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: 2Citation - Scopus: 2Bayesian Uncertainty Quantification in Temperature Simulation of Borehole Heat Exchanger Fields for Geothermal Energy Supply(Pergamon-Elsevier Science Ltd, 2025) Mohammadi, Hesam Soltan; Ringel, Lisa Maria; Bott, Christoph; Erol, Selcuk; Bayer, PeterAccurate temperature prediction is crucial for optimizing the performance of borehole heat exchanger (BHE) fields. This study introduces an efficient Bayesian approach for improving the forecast of temperature changes in the ground caused by the operation of BHEs. The framework addresses the complexities of multi-layer subsurface structures and groundwater flow. By utilizing an affine invariant ensemble sampler, the framework estimates the distribution of key parameters, including heat extraction rate, thermal conductivity, and Darcy velocity. Validation of the proposed methodology is conducted through a synthetic case involving four active and one inactive BHE over five years, using monthly temperature changes around BHEs from a detailed numerical model as a reference. The moving finite line source model with anisotropy is employed as the forward model for efficient temperature approximations. Applying the proposed methodology at a monthly resolution for less than three years reduces uncertainty in long-term predictions by over 90%. Additionally, it enhances the applicability of the employed analytical forward model in real field conditions. Thus, this advancement offers a robust tool for stochastic prediction of thermal behavior and decision-making in BHE systems, particularly in scenarios with complex subsurface conditions and limited prior knowledge.