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

Main Affiliation

Management Information Systems

Status

Former Staff

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Sustainable Development Goals Report Points

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Scholarly Output

17

Articles

12

Citation Count

0

Supervised Theses

3 Scholarly Output Search Results

Now showing 1 - 10 of 17

Citation - WoS: 26
Citation - Scopus: 32
The Geothermal Artificial Intelligence for Geothermal Exploration
(Pergamon-Elsevier Science Ltd, 2022) Moraga, J.; Çavur, Mahmut; Duzgun, H. S.; Cavur, M.; Soydan, H.; Management Information Systems
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.
Tarafsız 3d Mineral Harita Tahminleri Elde Etmek için Random Forest Tree Sınıflandırması Kullanılarak Epoksi Bloklardaki Dikey Kesitlerin Değerlendirilmesi
(2021) Camalan, Mahmut; Çavur, Mahmut; Çavur, Mahmut; Management Information Systems
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.
Gelı̇şmekte Olan Ülkelerde Matematı̇k Başarısını Etkı̇leyen Faktörlerı̇n Araştırılmasında Makı̇ne Öğrenme Teknı̇klerı̇nı̇n Kullanılması: Türkı̇ye, Meksı̇ka, Tayland ve Bulgarı̇stan Örneğı̇
(2023) Arpa, Tuba; Çavur, Mahmut; Çavur, Mahmut; Management Information Systems
Matematik tüm eğitim sistemlerinin vazgeçilmez bir parçasıdır. Çünkü matematik, hem günlük yaşamın önemli bir unsuru hem de pek çok meslek ve alan için olmazsa olmaz bir temeli teşkil etmektedir. Bu nedenle, matematik başarısını etkileyen unsurları belirlemenin, ülkelerin gelişimine katkı sağlayacağı söylenebilir. Bu doğrultuda, bu çalışmada PISA 2018 verileri kullanılarak, benzer eğitim sistemi ve ekonomik gelişmişliğe sahip dört ülke olan Türkiye, Bulgaristan, Meksika ve Tayland'ın matematik başarılarını etkileyen faktörleri makine öğrenmesi modelleri ile belirlemek, bu modellerin başarılarını karşılaştırmak amaçlanmıştır. İlgili alanyazında bu amaç için sıklıkla sınıflandırma algoritmaları tercih edildiği görülmektedir. Bu çalışmada hem sınıflandırma hem de regresyon modelleri kullanılmıştır. Çalışmada, regresyon algoritması olarak doğrusal regresyon, destek vektör regresyonu, karar ağacı regresyonu ve rastgele orman regresyonu; sınıflandırma algoritması olarak ise lojistik regresyon, destek vektör sınıflandırması, karar ağacı sınıflandırması ve rastgele orman sınıflandırması kullanılmıştır. Ayrıca, matematik başarısını tahmin etmek için en önemli faktörlerin belirlenmesinde XGradient Boosting algoritması kullanılmıştır. Son olarak, eksik verilerin doldurulmasında, K-Means metodu tercih edilmiştir. Çalışmanın sonuçlarına göre, dört ülke için de matematik başarına en büyük katkı sağlayan değişkenlerin öğrencinin ekonomik, sosyal ve kültürel statüsü, öğrencinin evde sahip olduğu çalışma materyali, öğrencinin sahiplik hissi ve ailenin refah düzeyi olduğu bulunmuştur. Model başarısı açısından hem regresyon hem de sınıflandırma açısından en yüksek başarıya sahip algoritmanın rastgele ormanlar olduğu bulunmuştur. Ayrıca, sınıflandırma algoritmaları ikili ve üçlü sınıflandırma üzerinden incelenmiş, ikili sınıflandırmanın daha yüksek başarıya sahip olduğu görülmüştür. Sonuç olarak, çalışmamızda elde edilen bulgular matematik başarısını tahmin etmede kullanılacak en uygun algoritmanın seçimi v konusunda önemli bir öngörü sunmaktadır. Ayrıca, çalışmanın bulguları, eğitim politikalarının geliştirilmesi ve öğrenci başarısını artırmak için uygulayıcı ve politika yapıcılara önemli iç görüler sağlamaktadır.
Citation - WoS: 5
Citation - Scopus: 5
Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms
(Mdpi, 2024) Cavur, Mahmut; Çavur, Mahmut; Yu, Yu-Ting; Demir, Ebubekir; Duzgun, Sebnem; Management Information Systems
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: 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; Management Information Systems
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: 8
