Roughness partition calibration method and system based on posteriori distribution

Abstract

The invention discloses a roughness partition calibration method and system based on posteriori distribution. The method comprises the steps that 1, dividing a target water body into k subareas; 2, determining the roughness value range of the target water body; 3, equally dividing the determined roughness value range into n intervals, and simultaneously extracting a plurality of roughness combinations in each sub-region; 4, selecting a target likelihood function to perform normalization calculation to obtain likelihood values of the sub-regions; 5, calculating the probability of the likelihoodvalue of each sub-region on each sub-interval, and obtaining the roughness posteriori distribution of each sub-region; 6, repeating the steps 3 to 5 for iteration once on the premise that the roughness posteriori distribution of each sub-region replaces the roughness uniform distribution of each sub-region; and 7, obtaining the maximum likelihood value probability of each subarea and the optimalroughness value of each subarea corresponding to the maximum likelihood value probability. According to the invention, the roughness partition calibration precision can be improved.

Description

technical field [0001] The invention relates to a roughness partition calibration method and system based on posterior distribution, and belongs to the technical field of water environment mathematical model parameter estimation and analysis. Background technique [0002] In the modeling process of large water bodies (such as rivers, lakes, reservoirs, etc.), due to the large spatial differences in the physical and chemical properties of water bodies, it is inevitable that the simulation results cannot be considered when applying a set of parameters to the entire water body space. The predicament of the current situation of the regional water environment, that is, the simulation error in one area is small and the simulation error in another area is large, so the setting of parameter partitions is particularly important to improve the accuracy of the model. [0003] At present, when performing hydrodynamic simulation on large water bodies, it has become a consensus to set par...

AI Technical Summary

Problems solved by technology

In the existing technology, it is common to set roughness partitions for large rivers, but the common practice is to gradually reduce the value range of roughness in different river sections according to the simulation error. The time cost and calculation cost of trial and error cannot be ignored, and are subject to The subjective influence of human beings is relatively large, and there is a lack of systematic calibration methods in the setting of roughness partitions for large lakes

At the same time, it is difficult to obtain the value range and prior distribution of roughness, especially the rough prior distribution is generally uniform, and it is not scientific and rational to apply it to large water bodies full of uncertainty and randomness.

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