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Forecasting river flow using nonlinear dynamics

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Abstract

A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts.

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Authors and Affiliations

  1. Mathematical Institute, Oxford University, 24-29 St. Giles, OX1 3LB, Oxford, England

    G. Kember & A. C. Flower

  2. Beak Consultants Ltd., 14 Abacus Rd., L6T 5B7, Brampton, Ontario, Canada

    J. Holubeshen

Authors
  1. G. Kember
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  2. A. C. Flower
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  3. J. Holubeshen
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Kember, G., Flower, A.C. & Holubeshen, J. Forecasting river flow using nonlinear dynamics. Stochastic Hydrol Hydraul 7, 205–212 (1993). https://doi.org/10.1007/BF01585599

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