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A multivariate non‐parametric model for synthetic generation of daily streamflow
Author(s) -
Wang Wensheng,
Ding Jing
Publication year - 2007
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.6340
Subject(s) - multivariate statistics , streamflow , parametric statistics , kernel density estimation , multivariate kernel density estimation , mathematics , kernel (algebra) , multivariate normal distribution , statistics , kernel method , econometrics , computer science , variable kernel density estimation , drainage basin , artificial intelligence , geography , discrete mathematics , cartography , estimator , support vector machine
A p ‐order multivariate kernel density model based on kernel density theory has been developed for synthetic generation of multivariate variables. It belongs to a kind of data‐driven approach and is able to avoid prior assumptions as to the form of probability distribution (normal or Pearson III) and the form of dependence (linear or non‐linear). The p ‐order multivariate kernel density model is a non‐parametric method for synthesis of streamflow. The model is more flexible than conventional parametric models used in stochastic hydrology. The effectiveness and satisfactoriness of this model are illustrated through its application to the simultaneous synthetic generation of daily streamflow from Pingshan station and Yibin‐Pingshan region (Yi‐Ping region) of the Jinsha River in China. Copyright © 2007 John Wiley & Sons, Ltd.