An Experimental Design Frame for Active Dam Reserve Ratio Forecasting Using Neural Networks

Open Access

Issue		E3S Web Conf. Volume 566, 2024 2024 6^th International Conference on Environmental Sciences and Renewable Energy (ESRE 2024)


Article Number		01006
Number of page(s)		8
Section		Wastewater Treatment and Water Resource Management
DOI		https://doi.org/10.1051/e3sconf/202456601006
Published online		06 September 2024

Turkish General Directorate of Meteorology, e-library (mgm.gov.tr), “https://www.mgm.gov.tr/genel/hidrometeoroloji.aspx?s=3#:~:text=D%C3%BCnyam%C4%B1z%C4%B1n%202%2F3%20%C3%BC%20su,tabakalarda%20yeralt%C4%B1%20suyu%20olarak%20bulunur” [Access: 22.03.2024] (In Turkish) [Google Scholar]
S. Corba, Danger for Future: Water Scarcity, Bilim Genc, The Scientific and Technological Research Council of Türkiye, (2021) “https://bilimgenc.tubitak.gov.tr/makale/gelecekteki-tehlike-su-kitligi#:~:text=Bu%20de%C4%9Ferin%201.000%20m3,olan%20%C3%BClkeler%20aras%C4%B1nda%20yer%20al%C4%B1yor” [Access: 22.03.2024] (In Turkish). [Google Scholar]
A. Altin, Modelling Water Amount Entering Dodurga Dam Using Box-Jenkins Technique, Eskişehir Osmangazi University Engineering Architecture Faculty J. 20(1), 81-100. (2007). (In Turkish) [Google Scholar]
D.H. Bae, D.M. Jong, G. Kim, Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique, Hydrological Sciences Journal, 52(1), 99-113, (2007). [CrossRef] [Google Scholar]
K.M.S. Santos, A.B. Celeste, A. El-Shafie, ANNs and inflow forecast to aid stochastic optimization of reservoir operation, Journal of Applied Water Engineering and Research, 7(4), 314-323, (2019). [CrossRef] [Google Scholar]
P.P. Hadiyan, R. Moeini, E. Ehsanzadeh, Application of static and dynamic artificial neural networks for forecasting inflow discharges, case study: Sefidroud Dam reservoir, Sustainable Computing: Informatics and Systems, 7, 100401, (2020). [CrossRef] [Google Scholar]
Z.C. Herbert, Z. Asghar, C.A. Oroza, Long-term Reservoir Inflow Forecasts: Enhanced Water Supply and Inflow Volume Accuracy Using Deep Learning, Journal of Hydrology, 601 (2021). [Google Scholar]
M. Yousefi, X. Cheng, M. Gazzea, A.H. Wierling, J. Rajasekharan, A. Helseth, H. Farahmand, R. Arghandeh, Day-ahead inflow forecasting using causal empirical decomposition, Journal of Hydrology, 613, (2022). [Google Scholar]
M.A. Akgul, M. Cetin, Forecasting Dam Reserve Level Using Landsat 8 Sattelite Images, Dicle Unviversity Journal of Engineering 9(1), 493–502, (2018). (In Turkish) [Google Scholar]
X. Zhang, P. Liu, Y. Zhao, C. Deng, Z. Li, M. Xiong, Error correction-based forecasting of reservoir water levels: Improving accuracy over multiple lead times, Environmental Modelling & Software 104, 27-39, (2018). [CrossRef] [Google Scholar]
H. Arslan, F. Unes, M. Demirci, B. Tasar, A. Yilmaz, Estimation of Keban Dam Lake Level Change Using ANFIS and Support Vector Machines, Osmaniye Korkut Ata University Journal of Natural Sciences Institute, 3(2), 71-77, (2020). (In Turkish) [Google Scholar]
T. Temiz, Y. Damla, E. Keskin, Comparison of Different Artificial Neural Network Methods in Determining Reservoir Capacity, Erzincan University Journal of Science and Technology, 15(1), 183-203, (2022). [Google Scholar]
S.F. Stefenon, L.O. Seman, L.S. Aquino, L.S. Coelho, Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants, Energy, 274, 127350, (2023). [CrossRef] [Google Scholar]
S. Filiz, S. Cevik, P. Mizrak Ozfirat. Dam Reserve Ratio Estimation, in Abstract Book of 5th. National University Industry Cooperation Research and Development Congress, 12-13 December 2023, Manisa, Turkiye, (2023). (In Turkish) [Google Scholar]
A.A. Semenoglou, E. Spiliotis, V. Assimakopoulos. Data augmentation for univariate time series forecasting with neural networks, Pattern Recogn. 134, 1–12, (2022). [Google Scholar]
E. Egrioglu, E. Bas, M.Y. Chen. Recurrent dendritic neuron model artificial neural network for time series forecasting, Inf. Sci. 607, 572–584, (2022). [CrossRef] [Google Scholar]
D. Ari, P. Mizrak Ozfirat. Comparison of artificial neural networks and regression analysis for airway passenger estimation, Journal of Air Transport Management 115, 102553, (2024). [CrossRef] [Google Scholar]
IZSU, (www.izsu.gov.tr), “https://www.izsu.gov.tr/tr/BarajlarinSuDurumu/1” [Access: 21.03.2024] (In Turkish). [Google Scholar]

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