Tuğrul, TürkerSelek, BülentHınıs, Mehmet AliSelek, ZelihaOruç, Sertaç2025-09-192025-09-19202500334553https://doi.org/10.1007/s00024-025-03800-4https://hdl.handle.net/20.500.12451/14502Drought is not only a problem that challenges scientists but also one of the most difficult natural disasters to combat for local governments and decision-makers. Like many parts of the world suffering from drought, the western Mediterranean region of Turkey is also affected by drought. In this study, innovative drought prediction models were created with different machine learning algorithms and deep learning methods to create a model that will help decision-makers regarding drought. 4 different monthly lagged model structures were established using SPI12 values calculated with precipitation data between 1967 and 2020 for the Acipayam, Bodrum and Fethiye regions located in the west of Turkey. While providing data, attention was paid to the distance between stations and data continuity. The models were analyzed with Long-Short Term Memory (LSTM), Artificial Neural Network (ANN) and Random Forest (RF) algorithms. In addition, Discrete Wavelet Transform (WT) was used to obtain better model results. The hyper-parameters of these algorithms were determined by taking into account the most commonly used parameters in the literature. The analysis results were evaluated by correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe Efficiency (NSE), and Combined Accuracy (CA). As a result of the comparison of these methods, the best results were obtained in the M01 model of LSTM, LSTM-WM01, for Acipayam after WT (r: 0.9910, NSE: 0.9733, RMSE: 0.1637, and CA: 0.1030). While the best prediction for Bodrum was obtained in LSTM-WM02 after WT (r:0.9657, NSE:0.9325, RMSE:0.3101, and CA: 0.1929), for Fethiye it was obtained in LSTM-WM02 having performance metrics r:0.9539, NSE:0.8973, RMSE:0.3689, and CA: 0.2359. It is expected that the results obtained with this study will help researchers and decision-making authorities on drought.eninfo:eu-repo/semantics/openAccessANNDroughtLSTMRFSoft ComputingSPIArticle10.1007/s00024-025-03800-42-s2.0-105014758160Q2WOS:001560293200001Q3