An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds

  • Iliadis L
  • Maris F
  • 36

    Readers

    Mendeley users who have this article in their library.
  • 51

    Citations

    Citations of this article.

Abstract

This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of mountainous water supplies. It proposes a model to be used effectively in the estimation of the average annual water supply, in each mountainous watershed of Cyprus. This is really a crucial task, especially during the long dry summer months of the island. On the other hand the evaluation of the potential torrential risk due to high volume of water flow in the winter season is also very important. Data (from 1965-1993) from 78 measuring stations located in the 70 distinct watersheds of Cyprus were used. This data volume was divided in the training subset comprising of 60 cases and in the testing subset containing 18 cases. The input parameters are the area of the watershed, the average annual and the average monthly rain-height, the altitude and the slope in the location of the measuring station. Consequently three structural and two dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one. © 2006 Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Artificial neural networks
  • Back propagation algorithm
  • Maximum volume of water-flow
  • Water resources management

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Lazaros S. Iliadis

  • Fotis Maris

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free