DHESN: A deep hierarchical echo state network approach for algal bloom prediction

A deep hierarchical echo state network (DHESN) is designed for rectifying the shortcomings of the shallow coupled structure with less reservoir dynamics. This design is with reference to algal bloom which is a complex ecological phenomenon. Accurate prediction of algal bloom can reduce the environmental impact and economic loss. Since the formation of algal bloom has chaotic characteristics, the ESN has been employed to realize its prediction function. First, the candidate variables with strong causal relationship have been screened by transfer entropy, and the redundant variables is eliminated. Then, a hierarchical reservoir structure is established that is inspired by the hierarchical characteristics from the brain. The hierarchical reservoir has realized the connection between the representative nodes of each subreservoir, and improved the information processing ability of the reservoir. Finally, the pruning and compression of the output weights have been realized by the elastic regularization method, which improves the robustness of the prediction model. The simulation results demonstrate that the DHESN has appreciable prediction accuracy in both the chaotic and the public algal bloom datasets. The DHESN contains richer dynamic characteristics, and can realize the self-organization of the network structure. It provides a novel idea to realize the prediction model of algal bloom with a high accuracy and low complexity.

Automated summary

A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.