Lake and reservoir cyanobacterial bloom prediction method based on self-organizing depth confidence echo state network

Abstract

The invention discloses a lake and reservoir cyanobacterial bloom prediction method based on a self-organizing depth confidence echo state network, and belongs to the technical field of cyanobacterial bloom prediction and information science cross fusion. According to the lake and reservoir cyanobacterial bloom prediction method, a mutual information method is adopted to screen an input variable and an output variable, then a structure of a deep confidence echo state network is constructed, self-organizing mechanisms are designed for the deep confidence network and the echo state network respectively, and a self-organizing deep confidence echo state network model is obtained after optimization of the structure self-organizing mechanisms so as to effectively predict cyanobacterial blooms in lakes and reservoirs, subsequent treatment of cyanobacterial blooms in lakes and reservoirs is faciliated. According to the method, deep features of training data are fully learned, a self-organizing mechanism is designed for the deep confidence echo state network, dynamic adjustment of hidden layer neurons and the number of sub-reserve pools is achieved, the method is suitable for lake and reservoir cyanobacterial bloom data containing abnormal values such as detection noise, and the precision and robustness of a prediction result can be improved.

Description

technical field [0001] The invention belongs to the technical field of cross-integration of cyanobacteria bloom prediction and information science, and specifically relates to a lake reservoir cyanobacteria bloom prediction method based on a self-organized deep confidence echo state network. Background technique [0002] Lake cyanobacteria bloom refers to the abnormal and rapid reproduction of algae and plankton in eutrophic lakes, which gather in large quantities on the surface of the water body to form a layer of blue-green algae that is visible to the naked eye and thickly covers the water surface. Due to the continuous discharge of urban and industrial wastewater into lakes and reservoirs, the content of nutrients such as nitrogen and phosphorus in the water is getting higher and higher, which provides an environmental basis for the outbreak of cyanobacteria blooms. Generally speaking, factors such as water temperature, wind speed, and nutrients will affect the outbreak ...

AI Technical Summary

Problems solved by technology

[0003] In order to effectively solve the problems of insufficient accuracy and poor robustness of existing lake and reservoir cyanobacteria bloom prediction methods, the present invention proposes a cyanobacteria bloom prediction method based on a self-organized depth confidence echo state network

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