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,t,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.

Sustainable River: The role of non-structural measures and public outreach programmes

“…The theory established four models: single-point, multi-point, lineal, and three-parameter. The model was used to explain China's frequent floods' mid-and long-term hydrologic prediction (Zhang et al, 2009). According to Zhang et al (2009), chaotic phase space theory has a more robust non-linear mapping function and much more information in time series than traditional methods.…”

“…The model was used to explain China's frequent floods' mid-and long-term hydrologic prediction (Zhang et al, 2009). According to Zhang et al (2009), chaotic phase space theory has a more robust non-linear mapping function and much more information in time series than traditional methods. Zhang et al (2009) postulated that chaotic phase space theory has a more robust non-linear mapping function and much more information in time series than traditional methods.…”

Echo state network with wavelet in load forecasting

“…Since short-term load forecasting is important for the economical and safety operation of power system, it has received focus from many scholars and researchers. Regression series, Markov and chaos phase space have been studied a lot (Zhang et al, 2009;Dongqing et al, 2009;Luo et al, 2010), and at current, neural network, support vector machine (SVM) and recurrent neural network (RNN) are popular in time series forecasting (Niu et al, 2010a, b;Wang, Y. et al, 2010;Cao and Lin, 2008a, b;Wang, J.G. et al, 2010;Liu et al, 2009).…”

Multiple time scales analysis of runoff series based on the Chaos Theory

“…area, and saturation of groundwater flows [3]. Most of the research in literature confirms the presence of chaos in the runoff time series [4]. Nonetheless, the existence of low-dimensional chaos has been a topic in wide dispute.…”