Short-term Streamflow Forecasting: ARIMA Vs Neural Networks

نویسندگان

  • JUAN FRAUSTO-SOLIS
  • ESMERALDA PITA
  • JAVIER LAGUNAS
چکیده

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network. Key-Words: Auto regressive Integrated Moving Average, Artificial Neural Networks, Streamflow, Forecasting.

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تاریخ انتشار 2008