Details
Title
Air quality prediction using stacked bi- long short-term memory and convolutional neural network in IndiaJournal title
Archives of Environmental ProtectionYearbook
2024Volume
50Issue
4Authors
Affiliation
Karkuzhali S : Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India ; Puyalnithi Thendral : Department of Artificial Intelligence and Data Science,Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India ; Nirmalan R2 : Department of Artificial Intelligence and Data Science,Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IndiaKeywords
air quality; ; Bi-Long Short-Term Memory; ; Convolutional Neural Network; ; Adam Optimizer; ; training process; ; hybrid analysis; ; pollution;Divisions of PAS
Nauki TechniczneCoverage
9-21Publisher
Polish Academy of SciencesBibliography
- Akinosho, T. D., Oyedele, L. O., Bilal, M., Barrera-Animas, A. Y., Gbadamosi, A. Q. & Olawale, O. A. (2022). A scalable deep learning system for monitoring and forecasting pollutant concentration levels on UK highways. Ecological Informatics, 69, 101609. DOI:10.1016/j.ecoinf.2022.101609
- Al-Eidi, S., Amsaad, F., Darwish, O., Tashtoush, Y., Alqahtani, A. & Niveshitha, N. (2023). Comparative Analysis Study for Air Quality Prediction in Smart Cities Using Regression Techniques. IEEE Access. DOI:10.1109/ACCESS.2023.3280129
- Cao, Y., Zhang, D., Ding, S., Zhong, W. & Yan, C. (2023). A Hybrid Air Quality Prediction Model Based on Empirical Mode Decomposition. Tsinghua Science and Technology, 29(1), 99-111. DOI:10.26599/TST.2023.2200016
- Dobrzyniewski, D., Szulczyński, B., Rybarczyk, P. & Gębicki, J. (2023). Process control of air stream deodorization from vapors of VOCs using a gas sensor matrix conducted in the biotrickling filter (BTF). Archives of Environmental Protection, 49(2). DOI:10.24425/aep.2023.144733
- Drewil, G. I. & AlBahadili, R. J. (2022). Air pollution prediction using LSTM deep learning and metaheuristics algorithms. Measurement: Sensors, 24, 100546. DOI:10.1016/j.measen.2022.100546
- Fang, Z., Yang, H., Li, C., Cheng, L., Zhao, M. & Xie, C. (2021). Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR. Archives of Environmental Protection, 47(3). DOI:10.24425/aep.2021.138474
- Fu, L., Li, J. & Chen, Y. (2023). An innovative decision-making method for air quality monitoring based on big data-assisted artificial intelligence technique. Journal of Innovation & Knowledge, 8(2), 100294. DOI:10.1016/j.jik.2023.100294
- Godłowska, J., Kaszowski, K. & Kaszowski, W. (2022). Application of the FAPPS system based on the CALPUFF model in short-term air pollution forecasting in Krakow and Lesser PolandApplication of the FAPPS system based on the CALPUFF model in short-term air pollution forecasting in Krakow and Lesser Poland. Archives of Environmental Protection, 48(3). DOI:10.24425/aep.2022.142698
- Holnicki, P., Kałuszko, A. & Nahorski, Z. (2021). Analysis of emission abatement scenario to improve urban air quality. Archives of Environmental Protection, 47(2). DOI:10.24425/aep.2021.137281
- Iskandaryan, D., Ramos, F. & Trilles, S. (2023). A set of deep learning algorithms for air quality prediction applications. Software Impacts, 17, 100562. DOI:10.1016/j.simpa.2023.100562
- Iskandaryan, D., Ramos, F. & Trilles, S. (2023). Graph Neural Network for Air Quality Prediction: A Case Study in Madrid. IEEE Access, 11, 2729-2742. DOI:10.1109/ACCESS.2023.3244295
- Janarthanan, R., Partheeban, P., Somasundaram, K. & Elamparithi, P. N. (2021). A deep learning approach for prediction of air quality index in a metropolitan city. Sustainable Cities and Society, 67, 102720. DOI:10.1016/j.scs.2021.102720
- Jurado, X., Reiminger, N., Benmoussa, M., Vazquez, J. & Wemmert, C. (2022). Deep learning methods evaluation to predict air quality based on Computational Fluid Dynamics. Expert Systems with Applications, 203, 117294. DOI:10.1016/j.eswa.2022.117294
- Kanmani, P., Selvaraj, P. & Burugari, V. K. (2022). An energy efficient approach of deep learning based soft sensor for air quality management. Measurement: Sensors, 24, 100460. DOI:10.1016/j.measen.2022.100460
