Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Development and Validation of Regression Model via Machine Learning to Estimate Thermal Conductivity and Heat Flow Using Igneous Rocks from the Dikili-Bergama Geothermal Region, Western Anatolia(Pergamon-Elsevier Science Ltd, 2026) Ayzit, Tolga; Sahin, Onur Gungor; Erol, Selcuk; Baba, AlperThermal conductivity is a fundamental parameter that significantly influences the thermal regime of the lithosphere. It plays a crucial role in a variety of geological applications, including geothermal energy exploration, igneous system assessment, and tectonic modeling. In this study, a machine learning approach is used to predict the thermal conductivity of igneous rocks based on the composition of major oxides. A total of 488 samples from different regions of the world were analyzed. The thermal conductivity values ranged from 1.20 to 3.74 Wm(-1) K-1 and the mean value was 2.61 Wm(-1) K-1. The Random Forest (RF) algorithm was used, resulting in a high coefficient of determination (R-2 = 0.913 for training and R-2 = 0.794 for testing) and a root mean square error (RMSE) of 0.112 and 0.179, respectively. Significance analysis of the traits identified SiO2 (>40 %), Na2O (>15 %) and Al2O3 (>10 %) as the most influential predictors. The study presented results from the Western Anatolia region, where felsic rocks had the highest thermal conductivity (mean = 2.69 Wm(-)(1)K(-)(1)) compared to mafic (mean = 2.34 Wm(-)(1)K(-)(1)) and ultramafic rocks (mean = 2.39 Wm(-)(1)K(-)(1)). In addition, the study evaluated the predictive capabilities of machine learning models for the igneous rocks of the Dikili-Bergama region and compared the results with those of saturated models. Using these data, we calculated heat flow values of up to 400 mWm(-2) under saturated conditions in western Anatolia. These results highlight the value of integrating geochemical data with machine learning to improve geothermal resource exploration and lithospheric modeling.Article Citation - WoS: 1Derivation of Soil Hydraulic Properties (SHPS) Using a Physics-Based Inverse Calibration Method and International Soil Moisture Network Database(Elsevier, 2025) Sahin, Onur Gungor; Gunduz, OrhanThis study used extensive soil moisture records to estimate "inverse-calibrated Soil Hydraulic Properties (SHPs)" using a multi-processing technique via high-performance computing clusters. Within this objective, a mass conservative numerical model was developed to solve the one-dimensional Richards Equation incorporating two different soil hydraulic models: the well-known van Genuchten Mualem (VGM) model and the relatively new Fredlund-Xing-Wang (FXW). A multiprocessing version of the Differential Evolution Algorithm (DEA) optimization technique was used for inverse calibration of the soil hydraulic parameters. For FXW, calibration statistics were calculated as means of the KGE' (0.89 f 0.1 and 0.83 f 0.23), R (0.89 f 0.1 and 0.85 f 0.21) and ubRMSE (0.017 f 0.01 and 0.015 f 0.02) for the depths 50 and 100 cm, respectively. For VGM, calibration statistics were found as means of the KGE' (0.87 f 0.11 and 0.78 f 0.22), R (0.90 f 0.08 and 0.86 f 0.17) and ubRMSE (0.019 f 0.01 and 0.017 f 0.01) for the same depths, respectively. The employed methodology had highly promising statistical performance for both FXW and VGM to derive SHPs. A comprehensive validation methodology was used to evaluate the reliability of derived SHPs. Correlation analysis showed that derived SHPs strongly correlated with the soil properties and environmental variables. Further, as a validation procedure, initial investigations were also conducted to explore the spatial transferability of the parameters. Despite the use of basic k-means clustering, the resulting soil hydraulic datasets showed statistical similarity or even improvement to hyper-resolution maps used in the literature. While the simulation model of the methodology has certain assumptions and limitations, this study proves that the ISMN database can be used to derive soil hydraulic properties and transfer these parameters to locations other than the calibration points. This study shows that FXW is a promising hydraulic model for the determination of soil moisture at root zone within the complete moisture range. The methodology can also be readily extended to other established soil moisture monitoring networks and potentially extended versions of "inverse-calibrated SHPs" and trained pedotransfer functions are considered to be valuable tools to estimate soil moisture profiles at the root zone.Article Citation - WoS: 8Citation - Scopus: 9Comprehensive Comparison of Different Gridded Precipitation Products Over Geographic Regions of Türkiye(Spie-soc Photo-optical instrumentation Engineers, 2024) Khorrami, Behnam; Sahin, Onur Gungor; Gunduz, OrhanThe traditionally used rain gauge stations provide the most reliable data on the spatiotemporal distribution of precipitation; however, they are limited in space and time. As an alternative to field observations, the gridded precipitation products (GPPs) offered by remote sensing missions are widely used. On account of the uncertainties associated with the GPPs, they have to be quality-checked for confidence in application over the region of interest. Although accuracy assessment of precipitation data is a common task, there is a gap in the literature regarding a comprehensive assessment of the currently available GPPs. In this study, 14 GPPs were used to investigate their performance in catching the spatio-temporal characteristics of precipitation over geographic regions of T & uuml;rkiye. According to the results, integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG), multi-source weighted-ensemble precipitation (MSWEP), and Tropical Rainfall Measuring Mission (TRMM) show better performance on monthly and annual scales while on the climatology scale, CHELSA, Climate Hazards Group Infrared Precipitation with Station, ERA5, and ERA5-Land also manifest better performance. The mean monthly correlation over Aegean Region (AEG), Marmara Region (MAR), Central Anatolian Region (CAR), Mediterranean Region (MED), Black Sea Region (BSR), East Anatolian Region (EAR), and South East Anatolian Region (SEA) are 0.77, 0.81, 0.77, 0.80, 0.79, 0.77, and 0.77, respectively. The annual assessment suggests that over the MAR, CAR, MED, and SEA, the IMERG mission performs very well. While TRMM showcases its best performance in the AEG, MED, BSR, and EAR, MSWEP performs well in the BSR and SEA Region. Overall, taking the country-average results into account, it can be stated that among the used GPPs, TRMM, MSWEP, and IMERG yield the best results for T & uuml;rkiye as a whole.