Flood disaster prediction and early warning method based on QR-BC-ELM

Abstract

The invention discloses a flood disaster prediction and early warning method based on QR-BC-ELM. The invention discloses a flood early warning method, relates to the technical field of flood early warning, considers various flood causes, establishes a flood model based on an extreme learning machine and a geographic information system to simulate a flood-prone area of the Yellow River basin, and verifies the efficiency and precision advantages of the extreme learning machine relative to an artificial neural network. The learning speed of the improved extreme learning model provided by the invention is 32 times that of an artificial neural network and 1.2 times that of a traditional extreme learning model. Moreover, the noise processing capacities of the proposed orthogonal triangular decomposition extreme learning model and the full-rank decomposition extreme learning model are greatly superior to those of an artificial neural network, and the BC-ELM and the QR-ELM have great advantages in the aspects of prediction precision and prediction efficiency, and are relatively suitable for flood prediction models.

Description

technical field [0001] The invention relates to the technical field of flood early warning, in particular to a flood disaster prediction and early warning method based on QR-BC-ELM. Background technique [0002] Today, flood disaster management has entered the era of intelligence, especially the development of data science has provided experts with timely, accurate and rich disaster information and decision support. In the current form, the flood model dominated by intelligent algorithms has made considerable progress, and solutions such as cluster analysis, collaborative filtering, and regression prediction have been widely applied to flood models. Intelligent algorithms dominated by neural networks are widely used in hydrological forecasting, and are more scientific, informative, time-sensitive, and accurate than traditional solutions. Ideas such as cluster analysis and collaborative filtering break through the problems of high memory consumption and complex calculations ...

AI Technical Summary

Problems solved by technology

Flood models based on traditional schemes have problems such as subjective factor intervention, one-sided characteristic response, low prediction accuracy, and complex calculations.

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