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Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin
(Springer Wien, 2024) Niazkar, Majid; Piraei, Reza; Turkkan, Gokcen Eryilmaz; Hirca, Tugce; Gangi, Fabiola; Afzali, Seied Hosein
This study aims to assess the Eastern Black Sea Basin drought conditions. For this purpose, the trend changes in SPI values of 6, 9, 12, and 24 months using innovative trend analysis were examined. Additionally, four machine learning models, including Multiple Linear Regression, Artificial Neural Networks, K Nearest Neighbors, and XGBoost Regressor, are employed to forecast SPI with rainfall data between 1965 and 2020 from eight rainfall stations. The input data for each model was SPI values from lead times of 1 to 6, resulting into 768 unique scenarios. The ML models estimated SPI values better as the SPI duration increased, with the 24-month SPI showing the highest accuracy. The results of SPI forecast indicated that the optimal model and number of input variables varied for each SPI and station, indicating that further studies are required to improve SPI predictions.