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The river runoff forecast based on the modeling of time series

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Abstract

Discussed are the methods of stochastic modeling the precipitation runoff time series and fields. Discussed are the structural attributes, scope, boundary conditions and various improvements of the univariate Autoregressive Integrated Moving Average (ARIMA) and the multivariate Transfer Function Model (TFM). Presented are the comparative studies of existing models of the neural network. An attempt is made to investigate various geographical locations and various applications of the river runoff forecast.

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  1. Rajeev Gandhi Technical University, Bhopal, M.P., India

    R. Nigam

  2. LNCTS, Bhopal, M.P., India

    S. Nigam

  3. MANIT, Bhopal, M.P., India

    S. K. Mittal

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Original Russian Text © R. Nigam, S. Nigam, S.K. Mittal, 2014, published in Meteorologiya i Gidrologiya, 2014, No. 11, pp. 56–73.

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Nigam, R., Nigam, S. & Mittal, S.K. The river runoff forecast based on the modeling of time series. Russ. Meteorol. Hydrol. 39, 750–761 (2014). https://doi.org/10.3103/S1068373914110053

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