Browsing by Author "Cavur, Mahmut"

Now showing 1 - 4 of 4

Citation - WoS: 1
Citation - Scopus: 2
An Evaluation of AI Models' Performance for Three Geothermal Sites
(Mdpi, 2024) Çavur, Mahmut; Cavur, Mahmut; Yu, Yu-Ting; Duzgun, H. Sebnem
Current artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites-Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals.
Citation - WoS: 0
Citation - Scopus: 0
İżnik Town and Its Rural Landscape: Decision Making, Socio-Demographic Profiling and Conservation Policy Development
(Routledge Journals, Taylor & Francis Ltd, 2024) Songulen, Nazli; Erkan, Yonca; Reis, Amine Seyhun Alkan; Çavur, Mahmut; Guvenc, Murat; Erkan, Yonca; Cavur, Mahmut
In light of recent advances in landscape conservation, this study introduces a profiling model that provides context-sensitive heritage conservation strategies. The model is adaptable and focuses on socio-demographic profiling of a rural landscape. It uses Iznik (ancient Nicaea) town, a UNESCO World Heritage candidate, and the surrounding rural landscape as a case study area. The model captures the intricate interplay between the socio-demographic conditions of agriculture-based local communities and rural heritage, offering policy options to enhance community well-being and conserve rural heritage. Based on the complementary use of Cluster and Multiple Correspondence Analysis, the model employs multi-layered analysis of quantitative and qualitative data. The model identifies six distinct clusters, revealing the vulnerability and resilience of rural settlements around Iznik town, and the priority sites where rural heritage and local populations face immediate threat. Fostering a symbiotic relationship between data-driven insights and locally informed policies, this model generates evidence-based, people-centred policy outputs for heritage conservation, which may be applicable in a variety of contexts.
Citation - WoS: 3
Citation - Scopus: 3
Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms
(Mdpi, 2024) Cavur, Mahmut; Çavur, Mahmut; Yu, Yu-Ting; Demir, Ebubekir; Duzgun, Sebnem
Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.
Citation - WoS: 6
Citation - Scopus: 10
Rssi-Based Hybrid Algorithm for Real-Time Tracking in Underground Mining by Using Rfid Technology
(Elsevier, 2022) Cavur, Mahmut; Çavur, Mahmut; Demir, Ebubekir
Knowing the precise and real-time location of underground mining workers is essential for their health and safety in any emergency. However, the standard Global Positioning System (GPS) is insufficient for such indoor environments as it requires new infrastructure based on different technologies and algorithms. Instead, Radio Frequency Identification (RFID)-based real-time indoor localization systems and a hybrid algorithm are developed. The received-signal-strength (RSS) based positioning techniques are investigated and applied in indoor environments. A unique hybrid approach based on fingerprinting is proposed and developed to solve the disadvantages of the existing techniques. Consequently, the accuracy of this one-of-a-kind algorithm is found to be 2.52 m in an office and 3.13 m in an underground mine. We also compared the proposed hybrid algorithm to the Weighted K-Nearest Neighbor (WKNN). WKNN, on the other hand, has an accuracy of 4.01 m in the office and 4.33 m in underground mining environments. (C) 2022 Elsevier B.V. All rights reserved.