Hydrologic process data and historical prior data combined flood prediction method

Abstract

The invention discloses a hydrologic process data and historical prior data combined flood prediction method. A weighted clustering algorithm is adopted to perform cluster analysis on rainfall information to approximate water content information; cleaning is conducted on data, missing data is supplemented, and wrong data is corrected; a general hydrologic model is selected according to the basin characteristics to obtain causal reasoning relationships between variables in the hydrologic process; a directed graph manner is adopted to represent each set of reasoning relationship, wherein nodes represent physical variables, the direction of edges indicates the causal reasoning relationships, and a bayesian network is constructed by using a topological structure; historical data is utilized incombination with the general hydrologic model for simulation to generate intermediate result data to form a sample set together; historical prior knowledge is obtained according to the sample set, and a conditional probability table is calculated to achieve training on the network and complete the comprehensive modeling of hydrologic process knowledge and the historical prior knowledge; test datais input to achieve flood prediction.

Description

technical field [0001] The invention relates to a flood forecasting method combining hydrological process data and historical prior data, belonging to the technical field of flood forecasting. Background technique [0002] Flash floods are sudden floods caused by precipitation in small and medium-sized rivers in hilly areas. However, there are many small and medium-sized rivers in my country, and each watershed has unique geographical features and climates, so there is a lack of unified and convenient modeling methods. In recent years, due to the frequent occurrence of extreme weather, continuous heavy rainfall and sudden rainstorms often occur in the area, while the underlying surface of the hilly area presents high and steep slopes, and the source of the river is short and fast. Disasters pose a threat to people's lives and property. Therefore, the country attaches great importance to flood forecasting based on small and medium rivers. [0003] In the field of flood cont...

AI Technical Summary

Problems solved by technology

Due to the inability to obtain accurate hydrological elements (rainfall and flow) and the reliable spatio-temporal distribution of water cycle elements in the watershed, the intelligent model has certain limitations in forecasting performance

In hydrological forecasting, there are uncertainties in various factors, and the factors are interrelated, which makes the physics-driven model and data-driven model have their own shortcomings

Images