Similarity model of chaos phase space and its application in mid‐ and long‐term hydrologic prediction

Abstract

Purpose

With frequent floods occurring, and the fast economic development in China, attention must be paid to flood prevention, water supply, and forecasting precision. In particular, mid‐ and long‐term runoff prediction is being paid more and more attention by researchers, and it is also the most difficult problem to solve. The purpose of this paper is to apply chaos phase space theory to forecast river run off.

Design/methodology/approach

Because the hydrologic system is a complicated huge system, there exist high non‐linear characteristics in the space‐time change of hydrologic factors. According to theory of chaotic phase space, the paper established four models of the single‐point, multi‐point, lineal, and three‐parameter D(m,τ,k) models, they have stronger non‐linear mapping function and much more information in the time series than traditional ways.

Findings

The results of calculation show that the models are highly effective and worthy of popularization and application. It is reasonable and superior to use these models in mid‐ and long‐term hydrologic prediction.

Research limitations/implications

The method cannot reduce or eliminate the un‐prediction parts caused by the inner random factors, such as the noise information of the observed data.

Practical implications

The models are applied in the long‐term runoff prediction of Baishan reservoir.

Originality/value

The new approach of hydrology forecasting due to the theory of chaotic phase space. The paper is aimed at hydrology forecasting researches and engineers, especially those who dealt with the mid‐ and long‐term prediction.

Citation

Zhang, L., Xia, J., Song, X. and Cheng, X. (2009), "Similarity model of chaos phase space and its application in mid‐ and long‐term hydrologic prediction", Kybernetes, Vol. 38 No. 10, pp. 1835-1842. https://doi.org/10.1108/03684920910994367

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