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.
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