Lake Water Level Estimation with Artificial Intelligence Methods - Aysu AYAR; Fatih ÜNEŞ; Bestami TAŞAR; Hakan VARÇIN (Aerul şi Apa: Componente ale Mediului)
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.