Big data analysis model for water property forecasting

Xuejiao Li; Zhiwei Cheng; Wei Wang; Yongsheng Deng

doi:10.1117/12.2671219

28 April 2023 Big data analysis model for water property forecasting

Xuejiao Li, Zhiwei Cheng, Wei Wang, Yongsheng Deng

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126102G (2023) https://doi.org/10.1117/12.2671219
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Water property forecasting can provide decision support for the protection and management of water resources. A big data analysis model, Multi-scale Extreme Learning (MEL), is reported in this work to address water property forecasting. Based on the divide-and-conquer philosophy, ensemble empirical mode decomposition is first adopted to decompose the Total Phosphorus (TP) that is a representation of water property into multi-scale features. The extreme learning machine is then employed to establish regression models in different scales. The outputs of multi-scale regression models are finally summarized into the ensemble forecasting result. A time series of historical weekly TP is introduced to validate the proposed MEL. Experimental results reveal that the proposed model based on the multiple scales representation capacity and the non-linear mapping, therefore, has the best excellent performance in water property forecasting.

Citation Download Citation

Xuejiao Li, Zhiwei Cheng, Wei Wang, and Yongsheng Deng "Big data analysis model for water property forecasting", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126102G (28 April 2023); https://doi.org/10.1117/12.2671219

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