Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study)
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,...
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| Format: | Article |
| Language: | English |
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University of Diyala
2014-12-01
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| Series: | Diyala Journal of Engineering Sciences |
| Subjects: | |
| Online Access: | https://djes.info/index.php/djes/article/view/438 |
| _version_ | 1811183230158635008 |
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| author | ABBAS M. ABD SAAD SH. SAMMEN |
| author_facet | ABBAS M. ABD SAAD SH. SAMMEN |
| author_sort | ABBAS M. ABD |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-04-11T09:44:20Z |
| format | Article |
| id | doaj.art-974d4909dfc74504b84eb44d0864677f |
| institution | Directory Open Access Journal |
| issn | 1999-8716 2616-6909 |
| language | English |
| last_indexed | 2024-04-11T09:44:20Z |
| publishDate | 2014-12-01 |
| publisher | University of Diyala |
| record_format | Article |
| series | Diyala Journal of Engineering Sciences |
| spelling | doaj.art-974d4909dfc74504b84eb44d0864677f2022-12-22T04:31:07ZengUniversity of DiyalaDiyala Journal of Engineering Sciences1999-87162616-69092014-12-017410.24237/djes.2014.07409Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study) ABBAS M. ABD0SAAD SH. SAMMEN 1College of Engineering, Diyala University College of Engineering, Diyala University 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. https://djes.info/index.php/djes/article/view/438ANN modelForecastingartificial neural networkreservoir inflow |
| spellingShingle | ABBAS M. ABD SAAD SH. SAMMEN Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study) Diyala Journal of Engineering Sciences ANN model Forecasting artificial neural network reservoir inflow |
| title | Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study) |
| title_full | Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study) |
| title_fullStr | Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study) |
| title_full_unstemmed | Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study) |
| title_short | Artificial Neural Network Model for Managing and Forecasting Water Reservoir Discharge (Hemren Reservoir as A Case Study) |
| title_sort | artificial neural network model for managing and forecasting water reservoir discharge hemren reservoir as a case study |
| topic | ANN model Forecasting artificial neural network reservoir inflow |
| url | https://djes.info/index.php/djes/article/view/438 |
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