Long Short Term Memory Neural Network-Based Model Construction and Fne-Tuning for Air Quality Parameters Prediction

1. World Health Assembly, 69. Health and the Environment: Draft Road Map for an Enhanced Global Response to the Adverse Health Effects of Air Pollution – Report by the Secretariat. – World Health Organization, 2016 (Online). https://apps.who.int/iris/handle/10665/252673 Search in Google Scholar

2. Hu, R., X. Xy, S. K. Xu, M. Wang, L. Jiang, R. Wen, W. Lai, L. Guan. PM2.5 Exposure Elicits Oxidative Stress Responses and Mitochondrial Apoptosis Pathway Activation in HaCaT Keratinocytes. – Chin. Med. J., Vol. 130, 2017, pp. 2205-2214.10.4103/0366-6999.212942559833328816208 Search in Google Scholar

3. Piao, M., H. YJ, K. Shilnikova, J. JW. Particular Matter 2.5 Damages Skin Cells by Inducing Oxidative Stress Subcellular Organelle Dysfunction and Apoptosis. – Arch. Toxicol, Vol. 92, 2018, pp. 2077-2.10.1007/s00204-018-2197-9600246829582092 Search in Google Scholar

4. Ching Chang, Yu, H. Yuan Lee, J. Long Huang, C. Hsun Chiu, C. Chen, C. Teng Wu. Risk Factors and Outcome Analysis in Children with Carbon Monoxide Poisonin – Pediatrics & Neonatology, Vol. 58, 2017, No 2, pp. 171-177.10.1016/j.pedneo.2016.03.00727502424 Search in Google Scholar

5. Kumar, S., S. Dixit, O. P. Murty. Fatal Carbon Monoxide Poisoning: A Lesson from a Retrospective Study at All India Institute of Medical Sciences, New Delhi. – Journal of Family Medicine and Primary Care, Vol. 6, 2017, No 4, pp. 791-794.10.4103/jfmpc.jfmpc_408_16584840029564265 Search in Google Scholar

6. He, H.-D., M. Li, W. L. Wang, Z.- Y. Wang, Y. Xue. Prediction of PM2.5 Concentration Based on the Similarity in Air Quality Monitoring Network – Building and Environment, Vol. 137, 2018, pp. 11-17.10.1016/j.buildenv.2018.03.058 Search in Google Scholar

7. Zhang, Y., Y. He, J. Zhu. Research on Forecasting Problem Based on Multiple Linear Regression Model PM2.5. – J. Anhui Sci. Technol. Univ., Vol. 30, 2016, No 3, pp. 92-97. Search in Google Scholar

8. Baker, K. R., K. M. Foley. A Nonlinear Regression Model Estimating Single Source Concentrations of Primary and Secondarily Formed PM2:5. – Atmos. Environ., Vol. 45, 2011, No 22, pp. 3758-3767.10.1016/j.atmosenv.2011.03.074 Search in Google Scholar

9. Wang, Z., Z. Long. PM2.5 Prediction Based on Neural Network. – In: Proc. of 11th Int. Conf. Intell. Comput. Technol. Automat. (ICICTA’18), Changsha, China, 2018, pp. 44-47.10.1109/ICICTA.2018.00018 Search in Google Scholar

10. Chuentawat, R., Y. Kan-Ngan. The Comparison of PM2:5 Forecasting Methods in the Form of Multivariate and Univariate Time Series Based on Support Vector Machine and Genetic Algorithm. – In: Proc. of 15th Int. Conf. Electr. Eng./Electron., Comput., Telecommun. Inf. Technol. (ECTI-CON’18), Chiang Rai, Thailand, 2018, pp. 572-575. Search in Google Scholar

11. Hoek, G., R. Beelen, K. Hoogh, D. Vienneau, J. Gulliver, P. Fischer, D. Briggs. A Review of Land-Use Regression Models to Assess Spatial Variation of Outdoor Air Pollution. – Atmos. Environ., Vol. 42, 2008, pp. 7561-7578.10.1016/j.atmosenv.2008.05.057 Search in Google Scholar

12. Kaburlasos, V. G., I. N. Athanasiadis, P. A. Mitkas. Fuzzy Lattice Reasoning (FLR) Classifier and Its Application for Ambient Ozone Estimation. – International Journal of Approximate Reasoning, Vol. 45, 2007, No 1, pp. 152-188.10.1016/j.ijar.2006.08.001 Search in Google Scholar

