Abstract
In this paper, we examined the applicability of a state space predictor and a fractal dimension to the short-term prediction of water-stages in a tidal river. The previous researches showed that the state space predictor was an efficient tool, particularly in problems when the characteristics of process could hardly be described by only physical equations. The fractal dimension were also found an effective measure showing the possibility of predictions and to be calculated more precisely, using Higuchi's method, than those presented before. First, hourly water-stage time series was embedded into a state space using time delay coordinates. The induced mapping was then obtained from the embedded trajectory using a local approximation. This enabled us to make the short-term prediction of time series using the information based only on the past values. Second, the fractal dimension calculated by Higuchi's method was incorporated in the state space predictor to estimate the confidence limit of prediction. It was concluded that the state space predictor was a powerful tool in the short-term prediction of water-stages having a strong autocorrelation structure due to tidal motion. The maximum lead-time of prediction was efficiently determined using the fractal dimensions calculated by Higuchi's method.
| Original language | English |
|---|---|
| Pages (from-to) | 255-265 |
| Number of pages | 11 |
| Journal | Journal of the Faculty of Agriculture, Kyushu University |
| Volume | 45 |
| Issue number | 1 |
| Publication status | Published - Nov 2000 |
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All Science Journal Classification (ASJC) codes
- Agricultural and Biological Sciences (miscellaneous)
Cite this
Chaotic and fractal modeling of water-stage time series in a tidal river. / Hiramatsu, Kazuaki; Shikasho, Shiomi; Mori, Ken.
In: Journal of the Faculty of Agriculture, Kyushu University, Vol. 45, No. 1, 11.2000, p. 255-265.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Chaotic and fractal modeling of water-stage time series in a tidal river
AU - Hiramatsu, Kazuaki
AU - Shikasho, Shiomi
AU - Mori, Ken
PY - 2000/11
Y1 - 2000/11
N2 - In this paper, we examined the applicability of a state space predictor and a fractal dimension to the short-term prediction of water-stages in a tidal river. The previous researches showed that the state space predictor was an efficient tool, particularly in problems when the characteristics of process could hardly be described by only physical equations. The fractal dimension were also found an effective measure showing the possibility of predictions and to be calculated more precisely, using Higuchi's method, than those presented before. First, hourly water-stage time series was embedded into a state space using time delay coordinates. The induced mapping was then obtained from the embedded trajectory using a local approximation. This enabled us to make the short-term prediction of time series using the information based only on the past values. Second, the fractal dimension calculated by Higuchi's method was incorporated in the state space predictor to estimate the confidence limit of prediction. It was concluded that the state space predictor was a powerful tool in the short-term prediction of water-stages having a strong autocorrelation structure due to tidal motion. The maximum lead-time of prediction was efficiently determined using the fractal dimensions calculated by Higuchi's method.
AB - In this paper, we examined the applicability of a state space predictor and a fractal dimension to the short-term prediction of water-stages in a tidal river. The previous researches showed that the state space predictor was an efficient tool, particularly in problems when the characteristics of process could hardly be described by only physical equations. The fractal dimension were also found an effective measure showing the possibility of predictions and to be calculated more precisely, using Higuchi's method, than those presented before. First, hourly water-stage time series was embedded into a state space using time delay coordinates. The induced mapping was then obtained from the embedded trajectory using a local approximation. This enabled us to make the short-term prediction of time series using the information based only on the past values. Second, the fractal dimension calculated by Higuchi's method was incorporated in the state space predictor to estimate the confidence limit of prediction. It was concluded that the state space predictor was a powerful tool in the short-term prediction of water-stages having a strong autocorrelation structure due to tidal motion. The maximum lead-time of prediction was efficiently determined using the fractal dimensions calculated by Higuchi's method.
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M3 - Article
AN - SCOPUS:0034559368
VL - 45
SP - 255
EP - 265
JO - Journal of the Faculty of Agriculture, Kyushu University
JF - Journal of the Faculty of Agriculture, Kyushu University
SN - 0023-6152
IS - 1
ER -