Open AccessArtificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)
Author(s) -
M. Abbas,
Saad Sh. Sammen
Publication year - 2014
Publication title -
mağallaẗ diyālá li-l-ʿulūm al-handasiyyaẗ/mağallaẗ diyālá li-l-ʻulūm al-handasiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2616-6909
pISSN - 1999-8716
DOI - 10.24237/djes.2014.07409
Subject(s) - inflow , artificial neural network , mean squared error , correlation coefficient , computer science , test data , process (computing) , early stopping , hydrology (agriculture) , statistics , artificial intelligence , machine learning , meteorology , engineering , mathematics , geotechnical engineering , physics , programming language , operating system
The prediction of different hydrological phenomenon (or system) plays an increasing role in the management of water resources. As engineers; it is required to predict the component of natural reservoirs’ inflow for numerous purposes. Resulting prediction techniques vary with the potential purpose, characteristics, and documented data. The best prediction method is of interest of experts to overcome the uncertainty, because the most hydrological parameters are subjected to the uncertainty. Artificial Neural Network (ANN) approach has adopted in this paper to predict Hemren reservoir inflow. Available data including monthly discharge supplied from DerbendiKhan reservoir and rain fall intensity falling on the intermediate catchment area between Hemren-DerbendiKhan dams were used.A Back Propagation (LMBP) algorithm (Levenberg-Marquardt) has been utilized to construct the ANN models. For the developed ANN model, different networks with different numbers of neurons and layers were evaluated. A total of 24 years of historical data for interval from 1980 to 2004 were used to train and test the networks. The optimum ANN network with 3 inputs, 40 neurons in both two hidden layers and one output was selected. Mean Squared Error (MSE) and the Correlation Coefficient (CC) were employed to evaluate the accuracy of the proposed model. The network was trained and converged at MSE = 0.027 by using training data subjected to early stopping approach. The network could forecast the testing data set with the accuracy of MSE = 0.031. Training and testing process showed the correlation coefficient of 0.97 and 0.77 respectively and this is refer to a high precision of that prediction technique.