Volume 11, Issue 40 (summer 2007)
jwss 2007, 11(40): 27-37 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
M. T. Dastorani. Evaluation of the Application of Artificial Intelligence Model for Simulation and Real – Time Prediction of Flood Flow. jwss 2007; 11 (40) :27-37
URL: http://jstnar.iut.ac.ir/article-1-684-en.html
URL: http://jstnar.iut.ac.ir/article-1-684-en.html
Abstract: (24133 Views)
The potential of artificial neural network models for simulating the hydrologic behaviour of catchments is presented in this paper. The main purpose is the modeling of river flow in a multi-gauging station catchment and real time prediction of peak flow downstream. The study area covers the Upper Derwent River catchment located in River Trent basin. The river flow has been predicted (at Whatstandwell gauging station) using upstream measured data. Three types of ANN were used for this application: Multi-layer perceptron, Recurrent and Time lag recurrent neural networks. Data with different lengths (1 month, 6 months and 3 years) have been used, and flow with 3, 6, 9 and 12 hours lead-time has been predicted. In general, although ANN shows a good capability to model river flow and predict downstream discharge by using only upstream flow data, however, the type of ANN as well as the characteristics of the training data was found as very important factors affecting the efficiency of the results.
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
