Bala Murugan Mudhanai Sanjeevirayar
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.
- Xu, Hanqing, "Environmental pollution, a hidden culprit for health issues." Eco-Environment & Health 1.1 (2022): 31-45.Google Scholar
Cross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Castelli, Mauro, "A machine learning approach to predict air quality in California." Complexity 2020 (2020).Google Scholar
- 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.Google Scholar
- Raes, Lieven, "DUET: A framework for building interoperable and trusted digital twins of smart cities." IEEE Internet Computing 26.3 (2021): 43-50.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarCross Ref
- Roy, Dilip Kumar, "Daily prediction and multi-step forward forecasting of reference evapotranspiration using LSTM and Bi-LSTM models." Agronomy 12.3 (2022): 594.Google ScholarCross Ref
- Reichstein, Markus, "Deep learning and process understanding for data-driven Earth system science." Nature 566.7743 (2019): 195-204.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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).Google Scholar
- Weerakody, Philip B., "A review of irregular time series data handling with gated recurrent neural networks." Neurocomputing 441 (2021): 161-178.Google ScholarCross Ref
- Alkabbani, Hanin, "An improved air quality index machine learning-based forecasting with multivariate data imputation approach." Atmosphere 13.7 (2022): 1144.Google ScholarCross Ref
- Li, Hongmin, Jianzhou Wang, and Hufang Yang. "A novel dynamic ensemble air quality index forecasting system." Atmospheric Pollution Research 11.8 (2020): 1258-1270.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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).Google Scholar
- 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.Google ScholarCross Ref
- Song, Zhenyu, "A simple dendritic neural network model-based approach for daily PM2. 5 concentration prediction." Electronics 10.4 (2021): 373.Google ScholarCross Ref
- Chang, Yue-Shan, "An LSTM-based aggregated model for air pollution forecasting." Atmospheric Pollution Research 11.8 (2020): 1451-1463.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Abirami, S., and P. Chitra. "Regional air quality forecasting using spatiotemporal deep learning." Journal of Cleaner Production 283 (2021): 125341.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Ameer, Saba, "Comparative analysis of machine learning techniques for predicting air quality in smart cities." IEEE Access 7 (2019): 128325-128338.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Yeong, De Jong, "Sensor and sensor fusion technology in autonomous vehicles: A review." Sensors 21.6 (2021): 2140.Google ScholarCross Ref
- Fayyad, Jamil, "Deep learning sensor fusion for autonomous vehicle perception and localization: A review." Sensors 20.15 (2020): 4220.Google ScholarCross Ref
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, ...
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 ...
Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer
Highlight- A novel deep learning-based machine learning model is proposed for weekly evapotranspiration forecasting.
AbstractReference evapotranspiration (ETo) is one of the most important and influential components in optimizing agricultural water consumption and water resources management. In the present study, an efficient deep learning model, Auto Encode ...
Comments