A river ecological abnormal change identification method based on machine learning

Abstract

The application discloses a river ecological abnormal change identification method based on machine learning, relates to the technical field of machine learning, and comprises the following steps: collecting multi-section ecological monitoring data, preprocessing, and constructing a time-space sequence dataset; a river directed topological structure is constructed, a topological adjacency matrix is generated, and the association mapping of time-space data and nodes is completed; the time-space characteristics and the adjacency matrix are inputted, the diffusion law is learned by improving DCRNN, and a dynamic ecological baseline sequence is outputted; a stable ecological sample set is constructed, a stable subspace is generated by mapping, the baseline is corrected, and the structural deviation is calculated; an ecological energy transmission interval matrix is constructed, bidirectional propagation consistency discrimination is performed, and a propagation consistency index is obtained; the baseline residual error is calculated, the structural deviation is combined, and an abnormal identification result is outputted. The application realizes accurate identification and early warning of gradual ecological abnormal changes in the river by constructing a topological constraint dynamic ecological baseline and fusing stable ecological structure and propagation consistency discrimination.

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