Ahmad, R., Younes, A. (2023). Prediction of Daily Flow to the Great Zab River Using Artificial Neural Network Models. , 28(2), 163-172. doi: 10.33899/rengj.2023.137478.1219
Ryam Younus Ahmad; Abdel Wahab Mohammad Younes. "Prediction of Daily Flow to the Great Zab River Using Artificial Neural Network Models". , 28, 2, 2023, 163-172. doi: 10.33899/rengj.2023.137478.1219
Ahmad, R., Younes, A. (2023). 'Prediction of Daily Flow to the Great Zab River Using Artificial Neural Network Models', , 28(2), pp. 163-172. doi: 10.33899/rengj.2023.137478.1219
Ahmad, R., Younes, A. Prediction of Daily Flow to the Great Zab River Using Artificial Neural Network Models. , 2023; 28(2): 163-172. doi: 10.33899/rengj.2023.137478.1219

Prediction of Daily Flow to the Great Zab River Using Artificial Neural Network Models

Al-Rafidain Engineering Journal (AREJ)
Article 15, Volume 28, Issue 2, September 2023, Pages 163-172    PDF (1.26 M)
Document Type: Research Paper
DOI: 10.33899/rengj.2023.137478.1219
Authors
Ryam Younus Ahmad* ; Abdel Wahab Mohammad Younes
Dams and Water Resources Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq
Abstract
The daily flow of rivers is one of the most important components of the hydrological cycle and plays an important role in the planning and management of various water resources projects, as the process of predicting such flow is very necessary in the operation of reservoirs, planning to prevent flooding and estimating water abundance or scarcity. This study aims to use two types of neural network models to predict the daily flow to the Great Zab river basin in the Northern Iraq region for the period (2012- 2021). Two types of Artificial Neural Networks (ANNs) are investigated and evaluated for flow forecasting of river. The first one is the Feed Forward Back Propagation (FFBP), and the second is the Multi-Layer Perceptron Neural Network (MLP). Data has been analyzed by comparing the simulation outputs delivered by models with two performance indices named as (a) correlation coefficient and root mean square errors, which can be denoted by (R^2) and (RMSE) respectively. The results showed that the neural network MLP structured (3-14-7-1), able to predict the daily flows in Eski-kelek station on correlation coefficient and root mean squared-errors (0.91, 51.7), respectively.
Keywords
flow prediction; daily flow of river; artificial neural networks; MLP; FFBP
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