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Spatial and Temporal Variations on Air Quality Prediction Using Deep Learning Techniques

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Lin, B., J. Zhu. Changes in Urban Air Quality during Urbanization in China. – J. Clean. Prod., Vol. 188, 2018, pp. 312-321. Search in Google Scholar

Li, L., et al. Evaluation of Future Energy Consumption on PM2.5 Emissions and Public Health Economic Loss in Beijing. – J. Clean. Prod., Vol. 187, 2018, pp. 1115-1128. Search in Google Scholar

Li, N., et al. Potential Impacts of Electric Vehicles on Air Quality in Taiwan. – Sci. Total Environ., Vol. 566-567, 2016, pp. 919-928. Search in Google Scholar

Wang, Y., M. Sun, X. Yang, X. Yuan. Public Awareness and Willingness to Pay for Tackling Smog Pollution in China: A Case Study. – J. Clean. Prod., Vol. 112, 2016., pp. 1627-1634 Search in Google Scholar

Kurt, A., B. Gulbagci, F. Karaca, O. Alagha. An Online Air Pollution Forecasting System Using Neural Networks. – Environ. Int., Vol. 34, 2008, No 5, pp. 592-598. Search in Google Scholar

Moisan, S., R. Herrera, A. Clements. A Dynamic Multiple Equation Approach for Forecasting PM2.5 Pollution in Santiago, Chile. – Int. J. Forecast., Vol. 34, 2018, No 4, pp. 566-581. Search in Google Scholar

Jiang, P., R. Li, K. Zhang. Two Combined Forecasting Models Based on Singular Spectrum Analysis and Intelligent Optimized Algorithm for Short-Term Wind Speed. – Neural Comput. Appl., Vol. 30, 2018, No 1. Search in Google Scholar

Feng, Y., W. Zhang, D. Sun, L. Zhang. Ozone Concentration Forecast Method Based on Genetic Algorithm Optimized Back Propagation Neural Networks and Support Vector Machine Data Classification. – Atmos. Environ., Vol. 45, 2011, No 11, pp. 1979-1985. Search in Google Scholar

Paschalidou, A. K., S. Karakitsios, S. Kleanthous, P. A. Kassomenos. Forecasting Hourly PM10 Concentration in Cyprus through Artificial Neural Networks and Multiple Regression Models: Implications to Local Environmental Management. – Environ. Sci. Pollut. Res., Vol. 18, 2011, No 2, pp. 316-327. Search in Google Scholar

Antanasijević, D. Z., M. D. Ristić, A. A. Perić-Grujić, V. V. Pocajt. Forecasting Human Exposure to PM10 at the National Level Using an Artificial Neural Network Approach. – J. Chemom., Vol. 27, 2013, No 6, pp. 170-177. Search in Google Scholar

Wu, S., Q. Feng, Y. Du, X. Li. Artificial Neural Network Models for Daily PM10 Air Pollution Index Prediction in the Urban Area of Wuhan, China. – Environ. Eng. Sci., Vol. 28, 2011, No 5, pp. 357-363. Search in Google Scholar

Pai, T. Y., K. Hanaki, H. C. Su, L. F. Yu. A 24-h Forecast of Oxidant Concentration in Tokyo Using Neural Network and Fuzzy Learning Approach. – Clean – Soil, Air, Water, Vol. 41, 2013, No 8, pp. 729-736. Search in Google Scholar

Brabhukumr, A., P. Malhi, K. Ravindra, P. V. M. Lakshmi. Exposure to Household Air Pollution during First 3 Years of Life and IQ Level Among 6-8-Year-Old Children in India – A Cross-Sectional Study. – Sci. Total Environ., Vol. 709, 2020, p. 135110. Search in Google Scholar

Balakrishnan, K., et al. The Impact of Air Pollution on Deaths, Disease Burden, and Life Expectancy across the States of India: The Global Burden of Disease Study 2017. – Lancet Planet. Heal., Vol. 3, 2019, No 1, pp. e26-e39. Search in Google Scholar

Pandey, V., E. Oksanen, N. Singh, C. Sharma. Impacts of Air Pollution and Climate Change on Plants: Implications for India. 1st Ed. Vol. 13. Elsevier Ltd., 2013. Search in Google Scholar

Saha, D. C., P. K. Padhy. Effect of Air and Noise Pollution on Species Diversity and Population Density of Forest Birds at Lalpahari, West Bengal, India. – Sci. Total Environ., Vol. 409, 2011, No 24, pp. 5328-5336. Search in Google Scholar

Barot, V., V. Kapadia, S. Pandya. QoS Enabled IoT Based Low Cost Air Quality Monitoring System with Power Consumption Optimization. – Cybernetics and Information Technologies, Vol. 20, 2020, No 2, pp. 122-140. Search in Google Scholar

Gocheva-Ilieva, S. G., A. V. Ivanov, I. E. Livieris. High Performance Machine Learning Models of Large Scale Air Pollution Data in Urban Area. – Cybernetics and Information Technologies, Vol. 20, 2020, No 6, pp. 49-60. Search in Google Scholar

Sharma, N., S. Taneja, V. Sagar, A. Bhatt. Forecasting Air Pollution Load in Delhi Using Data Analysis Tools. – Procedia Comput. Sci., Vol. 132, 2018, pp. 1077-1085. Search in Google Scholar

Gupta, P., S. A. Christopher. Particulate Matter Air Quality Assessment Using Integrated Surface, Satellite, and Meteorological Products: Multiple Regression Approach. – J. Geophys. Res. Atmos., Vol. 114, 2009, No 14, pp. 1-13. Search in Google Scholar

