Flood probability forecasting method based on multi-source uncertainty

Abstract

The invention relates to a flood probability forecasting method based on multi-source uncertainty. The method comprises the following steps: estimating surface rainfall probability distribution basedon an incomplete rainfall monitoring group; constructing a suboptimal solution set of each model parameter, and obtaining probability distribution of the model parameters; randomly generating a combination of surface rainfall and parameters, and driving the hydrological model to generate a plurality of groups of initial forecast flow processes; estimating the probability that each model is relatively optimal by using a BMA method; and randomly extracting the optimal model and the corresponding initial forecast flow, and estimating the posterior probability distribution of the forecast flow torealize probability forecast. According to the invention, uncertainty in three aspects of rainfall input, parameters and a model structure is comprehensively considered. A calculation method for realizing flood probability prediction can be widely applied to the situations that rainfall input errors are obvious. A model or a model structure suitable for flood prediction cannot be accurately predicted, and model parameter uncertainty is high, the accuracy and reliability of a flood prediction result can be improved, and technical support is provided for flood control and disaster reduction work.

Description

technical field [0001] The invention relates to the technical fields of hydrology and water resources, in particular to a flood probability forecasting method which comprehensively considers the uncertainty of rainfall input, parameters and model structure. Background technique [0002] The discipline of hydrology and water resources is one of the important technical fields of non-engineering measures for flood control. In the long history of discipline development, people have formed a certain degree of experience and knowledge reserves in terms of water source division mechanism and slope surface flow generation and confluence mechanism. However, the accuracy of flood forecasting is significantly affected by uncertainties in sources such as rainfall input, soil hydrological initial conditions, parameter generalization ability, and model structure. As a result, traditional hydrological forecasting techniques cannot accurately reflect the physical mechanism of watershed runof...

AI Technical Summary

Problems solved by technology

The purpose of the present invention is to propose a new set of solutions that comprehensively consider various sources of uncertainty and realize probability forecasting in view of the objective reality that multi-source uncertainty is difficult to avoid and it is difficult to comprehensively evaluate its impact on flood forecasting

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