13. Zhang, S., X. Li, Y. Li, J. Mei. Prediction of Urban PM2.5 Concentration Based on Wavelet Neural Network. – In: Proc. of 2018 Chinese Control and Decision Conference (CCDC’18), Shenyang, 2018, pp. 5514-5519.10.1109/CCDC.2018.8408092 Search in Google Scholar

14. Salman, G., Y. Heryadi, E. Abdurahman, W. Suparta. Single Layer & Multi-Layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting. – Procedia Comput. Sci., Vol. 135, 2018, pp. 89-98.10.1016/j.procs.2018.08.153 Search in Google Scholar

15. Tsai, Y., Y. Zeng, Y. Chang. Air Pollution Forecasting Using RNN with LSTM. – In: Proc. of IEEE 16th Int. Conf. Dependable, Autonomic Secure Comput., 16th Int. Conf. Pervasive Intell. Comput., 4th Int. Conf. Big Data Intell. Comput. Cyber. Sci. Technol. Congr., Athens, Greece, 2018, pp.1074-1079.10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00178 Search in Google Scholar

16. Kong, W., Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, Y. Zhang. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. – IEEE Trans. Smart Grid, Vol. 10, 2017, No 1, pp. 841-851.10.1109/TSG.2017.2753802 Search in Google Scholar

17. 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. 486-492.10.1016/j.chemosphere.2018.12.12830594800 Search in Google Scholar

18. Kim, S., J. M. Lee, J. Lee, J. Seo. Deep-Dust: Predicting Concentrations of Fine Dust in Seoul Using LSTM. – ArXiv, Vol. abs/1901.10106, 2019. Search in Google Scholar

19. Qin, D., J. Yu, G. Zou, R. Yong, Q. Zhao, B. Zhang. A Novel Combined Prediction Scheme Based on CNN and LSTM for Urban PM2.5 Concentration. – IEEE Access, Vol. 7, 2019, pp. 20050-20059.10.1109/ACCESS.2019.2897028 Search in Google Scholar

20. Xayasouk, T., H. Lee, G. Lee. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep AutoEncoder (DAE) Models. – Sustainability, Vol. 12, 2020, No 6.10.3390/su12062570 Search in Google Scholar

21. Tao, Q., F. Liu, Y. Li, D. Sidorov. Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU. – IEEE Access, Vol. 7, 2019, pp. 76690-76698.10.1109/ACCESS.2019.2921578 Search in Google Scholar

22. Verma, I., R. Ahuja, H. Meisheri, L. Dey. Air Pollutant Severity Prediction Using Bi-Directional LSTM Network. – In: Proc. of IEEE/WIC/ACM International Conference on Web Intelligence (WI), Santiago, 2018, pp. 651-654.10.1109/WI.2018.00-19 Search in Google Scholar

23. Tong, W., L. Li, X. Zhou, et al. Deep Learning PM2.5 Concentrations with Bidirectional LSTM RNN. – Air Qual Atmos Health, Vol. 12, 2019, pp. 411-423.10.1007/s11869-018-0647-4 Search in Google Scholar

24. Bengio, Y., P. Simard, P. Frasconi. Learning Long-Term Dependencies with Gradient Descent is Difficult. – IEEE Trans. Neural Netw., Vol. 5, 1994, No 2, pp. 157-166.10.1109/72.27918118267787 Search in Google Scholar

25. Pascanu, R., T. Mikolv, Y. Bengio. On the Difficulty of Training Recurrent Neural Networks. – In: Proc. of 30th International Conference on Machine Learning, Atlanta, Georgia, USA, Vol. 28, 2013, pp. III-1310-III-1318. Search in Google Scholar

26. Hochreiter, S., J. Schmidhuber. Long Short-Term Memory. – Neural Comput., Vol. 9, 1997, No 8, pp. 1735-1780.10.1162/neco.1997.9.8.17359377276 Search in Google Scholar

27. Schuster, M., K. K. Paliwal. Bidirectional Recurrent Neural Network – IEEE Transactions on Signal Processing, Vol. 45, 1997, No 11, pp. 2673-2681.10.1109/78.650093 Search in Google Scholar

