• Title of article

    Runoff forecasting by artificial neural network and conventional model

  • Author/Authors

    Ghumman, A.R. Al Qassim University - Department of Civil Engineering, Saudi Arabia , Ghazaw, Yousry M. Al Qassim University - Department of Civil Engineering, Saudi Arabia , Sohail, A.R. Water Resources Murray–Darling Basin Authority, Australia , Watanabe, K. Saitama University - Technical Development Center - Saitama Package-D, Japan

  • From page
    345
  • To page
    350
  • Abstract
    Rainfall runoff models are highly useful for water resources planning and development. In the present study rainfall–runoff model based on Artificial Neural Networks (ANNs) was developed and applied on a watershed in Pakistan. The model was developed to suite the conditions in which the collected dataset is short and the quality of dataset is questionable. The results of ANN models were compared with a mathematical conceptual model. The cross validation approach was adopted for the generalization of ANN models. The precipitation used data was collected from Meteorological Department Karachi Pakistan. The results confirmed that ANN model is an important alternative to conceptual models and it can be used when the range of collected dataset is short and data is of low standard.
  • Keywords
    Hub River , ANN models , Mathematical models , Low quality data , Runoff analysis
  • Journal title
    Alexandria Engineering Journal
  • Journal title
    Alexandria Engineering Journal
  • Record number

    2540012