Multi-objective optimization sampling based hydrologic model uncertainty analysis method

Abstract

The invention provides a multi-objective optimization sampling based hydrologic model uncertainty analysis method. The method specifically comprises the steps of 1) constructing a likelihood objective function by adopting a plurality of judgment standards; 2) clearly determining a value range and a prior distribution form of a hydrologic model parameter, and performing sampling by adopting an improved non-dominated sorting genetic algorithm epsilon-NSGAII; 3) analyzing the uncertainty of a hydrologic model; and 4) estimating a prediction uncertainty range of the hydrologic model. The method has the beneficial effects that multiple criteria are used as the judgment standards of a sampling effect, so that the sampling effect can be comprehensively reflected; the sampling is carried out by adopting the improved non-dominated sorting genetic algorithm epsilon-NSGAII, an elitist strategy is used, and non-dominated samples with low congestion degree are reserved only, so that the sampling efficiency, the parameter uncertainty and a flood prediction result can be effectively improved; and in flood control scheduling and risk control processes, the method can provide important reference information for decision makers.

Description

technical field [0001] The invention relates to the field of uncertainty analysis in water resource management, in particular to a sampling method in a multi-objective uncertainty analysis method of a flood forecast model. Background technique [0002] Hydrological models are widely used in water resource management, and the performance of hydrological models is mainly affected by model parameters. Traditional hydrological model parameter calibration methods try to find a set of optimal solutions suitable for a specific watershed, but due to the phenomenon of different parameters having the same effect, different parameter sets will show the same model effect. [0003] Therefore, scholars have proposed the Universal Likelihood Uncertainty Evaluation method (GLUE), which can give the upper and lower limits of hydrological model forecasts, which is more conducive to management decision-making. However, the universal likelihood uncertainty assessment method has the problem of ...

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Problems solved by technology

However, the universal likelihood uncertainty assessment method has the problem of low sampling efficiency, so many studies are devoted to improving its sampling efficiency, such as sampling methods using Bayesian theory and sampling methods using single-objective optimization

[0004] These sampling methods all use a single target as the evaluation standard of sampling effect, but in the flood forecast model, flood peak, flood volume, flood peak occurrence time and certainty coefficient are all issues that need to be considered, and only single target as the evaluation standard of sampling effect cannot Fully reflect the sampling effect

Moreover, most of the above-mentioned existing methods only start from the statistical point of view in the sampling process, and cannot directly improve the sampling efficiency and the multi-objective performance of the sampling results.

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