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    Öğe
    Climate change trends in the Southeastern Anatolia region of Turkiye: precipitation and drought
    (Iwa Publishing, 2024) Kartal, Veysi; Yavuz, Veysel Suleyman; Ariman, Sema; Kaya, Kubra; Alkanjo, Safa; Simsek, Oguz
    Drought, earthquake, flood, and fire are disasters whose effects occur after a more extended period than other disasters. Meteorological drought is called the beginning of drought types. In this study, trend analyses and temporal changes in temperature, precipitation, and drought index values were carried out between 1981 and 2022 at three meteorological observation stations in the Southeastern Anatolia Region of Turkiye. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Chinese Z Index, and Effective Drought Index methods were used for drought analysis, while Sen's slope, Mann-Kendall, and innovative trend analysis methods were used to detect the trend of precipitation. It was determined that precipitation generally tended to decrease, and drought increased since 1996. Although every type of dry and wet periods has occurred, normal dry periods were observed more. In the spatial distribution of drought, the inverse distance weighted method gives larger areas with more extreme drought and wet values than the Kriging method. The increase in extreme values in the region indicates that the severity of drought will increase. It has been determined that the region's water resources and agricultural activities are under pressure due to climate change and drought.
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    Öğe
    Prediction of groundwater drought based on hydro-meteorological insights via machine learning approaches
    (Pergamon-Elsevier Science Ltd, 2024) Kartal, Veysi; Katipoglu, Okan Mert; Karakoyun, Erkan; Simsek, Oguz; Yavuz, Veysel Suleyman; Ariman, Sema
    This study aims to predict groundwater drought-based meteorological drought index using machine learning instead of traditional approaches. Groundwater drought (GWD) was predicted using machine learning methodologies such as Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Least Squares Boosting Tree (LSBT), Generalized Linear Regression (GLR) and kNearest Neighbours (KNN). In addition, monthly, seasonal, and annual drought indices such as the Standardised Precipitation-Evapotranspiration Index (SPEI), China Z Index (CZI), Standardised Precipitation Index (SPI), ZScore Index (ZSI), Decile Index (DI), Percent of Normal Index (PNI) and Rainfall Anomaly Index (RAI) were used to analyse the drought of groundwater. These traditional drought indices were modified for the assessment of groundwater drought. Moreover, groundwater drought was predicted based on the hydro-meteorological parameters (temperature, relative humidity, wind speed, rainfall, groundwater level). The applied models' performances were evaluated via Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), R-squared (R2), Mean Bias Error (MBE), Bias Factor, and Variance Account Factor (VAF). Linear SVM is generally the best model for predicting GWD, while GLR is the second-best performing model. The KNN algorithm obtained the weakest performances. Although all types of drought and wet categories were observed, normal drought occurred more than in the other drought and wet categories. This study can contribute to the assessment of groundwater drought in regions where there is no groundwater station.