yue-Shan, "An LSTM-based aggregated model for air pollution forecasting." Atmospheric Pollution Research 11.8 (2020): 1451-1463.
[30]
Balogun, Abdul-Lateef, "A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science." Urban Climate 40 (2021): 100989.
[31]
Wang, Zicheng, "Multi-scale deep learning and optimal combination ensemble approach for AQI forecasting using big data with meteorological conditions." Journal of Intelligent & Fuzzy Systems 40.3 (2021): 5483-5500.
[32]
Li, Yiman, "Research and application of an evolutionary deep learning model based on improved grey wolf optimization algorithm and DBN-ELM for AQI prediction." Sustainable Cities and Society 87 (2022): 104209.
[33]
Abirami, S., and P. Chitra. "Regional air quality forecasting using spatiotemporal deep learning." Journal of Cleaner Production 283 (2021): 125341.
[34]
Wu, Qunli, and Huaxing Lin. "Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network." Sustainable Cities and Society 50 (2019): 101657.
[35]
Yan, Rui, "Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering." Expert Systems with Applications 169 (2021): 114513.
[36]
Wu, Zekai, Wenqin Zhao, and Yaqiong Lv. "An ensemble LSTM-based AQI forecasting model with decomposition-reconstruction technique via CEEMDAN and fuzzy entropy." Air Quality, Atmosphere & Health 15.12 (2022): 2299-2311.
[37]
Ameer, Saba, "Comparative analysis of machine learning techniques for predicting air quality in smart cities." IEEE Access 7 (2019): 128325-128338.
[38]
Harishkumar, K. S., K. M. Yogesh, and Ibrahim Gad. "Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models." Procedia Computer Science 171 (2020): 2057-2066.
[39]
Xayasouk, Thanongsak, HwaMin Lee, and Giyeol Lee. "Air pollution prediction using long short-term memory (LSTM) and deep autoencoder (DAE) models." Sustainability 12.6 (2020): 2570.
[40]
Wu, Qunli, and Huaxing Lin. "Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network." Sustainable Cities and Society 50 (2019): 101657.
[41]
Yeong, De Jong, "Sensor and sensor fusion technology in autonomous vehicles: A review." Sensors 21.6 (2021): 2140.
[42]
Fayyad, Jamil, "Deep learning sensor fusion for autonomous vehicle perception and localization: A review." Sensors 20.15 (2020): 4220.
Recommendations
A Hybrid Deep Learning Approach for Forecasting Air Temperature
Artificial Neural Networks in Pattern RecognitionAbstractForecasting the weather is a great scientific challenge. Physics-based, numerical weather prediction (NWP) models have been developed for decades by large research teams and the accuracy of forecasts has been steadily increased. Yet, recently, ...
A novel optimization model based on fuzzy time series for short-term Air Quality Index forecasting
Highlights- This paper simplifies the steps of fuzzification and de-fuzzification by removing the process of determining the fuzzy grade.
- Data preprocessing technology is used to extract the main features of data and remove the negative effects of ...
AbstractThe problem of air quality forecasting has caused a heated discussion among scholars at home and abroad. Short term air quality forecasting substantially impacts regulatory effectiveness to improve environmental and human health. Therefore, many ...
Application of deep learning to multivariate aviation weather forecasting by long short-term memory
Weather forecasts are essential to aviation safety. Unreliable forecasts not only cause problems to pilots and air traffic controllers, but also lead to aviation accidents and incidents. This study develops a long short-term memory (LSTM) integrating both ...
Comments
Information & Contributors
Information
Published In
December 2023
266 pages
ISBN:9798400709043
DOI:10.1145/3638985
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 March 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIT 2023
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 4Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)1
Reflects downloads up to 18 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format