A combination prediction model based on ELM and prior information of shapelet for algal bloom

Abstract

Prediction is crucial to prevent the outbreaks of algal bloom. However, due to the time-varying and nonlinear characteristics of algal bloom, which brings challenges for accurate prediction. Aiming at solving the problem, we consider both prior information and data-driven, and propose an error compensation combination prediction model based on the manifold regularized extreme learning machine fused shapelet (ECM-MRELM-FS). First, the prior information is extracted as the upward shapelet set to discover and predict the value of algal bloom at the typical evolution mode. Meanwhile, a data-driven model of manifold regularized extreme learning machine (MRELM) is used to predict the value of algal bloom at other atypical evolution modes. This switching combination builds a prediction model by MRELM fused the upward shapelet, which can effectively utilize the advantages of prior information and data-driven. Then, an optimized T-S fuzzy inference systems as error compensation model (ECM) based on improved fuzzy c-means clustering is proposed to promote the accuracy of the prediction further. The experiments are performed by two real water quality datasets from USA and China. The final results demonstrate that the proposed combination prediction model can effectively predict algal bloom and provide a feasible and scientific early warning support for the water quality management in lake and reservoir.