Abstract
The ability to perform air quality is a crucial component of wisdom city concept. However, accurate and reliable air quality forecasting is still a serious issue due to the complexity factors of air pollutions to help improve air quality. This paper presents an air quality forecasting model with enhanced Elman neural network. The model employs filter approaches to preform feature selection and followed by an enhanced Elman network for the prediction task. The framework is evaluated with real-world air quality data collected from Chengdu city of China. The results show that the proposed model achieves better performance compared to other methods.
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Acknowledgments
This work is supported by the Science and Technology Department of Sichuan Province (Grant No. 2017JZ0027, 2018HH0075), Foundation of Guangdong Province, China (Grant No. 2017A030313380), The Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China, and the Fundamental Research Funds for the Central Universities (Grant No. ZYGX2015J063).
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Zhang, L., Xie, Y., Chen, A., Duan, G. (2019). A Forecasting Model Based on Enhanced Elman Neural Network for Air Quality Prediction. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_9
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DOI: https://doi.org/10.1007/978-981-13-1328-8_9
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