Sürdürülebilir Kalkınma Amaçları

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Araştırmacılar(33)

• Akbulut, Zeynep
• Aliyazicioglu, Kubra
• Aşık, Yusuf
• Bayrak, T.
• Beker, Fatmanur
• Birinci, Abdulkadir
• Bostancı, Hasan Tahsin
• Bozkurt, Muhammet Furkan
• Ceylan, Mustafa
• Comert, Resul
• Corumluoglu, Ozsen
• Çelik, Kemal
• Döner, Fatih
• Günen, Mehmet Akif
• Inan, Halil Ibrahim
• Karacur, Soner
• Karagoz, Seray Nur
• Kaya, Serhat
• Kurt, Ahmet Melih
• Marabaoğlu, Seda Nur
• Özdemir, Samed
• Reis, Hatice Çatal
• Sadik, Suzan
• Tatli, Ramazan
• Tona, Aziz Uğur
• Topaloğlu, Raziye Hale
• Turk, Veysel
• Yagci, Zekeriya
• Yalvaç, Sefa
• Yamak, Beyzanur
• Yavuzdoğan, Ahmet
• Yılmaz, Hülya
• Yildirim, M. Sinan

• MakaleAçık Erişim2027/r/gumushane/10415
  Authorisation of notary publics on real property transactions in relation to the Turkish land registry system based on Cadastre 2014 vision

  Çoruhlu, Yakup Emre; Inan, Halil Ibrahim; Çelik, Mehmet Ozgur; Alemdar, Alkan; Yildiz, Okan; Azadi, Hossein

  The International Federation of Surveyors (FIG) has globally influenced land administration through its Cadastre 2014 vision and subsequent LADM standards. T & uuml;rkiye has been modernizing its land registry and cadastre system accordingly. The General Directorate of Land Registry and Cadastre (GDLRC) manages approximately 17 million annual transactions, including about 3.4 million conveyancing (long-term averages). To reduce congestion in Land Registry Offices (LROs), notaries were authorized to issue sales contracts in June 2022. This study investigates the effects of this change within the Cadastre 2014/2034 framework in terms of the maturity of the Turkish Land Administration System (LAS) and also by linking the issue to broader debates within land management and governance. A survey of 140 GDLRC experts revealed that notary involvement currently offers limited time and financial benefits to citizens and does not significantly reduce LRO workload. The central factor for this limitation is evaluated to be the maturity of Turkish LAS. On the other hand, the initiative aligns with Cadastre 2014's fifth statement, which advocates public-private collaboration, and therefore it should be further evolved. Within this evolution, administrative/managerial fixes and training programmes for notaries in the short term, management of all internal and external RRRs via LAS as a spatial data infrastructure service in line with LADM standards in the medium term, and betterment of institutional coordination, public responsibility development and capacity building in the long term are proposed for a better eventual land administration and management towards local and national good land governance.

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• MakaleAçık Erişim2027/r/gumushane/14229
  A hierarchical metaheuristic framework for CPT-based soil liquefaction prediction with Kolmogorov–Arnold Network variants

  Günen, Mehmet Akif; Öztürk, Kaşif Furkan

  Soil liquefaction is a critical geotechnical hazard that threatens the safety of civil infrastructure under seismic loading, necessitating robust and interpretable prediction models. This study proposes a hierarchical framework for CPT-based soil liquefaction prediction using meta-heuristic-optimized Kolmogorov–Arnold Network (KAN) variants. The framework follows a two-stage optimization strategy. First, six state-of-the-art meta-heuristic algorithms are statistically compared to identify the most effective optimization method. Second, the selected algorithm is employed to optimize six KAN variants, including FastKAN, EfficientKAN, FourierKAN, ChebyKAN, JacobiRKAN, and PadéRKAN. Statistical comparisons based on the Wilcoxon signed-rank test demonstrate that the Colony-Based Search algorithm (CSA) achieves lower objective-function values than the competing meta-heuristics (p<0.01) and is therefore adopted as the core optimization strategy. Performance evaluation reveals that the CSA-optimized ChebyKAN model achieves the highest predictive accuracy, with an F1 Score of 0.8875 ± 0.011 and an AUC of 0.9115 ± 0.013. Pairwise McNemar tests detected no statistically significant difference in classification decisions for any evaluated model pair (p>0.01). SHapley Additive exPlanations analysis identified qc, qc1N, and amax as the variables with the largest model-specific attributions, whereas d, c, and σv received lower attributions. A cost-sensitive error analysis further showed that ChebyKAN produced the lowest false-negative rate and the lowest asymmetric error cost under 5:1 and 10:1 false-negative-to-false-positive cost scenarios, supporting its conservative behaviour for safety-critical liquefaction screening. The results establish the proposed CSA–KAN framework as a reliable, high-accuracy, and interpretable solution for CPT-based soil liquefaction assessment in geotechnical engineering practice.

