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© 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Lake Water Level Estimation with Artificial Intelligence Methods.

With the decrease in water resources due to climate change, dam reservoir level estimation is important in terms of the construction, operation, design and safety of dams. In this study, average air temperature (T), relative humidity (SR), and precipitation (P) parameters were used for lake water level estimation. Thurmond Lake in McCormick County, South Carolina, USA was selected as the study area. 1286 daily data measured in real time between 2017-2023 were used as the study data. M5 Decision Tree (M5 Tree), Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models were selected for lake water level estimation and model results were compared with real observation results. In the comparison of the prediction models, performance criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used. When the model results were examined; it was determined that artificial intelligence methods performed well in predicting the lake water level change.

Details

Title
LAKE WATER LEVEL ESTIMATION WITH ARTIFICIAL INTELLIGENCE METHODS
Author
Ayar, Aysu 1 ; Üneş, Fatih 1 ; Taşar, Bestami 1 ; Varçin, Hakan 1 

 Iskenderun Technical University, Hatay – TURKEY 
Pages
115-121
Publication year
2025
Publication date
2025
Publisher
Babes Bolyai University Faculty of Geography
ISSN
2067743X
e-ISSN
23444401
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3229503304
Copyright
© 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.