Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Wien
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Standardized Precipitation Index, Regression, Network
Kaynak
Theoretical and Applied Climatology
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
155
Sayı
3