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• MakaleAçık Erişim2026/r/gumushane/10585
  Modeling Long-Term Postseismic Deformation Following the 2020 Mw 7.0 Samos Earthquake Using Campaign and Continuous GNSS Observations

  Solak, Halil Ibrahim; Tiryakioglu, Ibrahim; Gezgin, Cemil; Aladogan, Kayhan; Yalvaç, Sefa; Aktug, Bahadir; Yigit, Cemal Ozer; Dönmez, Ergin; Demirelli, Ertugrul; Eyübagil, Eda Esma

  Highlights What are the main findings? What are the implications of the main findings?Highlights What are the main findings? What are the implications of the main findings?Abstract Postseismic deformation provides fundamental insights into earthquake-cycle processes, lithospheric rheology, and stress redistribution following large earthquakes. Although the 2020 Mw 7.0 Samos earthquake has been extensively investigated in terms of coseismic deformation and early postseismic behavior, the long-term evolution of deformation following the event remains poorly constrained. This study characterizes the long-term spatiotemporal evolution of postseismic deformation associated with the 2020 Mw 7.0 Samos earthquake using combined campaign and continuous GNSS observations. A total of 18 GNSS stations were analyzed over an approximately 4.5-year period following the earthquake. Pre-earthquake GNSS velocities were incorporated as prior constraints, while postseismic deformation was modeled using linear, logarithmic, exponential, and combined logarithmic-exponential functions. The preferred model for each station component was identified using the corrected Akaike Information Criterion (AICc), and model-selection robustness was evaluated through 1000 Monte Carlo observation-perturbation simulations. The results reveal a spatially heterogeneous postseismic deformation field with station-dependent temporal behavior. Among the nonlinear solutions passing the Monte Carlo and goodness-of-fit criteria, the seven single-process LOG and EXP solutions yielded characteristic relaxation times ranging from 182.6 to 730.5 days, with a median of 438.3 days. The two LOGEXP solutions additionally contained a logarithmic timescale of 109.6 days and exponential timescales of 292.2-438.3 days. These findings demonstrate that campaign GNSS observations, when integrated with continuous GNSS data and an objective statistical framework, can provide meaningful constraints on the long-term evolution of postseismic deformation despite sparse temporal sampling. More broadly, the results emphasize the strongly time-dependent nature of postseismic deformation and the critical role of observation timing in capturing its spatiotemporal evolution.

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• MakaleAçık Erişim2026/r/gumushane/15958
  Assessment of topographically influenced gravity-height gradients and plumb line curvature at Sümela Monastery (Türkiye)

  Tütüncü, Mehmet; Yilmaz, Nazan

  Accurate estimation of gravity-height gradient and plumb line curvature is fundamental for gravity field modeling, geoid determination, and high-precision geodetic applications, especially in mountainous regions. This study presents an integrated computational framework combining terrestrial gravity observations, precise leveling, GNSS measurements, and Global Geopotential Models (GGMs) to estimate and compare gravity-height gradient and plumb line curvature in the rugged terrain surrounding S & uuml;mela Monastery. Simultaneous gravity and leveling surveys were conducted during two observation periods to compute actual and normal gradient, level surface curvature, and plumb line curvature. Additionally, twelve GGMs obtained from the ICGEM service were used to derive the same parameters through spherical harmonic expansions. A comprehensive statistical evaluation, including minimum, maximum, mean, standard deviation, and RMSE, was performed to assess the consistency between terrestrial and model-based results. The results show that normal gradient and level surface curvature from GGMs generally agree well with terrestrial data, whereas larger discrepancies occur in actual gradient and plumb line curvature in areas with steep topographic gradients. CHAMP-based models demonstrate comparatively better consistency in representing plumb line curvature. The proposed framework confirms the reliability of global models at regional scales while emphasizing the importance of terrestrial data in complex topographic environments.