- Liu, B., Yan, S., Li, J., Qu, G., Li, Y., Lang, J. & Gu, R. (2019). A sequence-to-sequence air quality predictor based on the n-step recurrent prediction. IEEE Access, 7, 43331-43345. DOI:10.1109/ACCESS.2019.2903323
- Liu, C., Pan, G., Song, D. & Wei, H. (2023). Air Quality Index Forecasting Via Genetic Algorithm-Based Improved Extreme Learning Machine. IEEE Access. DOI:10.1109/ACCESS.2023.3273346
- Lu, T., Gu, C., Yuan, D., Zhang, K. & Shao, C. (2023). Deep learning model for displacement monitoring of super high arch dams based on measured temperature data. Measurement, 222, 113579. DOI:10.1016/j.measurement.2023.113579
- Matthaios, V. N., Knibbs, L. D., Kramer, L. J., Crilley, L. R. & Bloss, W. J. (2023). Predicting real-time within-vehicle air pollution exposure with mass-balance and machine learning approaches using on-road and air quality data. Atmospheric Environment, 120233. DOI:10.1016/j.atmosenv.2023.120233
- Prado-Rujas, I. I., García-Dopico, A., Serrano, E., Córdoba, M. L. & Pérez, M. S. (2024). A multivariable sensor-agnostic framework for spatio-temporal air quality forecasting based on Deep Learning. Engineering Applications of Artificial Intelligence, 127, 107271. DOI:10.1016/j.engappai.2023.107271
- Shao, Q., Chen, J. & Jiang, T. (2023). A novel coupled optimization prediction model for air quality. IEEE Access. DOI:10.1109/ACCESS.2023.3267475
- Shin, S., Baek, K. & So, H. (2023). Rapid monitoring of indoor air quality for efficient HVAC systems using fully convolutional network deep learning model. Building and Environment, 234, 110191. DOI:10.1016/j.buildenv.2023.110191
- Wang, X., Wang, M., Liu, X., Mao, Y., Chen, Y. & Dai, S. (2024). Surveillance-image-based outdoor air quality monitoring. Environmental Science and Ecotechnology, 18, 100319. DOI:10.1016/j.ese.2024.100319
- Wardana, I. N. K., Fahmy, S. A. & Gardner, J. W. (2023). TinyML Models for a Low-cost Air Quality Monitoring Device. IEEE Sensors Letters. DOI:10.1109/LSENS.2023.3247646
- Wood, D. A. (2022). Local integrated air quality predictions from meteorology (2015 to 2020) with machine and deep learning assisted by data mining. Sustainability Analytics and Modeling, 2, 100002. DOI:10.1016/j.susanm.2022.100002
- Yadav, N., Sorek-Hamer, M., Von Pohle, M., Asanjan, A. A., Sahasrabhojanee, A., Suel, E., Arku, R., Lingenfelter, V., Brauer, M., Ezzati, M. & Oza, N. (2023). Using Deep Transfer Learning and Satellite Imagery to Estimate Urban Air Quality in Data-Poor Regions. Environmental Pollution, 122914. DOI:10.1016/j.envpol.2023.122914
- Yang, Y., Mei, G. & Izzo, S. (2022). Revealing influence of meteorological conditions on air quality prediction using explainable deep learning. IEEE Access, 10, 50755-50773. DOI:10.1109/ACCESS.2022.3163935
- Yu, W., Nakisa, B., Ali, E., Loke, S. W., Stevanovic, S. & Guo, Y. (2023). Sensor-based indoor air temperature prediction using deep ensemble machine learning: An Australian urban environment case study. Urban Climate, 51, 101599. DOI:10.1016/j.uclim.2023.101599
- Zhang, B., Wang, Z., Lu, Y., Li, M. Z., Yang, R., Pan, J., & Kou, Z. (2023). Air pollutant diffusion trend prediction based on deep learning for targeted season—North China as an example. Expert Systems with Applications, 232, 120718. DOI:10.1016/j.eswa.2023.120718
- Zhang, Y., Wang, Y., Gao, M., Ma, Q., Zhao, J., Zhang, R., Wang, Q. & Huang, L. (2019). A predictive data feature exploration-based air quality prediction approach. IEEE Access, 7, 30732-30743. DOI:10.1109/ACCESS.2019.2903346
- Zwierzchowski, R. & Różycka-Wrońska, E. (2021). Operational determinants of gaseous air pollutants emissions from coal-fired district heating sources. Archives of Environmental Protection, 47(3). DOI:10.24425/aep.2021.138473
Date
16.12.2024Type
ArticlesIdentifier
DOI: 10.24425/aep.2024.152891DOI
10.24425/aep.2024.152891Abstracting & Indexing
Abstracting & Indexing
Archives of Environmental Protection is covered by the following services:
AGRICOLA (National Agricultural Library)
Arianta
Baidu
BazTech
BIOSIS Citation Index
CABI
CAS
DOAJ
EBSCO
Engineering Village
GeoRef
Google Scholar
Index Copernicus
Journal Citation Reports™
Journal TOCs
KESLI-NDSL
Naviga
ProQuest
SCOPUS
Reaxys
Ulrich's Periodicals Directory
WorldCat
Web of Science