Akademik Veri Yönetim Sistemi
PURE AND APPLIED GEOPHYSICS, 2025 (SCI-Expanded, Scopus)
Accurately estimating evaporation in reservoir systems is an essential step in creating a water budget, as this information is crucial for the effective management of water resources, particularly in countries experiencing water stress. This investigation aims to test the success of tree-based (random forest, gradient boosting, extreme gradient boosting, adaptive boosting, and M5 prime) and neural network-based (multi-layer perceptron (MLP), Kolmogorov Arnold network (KAN), recurrent neural network, long short-term memory, and gated recurrent unit) methods, to estimation monthly evaporation at very important reservoir called Boukourdane Dam, which is located in a Mediterranean area in Algerian north. The KAN method was used for the first time in evaporation prediction. Data on minimum and maximum temperatures (Tmax, degrees C, Tmin, degrees C), wind speed (U, km/h), and relative humidity (H, %) between 1996 and 2016 were used as inputs to the models. Using lag values of the input data significantly increased the accuracy of the models. Although the applied machine learning models generally gave higher accuracy in predicting evaporation, neural network-based methods gave better results than tree-based ones. Although neural network-based methods give close results to each other, the MLP is the method that produces the best results for the test set. The most significant advantage of the KAN method, which consistently produces satisfactory results, is that it provides a clear and straightforward equation. Explainable artificial intelligence graphs showed that Tmax is the most effective parameter in evaporation estimation. The study results will provide convenience to decision-makers for efficient dam operation.