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You are here : eLibrary : IAHR World Congress Proceedings : 33rd Congress - Vancouver (2009) : Topic E: Advances in Hydroinformatics for Integrated Watershed and Coast Management : An artificial neural network model, detecting spatial and temporal correlation among stations in res...
An artificial neural network model, detecting spatial and temporal correlation among stations in reservoir inflow forecasting
Author : Mohammad Ebrahim Banihabib, Farimah S. Jamali and S. Majid Mousavi
Reservoir inflow forecasting has an important role in water resources engineering. This paper provides a solution for Dez reservoir inflow forecasting using an artificial neural network model (ANN) and a multiple regression model. Moreover it aims to define the best pattern with spatial and
temporal correlation, in daily (short-time) and monthly (long-time) time scales, among stations on dam upstream, in two scenarios. According to the correlation coefficient and mean square of errors (MSE), the performances of models were compared and the ANN model was found to model the flows better. In short- time scale, best result is according to two prior days and Tangpanj station; furthermore the longest time scale with best result is one month, using all upstream stations as input data.
File Size : 200,129 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 33rd Congress - Vancouver (2009)
Article : Topic E: Advances in Hydroinformatics for Integrated Watershed and Coast Management
Date Published : 09/08/2009
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