题名

A Comparative Analysis of Nonlinear and ARIMA Models for Daily Streamflow Forecasting

DOI

10.29417/JCSWC.202206_53(2).0004

作者

Shaw Wen Sheen

关键词

Chaos theory ； local approximation method ； Nash-Sutcliffe efficiency ； persistence index ； ARIMA ； streamflow prediction

期刊名称

中華水土保持學報

卷期/出版年月

53卷2期（2022 / 06 / 01）

页次

111 - 123

内容语文

英文
中文摘要

The theory of chaos which deals with unpredictable complex nonlinear systems had its breakthrough in the past decades. The aim of this study was to employ chaos methods, including phase space reconstruction and local approximation method, to examine the existence of chaos in streamflow dynamics. This study also applied ARIMA time series model to predict streamflow data in the series. The major objective was to investigate and compare the prediction accuracy of daily streamflow time series models from the gauging station in the northeastern Taiwan. The gauging station was at the Lanyang River. The observed streamflow time series spanned a time period of 70 years from 1950 to 2019. The first 50-year streamflow data were used as the training data set, and the last 20-year data were used as the testing data set. This study applied efficiency criteria: coefficient of variation, Nash-Sutcliffe efficiency, and persistence index. The results showed that the local approximation prediction models had better prediction performance than the ARIMA models at the gauging station used in this study. Prediction performance was much better in the recession periods than in the rising periods. The results concluded that the existence of chaos might exist only in the recession periods. In the rising periods, streamflow dynamics were more stochastic.
主题分类 生物農學 > 農業
生物農學 > 森林
生物農學 > 畜牧
生物農學 > 漁業
生物農學 > 生物環境與多樣性
工程學 > 土木與建築工程
工程學 > 市政與環境工程
参考文献
  1. Burt, J.E.,Barber, G.M.(1996).Elementary Statistics for Geographers.New York:Guilford.
  2. Crutchfield, J.P.,Farmer, J.D.,Packard, N.H.,Shaw, R.S.(1986).Chaos.Scientific American,244,46-57.
  3. Di, C.,Wang, T.,Istanbulluoglu, E.,Jayawardena, A.W.,Li, S.,Chen, X.(2019).Deterministic chaotic dynamics in soil moisture across Nebraska.Journal of Hydrology,578,1-12.
  4. Farmer, J.D.,Sidorowich, J.J.(1987).Predicting chaotic time series.Physical Review Letters,59,845-848.
  5. Islam, M.N.,Sivakumar, B.(2002).Characterization and prediction of runoff dynamics: a nonlinear dynamical view.Advances in Water Resources,25,179-190.
  6. Jayawardena, A.W.(1997).Runoff forecasting using a local approximation method.Destructive Water: Water-Caused Natural Disasters-Their Abatement and Control,Oxford:
  7. Jayawardena, A.W.,Gurung, A.B.(2000).Noise reduction and prediction of hydrometeorological time series: dynamical systems approach vs. stochastic approach.Journal of Hydrology,228,242-264.
  8. Jayawardena, A.W.,Lai, F.(1994).Analysis and prediction of chaos in rainfall and stream flow time series.Journal of Hydrology,153,23-52.
  9. Jayawardena, A.W.,Li, W.K.,Xu, P.(2002).Neighborhood selection for local modeling and prediction of hydrological time series.Journal of Hydrology,258,40-57.
  10. Kember, G.,Flower, A.C.,Holubeshen, J.(1993).Forecasting river flow using nonlinear dynamics.Stochastic Hydrology and Hydraulics,7,205-212.
  11. Khatibi, R.,Sivakumar, B.,Ghorbani, M.A.,Kisi, O.,Koçak, K.,Zadeh, D.F.(2012).Investigating chaos in river stage and discharge time series.Journal of Hydrology,414,108-117.
  12. Mehr, A.D.,Kahya, E.(2017).A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction.Journal of Hydrology,549,603-615.
  13. Phoon, K.K.,Islam, M.N.,Liaw, C.Y.,Liong, S.Y.(2002).Practical inverse approach for forecasting nonlinear hydrological time series.Journal of Hydrologic Engineering,7,116-128.
  14. Porporato, A.,Ridolfi, L.(2001).Multivariate nonlinear prediction of river flows.Journal of Hydrology,248,109-122.
  15. Porporato, A.,Ridolfi, L.(1996).Clues to the existence of deterministic chaos in river flow.International Journal of Modern Physics B,10,1821-1862.
  16. Porporato, A.,Ridolfi, L.(1997).Nonlinear analysis of river flow time sequences.Water Resources Research,33,1353-1367.
  17. Sivakumar, B.(2000).Chaos theory in hydrology: important issues and interpretations.Journal of Hydrology,227,1-20.
  18. Sivakumar, B.(2007).Nonlinear determinism in river flow: prediction as a possible indicator.Earth Surface Processes and Landforms,32,969-979.
  19. Sivakumar, B.(2009).Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward.Stochastic Environmental Research and Risk Assessment,23,1027-1036.
  20. Sivakumar, B.(2017).Chaos in Hydrology: Bridging Determinism and Stochasticity.Netherlands:Springer.
  21. Sivakumar, B.,Berndtsson, R.,Persson, M.(2001).Monthly runoff prediction using phase-space reconstruction.Hydrological Sciences Journal,46,377-387.
  22. Sivakumar, B.,Jayawardena, A.W.,Fernando, T.M.K.G.(2002).River flow forecasting: use of phasespace reconstruction and artificial neural networks approaches.Journal of Hydrology,265,225-245.
  23. Sivakumar, B.,Jayawardena, A.W.,Li, W.K.(2007).Hydrologic complexity and classification: a simple data reconstruction approach.Hydrological Processes,21,2713-2728.
  24. Sivakumar, B.,Persson, M.,Berndtsson, R.,Uv, C.B.(2002).Is correlation dimension a reliable indicator of low-dimensional chaos in short hydrological time series?.Water Resources Research,38,3–1-3-8.
  25. Sugihara, G.,May, R.M.(1990).Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series.Nature,344,734-741.
  26. Tsonis, A.A.,Elsner, J.B.(1992).Nonlinear prediction as a way of distinguishing chaos from random fractal sequences.Nature,358,217-220.
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