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• Yüksek Lisans TeziAçık Erişim2026/r/gumushane/20589
  Afet ve acil durum toplanma alanlarının yeterlilik ve uygunluklarının coğrafi bilgi sistemleri destekli analizi: Malatya merkez örneği

  Zehra Kalkan

  Türkiye'nin coğrafi konumu ve jeolojik yapısı; ülkeyi deprem, sel ve heyelan gibi doğal afetlere karşı yüksek riskli bir bölge haline getirmektedir. Bu risk, toplumsal direnci artırmaya yönelik etkili bir afet yönetim stratejisinin geliştirilmesini zorunlu kılmaktadır. Afet yönetiminin en kritik aşamalarından biri de afetlere yönelik hizmet alanlarının doğru ve ihtiyacı karşılayacak şekilde belirlenmesidir. Ancak yaşanan afetler sonucu ortaya çıkan durum, Türkiye’de bunun henüz başarılamadığı göstermektedir. Bu noktadan hareketle bu tez çalışmasında mevcut mevzuat ve yer seçimi yaklaşımları çerçevesinde belirlenmiş olan afet hizmet alanlarının yeterlilik ve uygunluklarının, Afet ve Acil Durum Toplanma Alanları (AADTA) esas alınarak, Coğrafi Bilgi Sistemleri destekli analizlerle belirlenmesi amaçlanmıştır. Daha anlamlı bir sonuç ortaya koyabilmek adına Şubat 2023 depremlerinden önemli ölçüde etkilenen Malatya ilinin kent merkezi çalışma alanı olarak belirlenmiştir. Ayrıntılı bir literatür ve mevzuat incelemesi sonucunda belirlenen; “Erişilebilirlik, Kapasite ve Yeterlilik Durumu”, “Topografik Özellikler”, “Ulaşım, Teknik Altyapı ve Arazi Kullanımı Durumu”, “Güvenlik ve Çevresel Risk Durumu” ve “Alansal Doluluk ve Kullanılabilirlik Durumu” ana başlıkları altındaki parametreler kullanılarak çalışma alanındaki AADTA’nın yeterlik ve uygunlukları analiz edilerek değerlendirilmiştir. Buna göre; mevcut AADTA’nın 500 m’lik erişilebilirlik mesafesi için çalışma alanındaki ihtiyacın en iyimser durumda ancak %27’sini karşılayabildiği, tanımlı alanlar ve 2.5 (kişi/m2) kullanımı esas alındığında 188808 kişilik toplam kapasitenin 500 m erişim mesafesindeki potansiyel nüfus açısından yeterli olduğu ancak AADTA’lar bazında sadece 2 tanesinin kapasite yönüyle yeterli olduğu, birleşik erişim alanları esas alındığında ise en çok 6 AADTA’nın kapasite yönüyle yeterli olduğu ve ayrıca alansal doluluk, kullanılabilirlik ve diğer parametreler açısında sorun ve yetersizliklerin olduğu belirlenmiştir.

  RDL DOI10.71008/gumushane.thesis.2026.170indekslendi
  İncele
• MakaleAçık Erişim2026/r/gumushane/19452
  Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi

  Bostancı, Hasan Tahsin

  Gümüşhane, jeolojik yapısı, engebeli topografyası ve iklimsel özellikleri nedeniyle ülkemizde heyelan, kaya düşmesi ve çığ gibi kütle hareketlerinin sıkça yaşandığı illerden biridir. 2021 yılında hazırlanan il afet risk azaltma planında il genelinde meydana gelen doğal afetlerin %49’unu heyelanların oluşturduğu belirtilmiştir. Dolayısıyla bu çalışmada, makine öğrenmesi algoritmaları kullanılarak Gümüşhane’nin Merkez ilçesinin heyelan duyarlılık haritalarının üretilmesi amaçlanmıştır. Çalışmada makine öğrenmesi algoritmaları olarak lojistik regresyon (LR), yapay sinir ağları (ANN) ve destek vektör makineleri (SVM) tercih edilmiştir. Adı geçen algoritmalarla oluşturulan duyarlılık modellerinde; bakı, drenaj ağlarına uzaklık, yola uzaklık, yükseklik, arazi örtüsü, litoloji, plan eğriliği, profil eğriliği, eğim ve topografik nemlilik indeksi (TWI)’nden oluşan 10 faktör kullanılmıştır. Modellerin eğitim ve doğrulama aşamasında MTA Genel Müdürlüğünden temin edilen heyelan envanter haritası kullanılmıştır. LR, ANN ve SVM modellerinin performansı ROC eğrisi altında kalan alan (area under the ROC curve - AUC) metriği kullanılarak değerlendirilmiştir. LR, ANN ve SVM modellerinin AUC değerleri, sırasıyla, 0.784, 0.865 ve 0.888 olarak belirlenmiştir. Bu sonuçlar SVM modelinin diğer modellerden daha iyi performans gösterdiğini ortaya koymuştur. Ayrıca, çalışma alanında heyelanların meydana gelmesinde en etkili faktörlerin sırasıyla eğim, yükseklik, TWI ve yola yakınlık olduğu belirlenmiştir. 

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• MakaleYalnız künye2026/r/gumushane/13913
  Efficient ensemble learning with multi-scale fusion based deep neural network for skin lesion classification

  Reis, Hatice Catal; Turk, Veysel

  Melanoma, a type of skin cancer, is a severe skin cancer that is critical for early diagnosis but difficult to detect. Deep learning is promising in melanoma diagnosis because it can automatically analyze visual features and perform end-to-end learning. However, achieving the optimum balance between performance and computational efficiency for the effective use of these models in clinical applications is an important research topic. In this study, three innovative methods are proposed for skin cancer detection: PADSCNet, P-DEFF, and M–DEFF. The PADSCNet is an effective neural network developed with CNN and multi-head attention techniques, designed with fusion methods, and stands out with its minimum number of parameters despite being quite deep. The P-DEFF and M–DEFF are based on ensemble learning techniques; P-DEFF was developed with a single deep learning model, and M–DEFF was developed with multiple pre-trained deep learning models. The proposed approaches were tested on the Skin Cancer: Malignant vs. Benign dataset, which was pre-processed using image enhancement techniques. According to the experimental results, PADSCNet achieved accuracy rates of 88.12 %, P-DEFF 93.75 %, and M–DEFF 95.00 %. In additional experiments conducted with the PADSCNet model, 93.12 % accuracy rates were achieved in the CNN for Melanoma Detection Dataset, and 95.00 % accuracy rates were achieved in the PH2 dataset. These findings show that the proposed methods are effective in skin cancer detection and can achieve successful results in clinical applications.

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• MakaleAçık Erişim2026/r/gumushane/13897
  Severity-Based Mapping of Land-Subsidence Hazard Zones and Critical Hotspots Using SBAS-InSAR and Spatial Statistics: The Konya Metropolitan Area, Turkey

  Yalvac, Sefa; Bjelotomić Oršulić, Olga

  Land subsidence induced by excessive groundwater withdrawal has become one of the most significant geohazards affecting the Konya Closed Basin, Turkey. Although previous studies have successfully monitored ground deformation using geodetic and remote sensing techniques, limited attention has been devoted to transforming deformation measurements into quantitative, spatially classified hazard information. This study presents a severity-based framework for delineating land-subsidence hazard zones and critical hotspots in the Konya metropolitan area by integrating SBAS-InSAR observations and spatial statistical analyses. A total of 82 Sentinel-1 SAR acquisitions (41 ascending and 41 descending images) acquired between January 2023 and May 2026 were processed using the Small Baseline Subset (SBAS) technique. Ascending and descending line-of-sight deformation measurements were combined to derive vertical deformation rates, which were integrated with spatial statistical indicators and a composite severity index to quantify deformation clustering and classify subsidence severity. Hazard zones and critical hotspot areas were delineated through severity-based classification and spatial connectivity analyses. The results reveal a continuous north–south-oriented subsidence deformation belt extending across the eastern Konya. Maximum vertical subsidence rates exceeded 230 mm/yr, while spatial statistical analyses confirmed strongly clustered and statistically significant deformation patterns. Severity-based hazard zonation identified four hazard classes and a continuous high-hazard corridor. Clustering analysis further identified a dominant hotspot belt covering approximately 160 km2, with mean subsidence rates of approximately 142 mm/yr. A sensitivity analysis of the composite severity index weighting scheme, the spatial statistical neighborhood distance, the DBSCAN clustering parameters, and the number of Jenks severity classes confirmed that the resulting hazard zones and critical hotspot belt are robust to reasonable parameter variations. The findings demonstrate that land subsidence in Konya is organized as a spatially continuous regional-scale deformation system rather than a collection of isolated subsidence centers. The proposed framework transforms InSAR-derived deformation measurements into quantitative, decision-support hazard information and provides a transferable methodology for land-subsidence hazard assessment in groundwater-stressed urban environments.