28. Graves, A., J. Schmidhuber. Frame Wise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures. – Neural Networks, Vol. 18, 2005, No 5, pp. 602-610.10.1016/j.neunet.2005.06.04216112549 Search in Google Scholar

29. Graves, A., N. Jaitly, A. Mohamed. Hybrid Speech Recognition with Deep Bidirectional Lstm. – In: Proc. of 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU’13), Olomouc, Czech Republic, IEEE, 2013, pp. 273-278.10.1109/ASRU.2013.6707742 Search in Google Scholar

30. LeCun, Y., Y. Bengio, G. Hinton. Deep Learning. – Nature, Vol. 521, 2015, No 7553, pp. 436-444.10.1038/nature1453926017442 Search in Google Scholar

31. Hermans, M., S. Benjamin. Training and Analysing Deep Recurrent Neural Networks. – In: Advances in Neural Information Processing Systems, Vol. 26, Nevada, USA, 2013, pp. 190-198. Search in Google Scholar

32. Pascanu, R., C. Gulcehre, K. Cho, Y. Bengio. How to Construct Deep Recurrent Neural Networks. – In: Proc. of Second International Conference on Learning Representations, ICLR, Banff, Canada, 2014. Search in Google Scholar

33. Choi, H., K. Cho, Y. Bengio. Fine-Grained Attention Mechanism for Neural Machine Translation. – Neurocomputing, Vol. 284, 2018, pp. 171-176.10.1016/j.neucom.2018.01.007 Search in Google Scholar

34. Bahdanau, D., K. Cho, Y. Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. – In: Proc. of 3rd International Conference on Learning Representations (ICLR’15), San Diego, CA, USA, 2015, pp. 7-9. Search in Google Scholar

35. Li, W., D. Guo, X. Fang. Multimodal Architecture for Video Captioning with Memory Networks and an Attention Mechanism. – Pattern Recognit. Lett., Vol. 105, 2018, pp. 23-29.10.1016/j.patrec.2017.10.012 Search in Google Scholar

36. 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. 122-140.10.2478/cait-2020-0021 Search in Google Scholar

37. Aungiers., J. Time Series Prediction Using LSTM Deep Neural Network. – Altum Intelligence, 2018 (Online). https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks Search in Google Scholar

38. Kingma, D. P., J. Ba. Adam: A Method for Stochastic Optimization. – CoRR, Vol. abs/1412.6980, 2015 (Online). https://arxiv.org/abs/1412.6980 Search in Google Scholar

39. Srivastava, N., G. Hinton, A. Krizhevsky, S. Ilya, R. Salakhutdinov. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. – Journal of Machine Learning Research, Vol. 15, 2014, pp. 1929-1958. Search in Google Scholar

40. Pereyra, G., G. Tucker, J. Chorowski, L. Kaiser, G. E. Hinton. Regularizing Neural Networks by Penalizing Confident Output Distributions. – CoRR, Vol. abs/1701.06548, 2017. Search in Google Scholar

41. Chang, Y.-S., H.-T. Chiao, S. Abimannan, Y.-P. Huang, Y.-T. Tsai, K.-M. Lin. An LSTM-Based Aggregated Model for Air Pollution Forecasting. – Atmospheric Pollution Research, Vol. 11, 2020, No 8, pp. 1451-1463.10.1016/j.apr.2020.05.015 Search in Google Scholar

42. Kaya, K., Ş. Gündüz Öğüdücü. Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting. – Sci. Rep., 10: 3346, 2020.10.1038/s41598-020-60102-6704233432098977 Search in Google Scholar

43. Gul, S., G. M. Khan. Forecasting Hazard Level of Air Pollutants Using LSTM’s. – In: Proc. of IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI’20), IFIP Advances in Information and Communication Technology, Vol. 584, 2020, pp. 143-153.10.1007/978-3-030-49186-4_13 Search in Google Scholar

44. Ma, J., Z. Li, C. P. J. Cheng, Y. Ding, C. Lin, Z. Xu. Air Quality Prediction at New Stations Using Spatially Transferred Bi-Directional Long Short-Term Memory Network. – Science of the Total Environment, Vol. 705, 2020, 135771.10.1016/j.scitotenv.2019.13577131972931 Search in Google Scholar