A River Runoff Prediction Method Based on Complex Network

Abstract

The invention discloses a river runoff prediction method mainly for runoff prediction for PUBs (prediction ungauged basins). The basic principle of the method refers to: using a complex network to mine topological characteristics of a hydrological spatiotemporal sequence, and performs runoff prediction on ungaged basins on such basis; a runoff complex network model is established according to runoff data of a monitoring network station, FN (fast Newman) algorithm is used to perform community mining on such basis, and candidate nodes are selected based on community mining results. The method of the invention considers relevancy between basin division and PUBs, common nodes and characteristic nodes are selected as candidate nodes, and runoff of a station to be predicted is predicted by means of transplantation process. The ungauged basin runoff prediction method considering both relevancy of runoff data topological structures and the runoff data itself is provided herein.

Description

Technical field [0001] The invention relates to the application field of complex networks, in particular to a method for predicting river runoff based on complex networks. Background technique [0002] Rivers play an important role in hydrology, water resources management, environment, and ecosystems. However, the evaluation and prediction of river runoff still face many problems. Because river runoff is a complex nonlinear process that interacts with climatic conditions and geomorphological features. For example, river runoff is not only affected by rainfall distribution in time and space, but many factors such as land use parameters, hydrological and soil factors, and geostatistical properties will all have an impact on river runoff. [0003] The existing research on river runoff is mainly to identify the connections between river runoff. However, most of the existing studies depend on specific issues and specific elements involved. There are still many problems in the study of...

AI Technical Summary

Problems solved by technology

However, most of the existing research depends on the specific issues and the specific elements involved, and there are still many problems in the research on river runoff prediction.

For example, most of the existing hydrological prediction models are relatively complex and need to rely on too many parameters and data for analysis. At the same time, due to the deviation of the data itself and the deviation of the model itself, the prediction of runoff is cumbersome and not necessarily reliable; Although some model correction algorithms for bias correction reduce the prediction error to a certain extent, this method does not help in understanding the hydrological mechanism of the watershed; from another perspective, most of the existing models are for a specific area However, there are still problems in applying them to wider watersheds, such as the Xin'anjiang model, etc., so there is a lack of a unified and universal hydrological framework

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