Hydrologic model parameter estimation method based on FCM-NSGA-II

Abstract

The invention relates to the field of hydrology simulation and forecasting, in particular to a hydrologic model parameter estimation method based on FCM-NSGA-II. The method comprises the steps of 1, adopting a fuzzy C-means clustering method to classify an unknown flood process, selecting rainfall data and potential evaporation capacity data as clustering indexes, normalizing the clustering indexes, and then dividing drainage basin data into a wet season and a dry season according to an FCM model; 2, setting target functions aiming at the wet season and the dry season separately; 3, adopting an NSGA-II multiobjective parameter optimization algorithm to estimate an optimal parameter set of a semi-distributed hydrologic model TOPMODEL to obtain a forecasting result of classification of a hydrologic process; 4, setting a single target function for the whole hydrologic time sequence, and adopting an SCE-UA single target parameter optimization algorithm to calculate the optimal parameter set of the TOPMODEL hydrologic model to obtain a forecasting result of unclassification of the hydrologic process; 5, comparing the forecasting result obtained in the third step with the forecasting result obtained in the fourth step, and conducting mutual verification.

Description

technical field [0001] The invention relates to the field of hydrological simulation and forecasting, and more specifically, to a method for estimating hydrological model parameters based on FCM-NSGA-II. Background technique [0002] Hydrological forecasting is one of the most important and difficult problems in hydrological science research. Improving the accuracy and reliability of hydrological forecasting can provide a more accurate and reliable basis for decision-making on flood control and water resource management. To explore new methods for hydrological simulation in data-free or data-poor areas and to improve the forecasting accuracy is one of the cores of current hydrological forecasting research. [0003] One of the effective ways to improve the accuracy of hydrological simulation and forecast is to make full use of existing data and introduce new data sources. When there is no new data source, the useful information in the existing data can be mined to the great...

AI Technical Summary

Problems solved by technology

Traditional model parameters are generally based on single-objective optimization, which cannot consider the changing characteristics of hydrological regimes (wet and dry seasons). It is necessary to introduce the idea of ​​multi-objective optimization to overcome the limitations of single-objective

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