Artificial neural network model for predicting water inflow into a reservoir

RELEVANCE of this study lies in the use of an artificial neural network to predict the volume of water in the Coca reservoir (Coca hydroelectric power station in Ethiopia). As you know, hydropower, being renewable energy, is one of the technologies that produce electricity with the least impact on global climate change. During this time, Ethiopia received about 87% (4,674 MW) of electricity from hydropower. It is one of the countries affected by the problems of climatic phenomena, such as floods, droughts and hurricanes, which affect the potential of hydropower. THE PURPOSE. In order to maintain safe operation, good production efficiency, better water resources management, effective decision-making, accident prevention and early warning and restrictions on electricity production, water volume forecasting is necessary. Which, in turn, is a nonlinear problem, and a multilinear perceptron-type neural network (MLP) is suitable for this purpose. METHODS. In this study, different models with different selected number of nodes and layers were identified, since there is no specific rule for determining the architecture of an artificial neural network. Statistical analysis (mean square error (MSE) and R-squared (R2)) was used to verify the validity of the model by comparing the actual values of water inflow with the predicted values. results. The inflow prediction was carried out using the ANN method based on a multilayer perceptron (MLP). The performance of each model was evaluated using the mean square error (MSE) and efficiency coefficient (R2), which are among the most commonly used statistical methods in hydrological modeling. CONCLUSION. The results obtained show that the models successfully predicted flood runoff over the reservoir.

1. Chabi, Amedée, et al. "Assessment of Water Balance in the Ilauko Reservoir of the Ouémé at Savè Catchment (Benin, West Africa)." (2019).

2. Zhou, Wugang, et al. "Estimation of reservoir inflow with significant lateral inflow by using the adjoint equation method." Journal of Hydrology 574 (2019): 360-372.

3. Song, Jung‐Hun, Younggu Her, and Moon‐Seong Kang. "Estimating Reservoir Inflow and Outflow from Water Level Observations Using Expert Knowledge: Dealing With an Ill‐Posed Water Balance Equation in Reservoir Management." Water Resources Research 58.4 (2022): e2020WR028183.

4. Golob R., Shtokel T., Grgic D. Forecasting water inflow based on neural networks // Engineering management practice. – 1998. – T. 6. – No. 5. – pp. 593-600.

5. Gurara, Mikiyas, Addis Bekele, and Selamawit Gebeyehu. "Hydrological Data Processing and Management System."- International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181- Vol. 9 Issue 05, May-2020

6. Haykin S. Neural networks: MacMillan College Publ //Co., New York. – 1994.

7. TEMİZ, Temel, Yunus DAMLA, and Erdinç KESKİN. "Comparison of Different Artificial Neural Network Methods in Determining Reservoir Capacity." Erzincan University Journal of Science and Technology 15.1 (2022): 183-203.

8. Keskenler M. F., Keskenler E. F. From past to present artificial neural networks and history //Takvim-i Vekayi. – 2017. – Т. 5. – №. 2. – С. 8-18.

9. Shilin, A. N. Application of artificial neural networks for forecasting accidents of overhead power lines during the icy period / A. N. Shilin, A. A. Shilin, S. S. Dementyev // Elektro. Electrical engineering, electrical power engineering, electrical industry. – 2016. – No. 2. – P. 15-21. – EDN VVKJSP.

10. Khaikin S. Neural networks: a complete course / ed. N.N. Kussul; lane from English N.N. Kussul, A.A. Shelestova. 2nd ed., rev. M.: Williams, 2006. 1104 p.

11. Borodin, Georgy Dmitrievich. «Brief overview and classification artificial neural Networks» News of Tula State University. Technical Sciences 11 (2021): 45-53

12. Osovsky, Stanislav. Neural networks for information processing. Finance and statistics, 2004.

13. Yasnitsky, Leonid Nakhimovich. "Introduction to Artificial Intelligence." (2008).

14. Jais, Imran Khan Mohd, Amelia Ritahani Ismail, and Syed Qamrun Nisa. "Adam optimization algorithm for wide and deep neural network." Knowledge Engineering and Data Science 2.1 (2019): 41- 46.

15. Chicco, Davide, Matthijs J. Warrens, and Giuseppe Jurman. "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation." PeerJ Computer Science 7 (2021): e623.