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• MakaleYalnız künye2026/r/gumushane/13804
  Very fast Kolmogorov-Arnold network for landslide susceptibility prediction

  Günen, Mehmet Akif; Tona, Aziz Uğur

  Landslides represent critical geological events causing loss of life and property. Prediction of landslide susceptibility is essential for risk reduction and decision-making. XGBoost, LightGBM, NGBoost, CatBoost, MLP, and FastKAN were used to predict landslide susceptibility in Gümüşhane, Türkiye, a region with complex topographic and geological structures. 20 factors were used: 9 topographical, 3 hydrological, 4 climatic, 2 geological, and 2 environmental and infrastructure-related factors. Model performance was assessed using Accuracy, Precision, Recall, and F-Score, with SHAP analysis showing feature contributions. A multicollinearity analysis was used to determine the final feature set. Topographical variables (Aspect, Slope) showed lower impacts, while geological and environmental factors (Lithology, Distance to Fault) demonstrated moderate influence across models. For FastKAN, DEM, precipitation, and distance to fault were key factors, while for boosting methods, GHI, Tmax, and DEM were most significant. FastKAN achieved the highest performance under the internal pixel-wise evaluation scheme, with an accuracy of 96.54%, followed by XGBoost at 95.56%. McNemar’s statistical test showed no significant difference between NGBoost and MLP. Post-inventory landslide events observed in 2025 and 2026 were spatially consistent with high and very high susceptibility zones, providing a qualitative consistency check rather than rigorous independent quantitative validation.

  İncele
• MakaleYalnız künye2026/r/gumushane/13762
  CropHealthNet: Potato Disease Detection Using Depthwise Separable Convolutional Neural Networks

  Catal Reis, Hatice

  Plant diseases are one of the primary factors that threaten food security, crop yields, harvesting, and sustainable production in the agricultural sector. In this context, early disease diagnosis is essential for enhancing agricultural productivity and reducing crop losses. In modern agricultural practices, machine learning-based approaches offer effective and innovative solutions for detecting and classifying plant diseases. These modern and innovative methods generally reach high accuracy rates with large-scale image datasets. In particular, deep learning algorithms have achieved significant success in modeling disease’s spread dynamics and detecting early disease symptoms. In this study, a convolutional neural network (CNN)-based, lightly parallel deep learning model called CropHealthNet is proposed for classifying potato leaf diseases. The model is designed with special depthwise separable convolution-based convolution layers to reduce the computational cost of CNN. CropHealthNet is a low-parameter model suitable for real-time operation, capable of recognizing disease-specific patterns with high accuracy. CropHealthNet aims to achieve deep and comprehensive feature extraction through its parallel structure and varying channel numbers. The features from each parallel structure are combined at multiple levels, increasing the model’s ability to detect abstract and deep patterns. Moreover, the proposed model has extremely low computational complexity with only 0.47 million parameters and 0.59 GFLOPs, thus offering high efficiency compared to existing approaches. The effectiveness of the proposed model was evaluated using the PlantVillage Potato Dataset. Since the PlantVillage Potato Dataset has an unbalanced data distribution, the number of data for each class has been equalized using the Synthetic Minority Over-sampling Technique (SMOTE) to evaluate the model’s performance against data imbalance. The proposed model succeeded in achieving 99.30% and 99.50% accuracy rates in the tests conducted with unbalanced and balanced datasets, respectively. Experimental findings show that the proposed model generalizes well despite data imbalance and correctly classify diseases on potato leaves. Therefore, the model can demonstrate strong performance in agricultural practices and can detect plant diseases under real-world conditions.

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