Citation - Scopus: 12
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; Çavur, Mahmut; Moraga, Jaime; Düzgün, H. Şebnem; Soydan, Hilal; Jin, Ge; Management Information Systems
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 - WoS: 1
Yereraltı Maden İşçilerini Gerçek Zamanlı Takip Etmek İçin Rfıd Teknolojisine Dayalı Özgün Bir Entegrasyon Metodolojisi
(Gazi Universitesi, 2018) Çavur, Mahmut; Çavur, Mahmut; Management Information Systems
In recent years many companies want to keep track of their employees sources and working machines due to various reasons like security coordination performance monitoring. The purpose and requirements are the main factors that determine the methodology of tracking. The real-time tracking can be determined with high precision in open areas with the global positioning system (GPS). However previous research and developments for indoor tracking have mostly focused on infrared wireless LAN and ultrasonic. In this study a Radio-Frequency Identification (RFID) protocol and interface are integrated into an open source Information Systems (IS) software. A tight coupling methodology is developed for integration of RFID into an open source software. The use of open source software as a common interface also provides better spatial display and analysis capabilities. The tracking algorithm is completely unique original and it is encoded in the Java programming language. In the algorithm the accuracy of locating the proximity direction of miners and whether the RFID tag is on the right and left of the last point of RFID receiver is determined with 20 m accuracy. The system was tested in an underground salt mine. The developed methodology and system are now being commercialized in Turkey.
Citation - WoS: 4
Citation - Scopus: 5
Development of a Supervised Classification Method To Construct 2d Mineral Maps on Backscattered Electron Images
(Tubitak, 2020) Camalan, Mahmut; Çavur, Mahmut; Çavur, Mahmut; Management Information Systems
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 - WoS: 1
Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks To Get Unbiased Estimates for 3d Mineral Map
(Gazi University, 2021) Camalan, Mahmut; Çavur, Mahmut; Çavur, Mahmut; Management Information Systems
Areal mineral maps are constructed from the polished sections of particles that settle to the bottom of epoxy resin. However, heavy minerals can preferentially settle to the bottom, making the polished surface rich in heavy minerals. Therefore, polished sections will become biased estimates of the volumetric (3D) map. The study aims to test whether any vertical cross-section (any section along the settling direction of particles) can be an unbiased estimate of the 3D mineral map of a chromite ore sample. For the purpose of this study, 2D maps of the vertical cross-sections were acquired by using Random Forest classification coupled with image pre- and post-processing tools. Then, 3D mineral maps were converted from 2D maps without assuming stereological errors. The modal mineralogy and particle size distributions predicted from 3D maps were compared with the same features estimated from the particulate sample by XRD and dry sieving analyses, respectively. Any 2D map which yields a modal mineralogy and a size distribution similar to the true analyses was selected as an unbiased estimate of the true 3D map. The results suggest that any vertical cross-section is an unbiased estimate, unlike polished section at which heavier minerals settle preferentially.
Citation - Scopus: 3
Sentinel-1 Sar Verileri Kullanilanarak Maden Kaymalarini ve Deformasyonlarini İzleme [monıtorıng Of Mıne Landslıde And Deformatıon Usıng Sentınel-1 Sar Data]
(Baski, 2019) Çavur, Mahmut; Çavur, Mahmut; Camalan, Mahmut; Ketizmen, Hakkı; Ağıtoğlu, Suud; Management Information Systems
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.