Streamflow forecasting using artificial neural networks

Kuo lin Hsu, Hoshin V. Gupta, Soroosh Sorooshian

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

A variety of ANN models are being tested for their performance in forecasting daily streamflow from rainfall measurements. In this study, a new ANN structure, called SOLO (Self Organizing feature map with Linear Output) is compared to the conventional time-delay neural network (TDNN) and recurrent neural network (RNN) structures. Results for the Leaf River watershed in Mississippi indicate that the SOLO structure provides equivalent or superior performance across the full range of flow levels (base flow recessions to peaks). Further, the SOLO structure is considerably less costly (in terms of effort and computational requirements) to identify and train.

Original languageEnglish (US)
Pages967-972
Number of pages6
StatePublished - Jan 1 1998
EventProceedings of the 1998 International Water Resources Engineering Conference. Part 2 (of 2) - Memphis, TN, USA
Duration: Aug 3 1998Aug 7 1998

Other

OtherProceedings of the 1998 International Water Resources Engineering Conference. Part 2 (of 2)
CityMemphis, TN, USA
Period8/3/988/7/98

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Environmental Science(all)

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    Hsu, K. L., Gupta, H. V., & Sorooshian, S. (1998). Streamflow forecasting using artificial neural networks. 967-972. Paper presented at Proceedings of the 1998 International Water Resources Engineering Conference. Part 2 (of 2), Memphis, TN, USA, .