Document Type : Research Paper

Authors

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.

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Main Subjects

  1. A. A. Kassem, A. M. Raheem, and K. M. Khidir, “Daily Streamflow Prediction for Khazir River Basin Using ARIMA and ANN Models,” Zanco J. Pure Appl. Sci., vol. 32, no. 3, 2020, doi: 10.21271/zjpas.32.3.4.
  2. M. Ş. Güneş, C. Parim, D. Yıldız, and A. H. Büyüklü, “Predicting monthly streamflow using a hybrid wavelet neural network: Case study of the Çoruh river basin,” Polish J. Environ. Stud., vol. 30, no. 4, pp. 3065–3075, 2021, doi: 10.15244/pjoes/130767.
  3. H. Herawati, Suripin, and Suharyanto, “River flow modeling using artificial neural networks in Kapuas river, West Kalimantan, Indonesia,” AIP Conf. Proc., vol. 1903, no. November, 2017, doi: 10.1063/1.5011620.
  4. P. Mittal, S. Chowdhury, S. Roy, N. Bhatia, and R. Srivastav, “Dual Artificial Neural Network for Rainfall-Runoff Forecasting,” J. Water Resour. Prot., vol. 04, no. 12, pp. 1024–1028, 2012, doi: 10.4236/jwarp.2012.412118.
  5. T. A. Awchi, “River discharges forecasting in northern Iraq using different ANN techniques,” Water Resour. Manag., vol. 28, no. 3, pp. 801–814, Feb. 2014, doi: 10.1007/s11269-014-0516-3.
  6. K. A. Al-Mohseen and A. R. M. Towfeeq, “Artificial Neural Network for Single Reservoir Operation,” AL-Rafdain Eng. J., vol. 22, no. 2, pp. 29–37, 2014, doi: 10.33899/rengj.2014.87313.
  7. A. M. Atiaa, “PREDICTION OF RIVER DISCHARGE USING ARTIFICIAL NEURAL NETWORKS : AN EXAMPLE OF GHARRAF RIVER , SOUTH OF IRAQ Study area,” vol. 50, no. 2, pp. 200–205, 2009.
  8. N. Pramanik and R. K. Panda, “Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction,” Hydrol. Sci. J., vol. 54, no. 2, pp. 247–260, 2009, doi: 10.1623/hysj.54.2.247.
  9. L. E. Besaw, D. M. Rizzo, P. R. Bierman, and W. R. Hackett, “Advances in ungauged streamflow prediction using artificial neural networks,” J. Hydrol., vol. 386, no. 1–4, pp. 27–37, May 2010, doi: 10.1016/j.jhydrol.2010.02.037.
  10. S. Londhe and S. Charhate, “Comparaison de techniques de modélisation conditionnée par les données pour la prévision des débits fluviaux,” Hydrol. Sci. J., vol. 55, no. 7, pp. 1163–1174, 2010, doi: 10.1080/02626667.2010.512867.
  11. S. K. Patil and S. S. Valunjkar, “PREDICTION OF DAILY RUNOFF USING,” vol. 14, pp. 241–245.
  12. T. A. Awchi, “River discharges forecasting in northern Iraq using different ANN techniques,” Water Resour. Manag., vol. 28, no. 3, pp. 801–814, 2014, doi: 10.1007/s11269-014-0516-3.
  13. G. Ammar and B. Haidar, “‫Using Neural Networks models with Wavelet transform technology To Predict Flows Coming into 16 Tishreen Lake", Tishreen University Journal for Research ,39 .and Scientific Studies vol
  14. A. P. Engelbrecht, “Comp_Intelligence.” p. 630, 2007.
  15. R. M. A. Q. Bashi, O. M. A. M. Agha, and A. W. M. Younes, “Forecasting the Reconnaissance Drought Index (RDI) Using Artificial Neural Networks (ANNs),” vol. 27, no. 2, pp. 140–155, 2022.
  16. H. Y. Dalkiliç and S. A. Hashimi, “Prediction of daily streamflow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models,” Water Sci. Technol. Water Supply, vol. 20, no. 4, pp. 1396–1408, Jun. 2020, doi: 10.2166/ws.2020.062.
  17. K. A. Abdulmuhsin and I. A. Al-Ani, “Using of Learning Vector Quantization Network for Pan Evaporation Estimation,” Tikrit J. Eng. Sci., vol. 16, no. 2, pp. 43–50, 2009, doi: 10.25130/tjes.16.2.07.
  18. D. Liu, W. Jiang, L. Mu, and S. Wang, “Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River,” IEEE Access, vol. 8, pp. 90069–90086, 2020, doi: 10.1109/ACCESS.2020.2993874.