Abstract
ABSTRACTHydrological stochastic simulation is an important method for hydrologic analysis which needs a large amount of measured data to determine the variable distribution function. That impedes the development of the random simulation in small sample cases. The characteristics of daily runoff series in the middle reaches of Yangtze River has changed a lot since the Three Gorges dam was built in 2003. The measured hydrologic data since 2003 is insufficient for stochastic simulation with conventional methods. In order to overcome the difficulty, a simulation model coupling the support vector machine with the Copula function was established in this paper. The measured data of Hankou Hydrology Station from 2003 to 2015 was used as an applied example to verify the applicability of the model. The research showed that the model we established can maintain the statistical characteristics of the original daily runoff series well, it could be a new way for the random simulation in the case of insufficient data.
Highlights
Hydrological processes are quite complicated due to the influence of meteorology, terrain and human activities
The parameters of Support vector machine (SVM)-Copula daily runoff series stochastic model can be divided into two categories: marginal distribution function parameters and the Copula function parameters
The algorithm of the SVM-Copula model consists of two parts: the marginal distributions which are determined by SVM classifier and the conditional distribution functions which are calculated by Copula functions
Summary
Hydrological processes are quite complicated due to the influence of meteorology, terrain and human activities. The stochastic model based on Copula functions can effectively describe the relationship between variables and construct multi-variable joint distribution It has been widely used in runoff stochastic simulation since Sklar first proposed the Copula theory in 1959 (Sklar, 1973). Yang, Si, Fan, Kong, and Lin (2019) constructed the correlation analysis between rainfall and runoff in Xiangxi River by using Archimedean Copula method, and the joint distribution of monthly rainfall and runoff was obtained In these studies, the determination of the variable marginal distribution and the joint distribution between variables are the two keys to construct an appropriate stochastic model. Based on the SVM pattern recognition method and the Copula function of constructing joint distribution, the SVM-Copula coupled stochastic model is established in this paper It provides a new way for the random simulation of the daily runoff sequence in the small sample cases. The SVM classification has been widely used in text classification, image recognition, handwritten recognition, biometric classification and recognition (Cristianini & Shawe, 2001)
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