Air Quality Prediction Using Recurrent Neural Network

Shewta Borse, Dipak V. Patil

Abstract


Pollution effect on humanoid fitness and the environment. Forecast air quality by using machine learning. We developed a method to forecast the Air Quality Index on prior time information about pollution. We develop a model using machine learning methods such as Simple-RNN, Simple-LSTM, and Stack-LSTM. In this paper, we concentrate on Simple-RNN, Simple-LSTM and Stack-LSTM. In this experiment, we observed that Stack-LSTM give better result as compared to Simple-LSTM and simple-RNN.

References


Choubin, B., Abdolshahnejad, M., Moradi, E., Querol, X., Mosavi, A.,

Shamshirband, S., & Ghamisi, P. (2019). Spatial hazard assessment of the

PM10 using machine learning models in Barcelona, Spain. Science of The

Total Environment, 134474. doi:10.1016/j. scitotenv. 2019. 134474.

an, J., Wu, L., Ma, X., Zhou, H., & Zhang, F. (2019). Hybrid support

vector machines with heuristic algorithms for prediction of daily diffuse

solar radiation in air-polluted regions. Renewable Energy. doi:10.1016/j.

Rene ne. 2019. 07. 104.

Y. C. Lin, S. J. Lee, C. S. Ouyang, Chih-Hung Wu, Air quality prediction

by neural-fuzzy modeling approach, Applied Soft Computing Journal

(2019) 105898, https://doi.org/10.1016/j.asoc.2019.105898.

Ding., Y., Li, Z., Zhang., C., and Ma., J., (2019)., Prediction of

Ambient PM2.5 Concentrations Using a Correlation Filtered SpatialTemporal Long Short-Term Memory Model., Applied Sciences, 10(1).,

doi:10.3390/app10010014.

Zhang, D., and Woo, S. S. (2019). Predicting Air Quality using Moving Sensors. Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services - MobiSys ’19.

doi:10.1145/3307334.3328647.

Tian, Y., Huang, W., Ye, B., & Yang, M. (2019). A New Air Quality

Prediction Framework for Airports Developed with a Hybrid Supervised

Learning Method. Discrete Dynamics in Nature and Society, 2019, 1–13.

doi:10.1155/2019/1562537.

Kuo, R. J., Prasetyo, B., & Wibowo, B. S. (2019). Deep LearningBased Approach for Air Quality Forecasting by Using Recurrent Neural

Network with Gaussian Process in Taiwan. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA).

doi:10.1109/iea.2019.8715113.

S. Shanthi and M. Pyingkodi, Air Quality Index Prediction using Machine

Learning Algorithms, International Journal of Recent Technology and

Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November

, DOI:10.35940/ijrte.D5326.118419.

Zhu., D., Cai., C., Yang., T., and Zhou., X., (2018)., A Machine

Learning Approach for Air Quality Prediction- Model Regularization and Optimization., Big Data and Cognitive Computing, 2(1), 5.

doi:10.3390/bdcc2010005.

Kok, I., Simsek, M. U., & Ozdemir, S. (2017). A deep learning model for

air quality prediction in smart cities. 2017 IEEE International Conference

on Big Data (Big Data). doi:10.1109/bigdata.2017.8258144.

Tsai., Y.,-T., Zeng., Y.,-R., and Chang, Y.,-S., Air Pollution Forecasting

Using RNN with LSTM. 2018 IEEE 16th International Conference

on Dependable. Autonomic and Secure Computing., 16th International

Conference on Pervasive Intelligence and Computing. 4th International

Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech).

doi:10.1109/dasc/picom/Datacom/cyberscitec.2018.00178.

Freeman B S , Taylor, G., Gharabaghi, B., and J The. (2018).

Forecasting air quality time series using deep learning. Journal of

the Air and Waste Management Association, 68(8), 866 – 886. doi10.1080/10962247.2018.1459956.

V, A., P, G., R, V., & K P, S. (2018). Deep Air Net: Applying Recurrent

Networks for Air Quality Prediction. Procedia Computer Science, 132,

–1403. doi: 10.1016/j.procs.2018.05.068.


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