Yazar "Ariman, Sema" seçeneğine göre listele

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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
    Comparison of different techniques in determining groundwater levels trends in Türkiye
    (Wiley, 2024) Kartal, Veysi; Nones, Michael; Topcu, Emre; Ariman, Sema
    Groundwater represents one of the largest resources of freshwater in the world. Thus, investigations of groundwater level variations due to climate change and increasing human activities are of great importance, especially in resource scarce regions. Our research aimed to understand the long-term effects of climate events and water use on groundwater levels over the study area via Mann-Kendall, Sen's Slope, Innovative Polygon Trend Analysis (IPTA), and Innovative Trend Analysis (ITA) analyses. Although several studies are available in relation to GWL trend analysis via ITA, Mann-Kendall and Sen slope in the literature, there are few IPTA studies conducted. The focus of the study was seven wells across T & uuml;rkiye over the period 1987-2022. Results demonstrate that there was a downward trend in GWL in all stations annually, regardless of the method. At monthly scale, a decrease was noted, especially in June, August, and September, while seasonally, decreases were seen in autumn and winter. Moreover, it was evident the results of the Sen slope and ITA were compatible, while the IPTA was a useful tool in detecting GWL trends. Identifying and understanding GWL trends is highly valuable in informing groundwater resource managers of critical areas of overuse and other factors affecting groundwater, resulting in preventive interventions to overcome such problems and protect this critical resource. GWL trends are analysed at the monthly, seasonal, and annual scales using a combination of techniques, such as Mann-Kendall, Sen's Slope, Innovative Polygon Trend Analysis (IPTA), and Innovative Trend Analysis (ITA), using data monitored between 1987 and 2022 in seven wells in Turkey. Trend analysis will allow for evaluating differences among commonly used methods in investigating GWL levels, eventually providing insights on critical areas to prioritize interventions. Besides analysing trends, a homogeneity analysis was conducted to detect change points in groundwater levels. image
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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.