Çavur, Mahmut

Çavur, Mahmut

Name Variants

Çavur, Mahmut
M.,Çavur
M. Çavur
Mahmut, Çavur
Cavur, Mahmut
M.,Cavur
M. Cavur
Mahmut, Cavur
cavur, mahmut
Çavur, Mehmet
Cavur, M.

Email Address

Mahmut.cavur@khas.edu.tr

Full item page

Scholarly Output

17

Articles

12

Citation Count

0

Supervised Theses

3 Scholarly Output Search Results

Now showing 1 - 10 of 17

Citation Count: 0
İżnik Town and Its Rural Landscape: Decision Making, Socio-Demographic Profiling and Conservation Policy Development
(Routledge Journals, Taylor & Francis Ltd, 2024) Erkan, Yonca; 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.
Rssi-Based Hybrid Algorithm for Real-Time Pedestrian Tracking in Indoor Environments by Using Rfid Technology
(Kadir Has Üniversitesi, 2019) Demir, Ebubekir; Çavur, Mahmut; Çavur, Mehmet
The usage and importance of Location Based Services for indoor environments are increasing recently. The knowledge of the exact and real-time location is required by many of these services. Since Global Positioning System (GPS) is not designed for indoor environment, new positioning systems based on new technologies and methods are needed for these type of environments. In this thesis, RFID-based real-time indoor positioning systems and algorithm are developed. Received Signal Strength (RSS) based positioning techniques, are studied in detail. A hybrid algorithm is developed which depends on the mainly fingerprinting. The advantages of each method are emphasized. An original and unique hybrid algorithm is developed in this study in order to overcome available algorithm's' drawbacks. The algorithm and methodology is tested in two different indoor environments. As a result, the accuracy of this original and unique methodology and algorithm is 2,5 m.
Citation Count: 3
E-İşte Sürdürülebilir Bağlantılığı İzlemek için Ağ Tabanlı Teorinin Kullanımı
(Turkish Journal of Electrical Engineering and Computer Sciences, 2020) Çavur, Mahmut; Çavur, Mahmut
The Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section.
Citation Count: 0
Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms
(Mdpi, 2024) 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 Count: 0
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 Count: 6
Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada-USA
(MDPI, 2021-02) Çavur, Mahmut; Moraga, Jaime; Düzgün, H. Şebnem; Soydan, Hilal; Jin, Ge
The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is -19 mm/yr with an average value of -6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.
Citation Count: 4
RSSI-based hybrid algorithm for real-time tracking in underground mining by using RFID technology
(Elsevier, 2022) 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.
Citation Count: 2
Sentinel-1 sar verileri kullanilanarak maden kaymalarini ve deformasyonlarini izleme [MONITORING of MINE LANDSLIDE and DEFORMATION USING SENTINEL-1 SAR DATA]
(Baski, 2019) Çavur, Mahmut; Camalan, Mahmut; Ketizmen, Hakkı; Ağıtoğlu, Suud
In this study, an original DInSAR method was used to monitor landslides and deformation in a coal mine area. The open-pit mine operation belonging to the Ciner Group in Silopi, Sirnak was selected fort he case study. Between 21 November 2017 and December 31, 2017, 2-month Sentinel-1 data were analyzed every 12 days and interferometric results were obtained. It has been shown that the DInSAR method can be used effectively in order to monitor the mineral movements by using satellite images. The results of the analysis were reported in mm and accuracy analysis was performed on the field. SNAP, Cygwin, and ArcGIS 10.4 software are used for reporting and analysis purposes. The maximum subsidence was measured by radar as 45 mm. The mean subsidence rate of one class was found to be 45 mm as landslide and 46 mm as uplift where cracks most severely developed. The proposed method is an effective method for mining in order to determine the effects that may occur as a result of landslides, displacement, and uplift caused by underground and surface mining.
Citation Count: 9
The Geothermal Artificial Intelligence for geothermal exploration
(Pergamon-Elsevier Science Ltd, 2022) Çavur, Mahmut; Duzgun, H. S.; Cavur, M.; Soydan, H.
Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). We processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal/non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.(c) 2022 Elsevier Ltd. All rights reserved.
Citation Count: 0
Tarafsız 3D mineral harita tahminleri elde etmek için random forest tree sınıflandırması kullanılarak epoksi bloklardaki dikey kesitlerin değerlendirilmesi
(2021) Çavur, Mahmut; Çavur, Mahmut
Alansal mineral haritaları, epoksi reçinenin dibine çöken cevher tanelerinin yüzeylerini içeren parlak kesitlerinden yapılmaktadır.Fakat, ağır mineraller nispeten dibe çökebilmekte ve parlak yüzeyi ağır mineraller açısından zengin yapabilmektedir. Bu ise parlakkesitlerden hesaplanan alansal (2D) mineral haritalarının, hacimsel (3D) haritaların taraflı tahminleri haline gelmesine sebepolabilmektedir. Bu çalışma, parlak kesite dik olarak (parçacıkların çökelme yönü boyunca) alınan rastgele bir kesitin bir kromitcevheri numunesinin 3D mineral haritasının tarafsız bir tahmini olarak kullanılıp kullanılamayacağını test etmeyi amaçlamaktadır.Bu çalışmanın amacı için, dikey kesitlerin 2D haritaları, öncesi ve sonrası görüntü işleme araçlarıyla bütünleşmiş Random Forestsınıflandırmasıyla elde edilmiştir. Daha sonra, 2D haritalar, stereolojik hatalar olmadığı varsayılarak 3D mineral haritalarınadönüştürülmüştür. 3D haritalardan tahmin edilen modal mineraloji ve tane boyu dağılımları, sırasıyla XRD ve kuru elemeanalizlerinden tahmin edilen sonuçlarla karşılaştırılmıştır. Herhangi bir 2D harita gerçek analizlere yakın modal mineraloji ve taneboyu dağılımı veriyorsa, bu 2D harita cevher numunesinin 3D haritasının tarafsız bir tahmini olarak seçilmiştir. Bu çalışmanınsonuçları herhangi bir dikey kesitin, ağır minerallerin öncelikli olarak çöktüğü parlak kesitten farklı olarak gerçek 3D haritanıntarafsız bir tahmini olacağını desteklemektedir.