Rainfall-runoff modeling using Artificial Neural Networks

A. Sezin Tokar, Peggy Ann Johnson

Research output: Contribution to journalArticle

472 Scopus citations

Abstract

An Artificial Neural Network (ANN) methodology was employed to forecast daily runoff as a function of daily precipitation, temperature, and snowmelt for the Little Patuxent River watershed in Maryland. The sensitivity of the prediction accuracy to the content and length of training data was investigated. The ANN rainfall-runoff model compared favorably with results obtained using existing techniques including statistical regression and a simple conceptual model. The ANN model provides a more systematic approach, reduces the length of calibration data, and shortens the time spent in calibration of the models. At the same time, it represents an improvement upon the prediction accuracy and flexibility of current methods.

Original languageEnglish (US)
Pages (from-to)232-239
Number of pages8
JournalJournal of Hydrologic Engineering
Volume4
Issue number3
DOIs
StatePublished - Jul 1 1999

All Science Journal Classification (ASJC) codes

  • Environmental Chemistry
  • Civil and Structural Engineering
  • Water Science and Technology
  • Environmental Science(all)

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