Wang, P., H. Zhang, Z. Qin, G. Zhang. A Novel Hybrid-Garch Model Based on ARIMA and SVM for PM2.5 Concentrations Forecasting. – Atmos. Pollut. Res., Vol. 8, 2017, No 5, pp. 850-860. Search in Google Scholar

Ni, X. Y., H. Huang, W. P. Du. Relevance Analysis and Short-Term Prediction of PM2.5 Concentrations in Beijing Based on Multi-Source Data. – Atmos. Environ., Vol. 150, 2017, No February 2017, pp. 146-161. Search in Google Scholar

Gardner, M. W., S. R. Dorling. Artificial Neural Networks (the Multilayer Perceptron) – A Review of Applications in the Atmospheric Sciences. – Atmos. Environ., Vol. 32, 1998, No 14-15, pp. 2627-2636. Search in Google Scholar

Grivas, G., A. Chaloulakou. Artificial Neural Network Models for Prediction of PM10 Hourly Concentrations, in the Greater Area of Athens, Greece. – Atmos. Environ., Vol. 40, 2006, No 7, pp. 1216-1229. Search in Google Scholar

Iglesias-Otero, M. A., M. Fernández-González, D. Rodríguez-Caride, G. Astray, J. C. Mejuto, F. J. Rodríguez-Rajo. A Model to Forecast the Risk Periods of Plantago Pollen Allergy by Using the ANN Methodology. – Aerobiologia (Bologna)., Vol. 31, 2015, No 2, pp. 201-211. Search in Google Scholar

Li, Y., P. Jiang, Q. She, G. Lin. Research on Air Pollutant Concentration Prediction Method Based on Self-Adaptive Neuro-Fuzzy Weighted Extreme Learning Machine. – Environ. Pollut., Vol. 241, 2018, pp. 1115-1127. Search in Google Scholar

Alimissis, A., K. Philippopoulos, C. G. Tzanis, D. Deligiorgi. Spatial Estimation of Urban Air Pollution with the Use of Artificial Neural Network Models. – Atmos. Environ., Vol. 191, 2018, pp. 205-213. Search in Google Scholar

Yang, Z., J. Wang. A New Air Quality Monitoring and Early Warning System: Air Quality Assessment and Air Pollutant Concentration Prediction. – Environ. Res., Vol. 158, 2017, No May, pp. 105-117. Search in Google Scholar

Niska, H., T. Hiltunen, A. Karppinen, J. Ruuskanen, M. Kolehmainen. Evolving the Neural Network Model for Forecasting Air Pollution Time Series. – Eng. Appl. Artif. Intell., Vol. 17, 2004, No 2, pp. 159-167. Search in Google Scholar

Fu, R., Z. Zhang, L. Li. Using LSTM and GRU Neural Network Methods for Traffic Flow Prediction. – In: Proc. of 31st Youth Acad. Annu. Conf. Chinese Assoc. Autom. (YAC’16), No December, 2017, pp. 324-328. Search in Google Scholar

Shi, X., Z. Chen, H. Wang. Convolutional LSTM Network. – Nips, 2015, pp. 2-3. Search in Google Scholar

Zhao, J., F. Deng, Y. Cai, J. Chen. Long Short-Term Memory – Fully Connected (LSTM-FC) Neural Network for PM2.5 Concentration Prediction. – Chemosphere, Vol. 220, 2019, pp. 486-492. Search in Google Scholar

Tong, W., L. Li, X. Zhou, A. Hamilton, K. Zhang. Deep Learning PM2.5 Concentrations with Bidirectional LSTM RNN. – Air Qual. Atmos. Heal., Vol. 12, 2019, No 4, pp. 411-423. Search in Google Scholar

Qi, Z., T. Wang, G. Song, W. Hu, X. Li, Z. Zhang. Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality. – IEEE Trans. Knowl. Data Eng., Vol. 30, 2018, No 12, pp. 2285-2297. Search in Google Scholar

Xu, C., L. Xie, X. Xiao. A Bidirectional LSTM Approach with Word Embeddings for Sentence Boundary Detection. – J. Signal Process. Syst., Vol. 90, 2018, No 7, pp. 1063-1075. Search in Google Scholar

Lin, B. Y., F. Xu, Z. Luo, K. Zhu. Multi-Channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media. – In: Proc. of 3rd Workshop on Noisy User-Generated Text, September 2017, pp. 160-165. Search in Google Scholar

Barot, V., V. Kapadia. Long Short Term Memory Neural Network-Based Model Construction and Fne-Tuning for Air Quality Parameters Prediction. – Cybernetics and Information Technologies, Vol. 22, 2022, No 1, pp. 171-189. Search in Google Scholar

Ikram, S. T., A. K. Cherukuri, B. Poorva, P. S. Ushasree, Y. Zhang, X. Liu, G. Li. Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models. – Cybernetics and Information Technologies, Vol. 21, 2021, No 3, pp. 175-188. Search in Google Scholar

Hochreiter, S., J. UrgenSchmidhuber. Long Shortterm Memory. – Neural Comput., Vol. 9, 1997, No 8, 17351780. Search in Google Scholar

Graves, A., J. Schmidhuber. Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures. – Neural Networks, Vol. 18, 2005, No 5-6, pp. 602-610. Search in Google Scholar

Zhang, B., H. Zhang, G. Zhao, J. Lian. Constructing a PM2.5 Concentration Prediction Model by Combining Auto-Encoder with Bi-LSTM Neural Networks. – Environmental Modelling & Software, Vol. 124, 2020, p. 104600. Search in Google Scholar

Tobler, A. W. R. Clark University. – Science (80), Vol. 13, 1889, No 332, pp. 462-465. Search in Google Scholar

eISSN:
1314-4081
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Computer Sciences, Information Technology