Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ2/r/bayburt/11185
  Depth-dependent predictability of soil temperature under Mediterranean climatic conditions: a frequency-based perspective

  Guleryuz, Didem; Yıldırım, Ümit; Güler, Cüneyt

  Soil-temperature predictability is inherently depth-dependent because atmospheric forcing is progressively attenuated and transformed into smoother, lower-frequency thermal dynamics with increasing depth. Under Mediterranean climatic conditions, where pronounced seasonality and strong surface forcing coexist, this depth-dependent transformation provides an informative basis for evaluating model behaviour. This study examines soil-temperature prediction at depths of 5, 10, 20, 50, and 100 cm using a multivariate dataset comprising 5,285 retained daily observations from the Mersin meteorological station on Türkiye’s eastern Mediterranean coast. Precipitation, air temperature, relative humidity, wind speed, and evaporation were used as predictors. Support vector regression, random forest, and LightGBM were evaluated using R², RMSE, and MAE. All three models performed strongly in the near-surface layers, with test R² values ranging from 0.948 to 0.954 at depths of 5–20 cm. Predictive performance declined with increasing depth as short-period variability weakened and the thermal signal became smoother and more seasonally structured. RF and LightGBM showed a greater numerical decline at 50–100 cm, whereas SVR achieved test R² values of 0.923 at 50 cm and 0.847 at 100 cm; however, inter-model differences remained modest and were interpreted descriptively. Lomb–Scargle spectral analysis, applied using the actual observation dates, indicated that short-period variability became relatively less pronounced with depth while seasonal-scale and low-frequency components remained prominent. Overall, the findings suggest that soil-temperature prediction should be interpreted in relation to depth-dependent temporal signal characteristics rather than as a search for a universally superior algorithm.

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• MakaleYalnız künye2024Scopus SJRQ2/r/bayburt/11605
  Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin

  Niazkar, Majid; Piraei, Reza; Eryılmaz Türkkan, Gökçen; Hırca, Tuğçe; 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.

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  AtıfScopus24·WoS23·OpenAlex25

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• MakaleAçık Erişim2022Scopus SJRQ2/r/bayburt/13276
  Estimation of soil temperatures with machine learning algorithms—Giresun and Bayburt stations in Turkey

  Guleryuz, Didem

  Since soil temperature (ST) is one of the most critical determinants affecting the soil’s physical and chemical properties, the studies on soil temperature estimation increase with the widespread use of deep learning and machine learning algorithms. This study estimates soil temperature at four depths for Giresun and Bayburt stations in Turkey employing the Bayesian Tuned Gaussian Process Regression (BT-GPR), Bayesian Tuned Support Vector Regression (BT-SVR), and Long Short Term Memory (LSTM) models. The stations were selected from semiarid (Bayburt station) and very humid (Giresun station) climates to compare the models’ performance and measure their applicability in different climate classes. Common meteorological indicators were determined as input parameters in the developed models, and a five-and-a-half-year daily dataset was used for all models. This paper represents a novel scheme to optimize the hyperparameters of kernel functions for GPR and SVR models using the Bayesian optimization method to expand predictive efficiency. The developed GPR and SVR models’ outputs are compared with LSTM via three statistical metrics comprising the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The results show that the BT-GPR model has a superior estimation ability than other developed models for the two stations. The daily ST estimation with the highest accuracy was obtained at a 5-cm depth using BT-GPR at Giresun station (RMSE = 0.0439, R2 = 0.9535, MAE = 0.0344 in the testing phase) and Bayburt station (RMSE = 0.0525, R2 = 0.9438, MAE = 0.0412 in the testing phase). These outcomes provide helpful benchmarking guidance for future soil temperature investigation at various depths across the selected regions.

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  AtıfScopus12·WoS14·OpenAlex21

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• MakaleYalnız künye2022Scopus SJRQ2/r/bayburt/12191
  Applications of innovative polygonal trend analyses to precipitation series of Eastern Black Sea Basin, Turkey

  Hırca, Tuğçe; Eryılmaz Türkkan, Gökçen; Niazkar, Majid

  Examining historical variations of hydroclimatic variables can provide crucial information about changes of water resources in a water cycle. In this study, the Mann–Kendall (MK) and Innovative Polygon Trend Analysis (IPTA) methods were applied to 56-year precipitation data collected at 8 measuring stations. These stations are located in Eastern Black Sea Basin (EBSB), which has a significant amount of annual precipitation and hydroelectric potential in Turkey. This study has two objectives: (1) investigating possible changes in the monthly precipitation and (2) comparing the results achieved by a classical (MK) and one of the latest trend analysis methods presented in the literature (IPTA). Based on the results, MK achieved no trend for most of months, while it reached an increasing trend for March at most of the stations. Likewise, IPTA determined an increasing trend for March precipitation. However, an increasing/decreasing trend was obtained by IPTA for most of the months and stations. In other words, comparing the trend analysis results obtained by IPTA and MK indicates a significant discrepancy between the numbers of months with detected trends primarily because the former is relatively more sensitive in trend identification. To be more precise, IPTA and MK determined trends in approximately 81.25% and 12.5% of all months, respectively. Furthermore, the former identified quite the same trends in every month which the latter reported a trend. Moreover, the polygon of the mean and standard deviation graphs developed by IPTA provides a year cycle, which brings about useful information for water utility sectors and decision makers of the study area. Finally, the findings of this study contribute to a large amount of research that attempts to explore spatio-temporal variations of hydroclimatic variables around the globe not only to enhance humans’ knowledge about changes in a water cycle but also assess climate change impacts.

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  AtıfScopus54·WoS50·OpenAlex64

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