NDLI: Research on runoff forecast model based on phase space reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Li Jingbo Dong Zengchuan Wang Dezhi Li Shaohua
Copyright Year	2008
Description	Author affiliation: State Key Lab. of Hydrol.-Water Resources & Hydraulic Eng., Hohai Univ., Nanjing (Li Jingbo; Dong Zengchuan)
Abstract	In this paper, the problem of runoff forecasting is researched for water supply reservoir group based on Phase Space Reconstruction Theory. The statistic method of BDS is applied to prove its non-linearity and the largest Lyapunov exponent is computed, which manifests that there is chaotic characteristics in the runoff sequence of reservoir group. Single-dimensional and multi-dimensional runoff forecast models are built and analyzed based on State Space Reconstruction Theory, Artificial Neural Network and Genetic Algorithm. Their performances in practice are compared and analyzed, which manifests its validity and a broad prospect.
Starting Page	5339
Ending Page	5343
File Size	248299
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4593799
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Time series analysis Reservoirs Water resources Predictive models Hydrology Gallium phase space reconstruction runoff forecast long-term forecast chaos
Content Type	Text
Resource Type	Article

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Research on runoff forecast model based on phase space reconstruction