Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi

Hasan Tahsin Bostancı

doi:10.48123/rsgis.1836037

EN TR

Assessment of Landslide Susceptibility of Gümüşhane Using Machine Learning Algorithms

Öz

Gümüşhane is one of the provinces in Turkey where mass movements such as landslides, rockfalls, and avalanches frequently occur due to its geological structure, rugged topography, and climatic characteristics. The provincial disaster risk reduction plan prepared in 2021 states that landslides account for 49% of all natural disasters occurring throughout the province. Therefore, this study aims to produce landslide susceptibility maps for the Central District of Gümüşhane using machine learning algorithms. In this study, logistic regression (LR), artificial neural networks (ANN), and support vector machines (SVM) were preferred as machine learning algorithms. Ten factors were used in the susceptibility models created with the aforementioned algorithms: aspect, distance to drainage networks, distance to roads, elevation, land cover, lithology, plan curvature, profile curvature, slope, and topographic wetness index (TWI). The landslide inventory map obtained from the General Directorate of the MTA was used in the training and validation stages of the models. The performance of the LR, ANN, and SVM models was evaluated using the area under the ROC curve (AUC) metric. The AUC values of the LR, ANN, and SVM models were determined to be 0.784, 0.865, and 0.888, respectively. These results indicate that the SVM model performed better than the other models. Additionally, it was determined that the most influential factors in the occurrence of landslides in the study area were slope, elevation, TWI, and distance to roads, in that order.

Anahtar Kelimeler

Landslides, Machine learning, LR, ANN, SVM, Gümüşhane

Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi

Öz

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.

Anahtar Kelimeler

Heyelan, Makine öğrenmesi, LR, ANN, SVM, Gümüşhane

Kaynakça

Abidi, A., Demehati, A., & El Qandil, M. (2019). Landslide susceptibility assessment using evidence belief function and frequency ratio models in Taounate city (North of Morocco). Geotechnical and Geological Engineering, 37, 5457–5471. https://doi.org/10.1007/s10706-019-00992-0
Aditian, A., Kubota, T., & Shinohara, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
Akbaş, B., Hacı Arslan, H., Halil Keskin, H., Hamdi Mengi, H., Altun, İ. E., Erdoğan, K., Sevin, M., Deniz, N., Talia, Ş. A., & Köse, Y. Z. (1994). Giresun-Piraziz-Şebinkarahisar arasında kalan bölgenin jeolojisi. Maden Tetkik ve Arama Genel Müdürlüğü, Jeoloji Etütleri Dairesi Başkanlığı.
Akıncı, H., Yavuz Özalp, A., Özalp, M., Temuçin Kılıçer, S., Kılıçoğlu, C., & Everan, E. (2015a). Production of landslide susceptibility map using Bayesian probability model. International Journal of 3-D Information Modeling, 4(2), 16–33. https://doi.org/10.4018/IJ3DIM.201504010
Akıncı, H., Yavuz Özalp, A., & Temuçin Kılıçer, S. (2015b). Coğrafi bilgi sistemleri ve AHP yöntemi kullanılarak planlı alanlarda heyelan duyarlılığının değerlendirilmesi: Artvin örneği. Doğal Afetler ve Çevre Dergisi, 1(1–2), 40–53. https://doi.org/10.21324/dacd.20952
Akinci, H., & Yavuz Ozalp, A. (2021). Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophysica, 69, 725–745. https://doi.org/10.1007/s11600-021-00577-7
Akinci, H., & Zeybek, M. (2021). Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuç (Artvin), Turkey. Natural Hazards, 108, 1515–1543. https://doi.org/10.1007/s11069-021-04743-4
Akinci, H. (2022). Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques. Journal of African Earth Sciences, 191, Article 104535. https://doi.org/10.1016/j.jafrearsci.2022.104535
Akinci, H., Zeybek, M., & Dogan, S. (2022). Evaluation of landslide susceptibility of Şavşat district of Artvin Province (Turkey) using machine learning techniques. In Y. Zhang & Q. Cheng (Eds.), Landslides (pp. 69–96). IntechOpen. http://dx.doi.org/10.5772/intechopen.99864
Akinci, H., & Yavuz Ozalp, A. (2025). Investigating the effects of different data classification methods on landslide susceptibility mapping. Advances in Space Research, 75, 3427–3450. https://doi.org/10.1016/j.asr.2024.12.020

Alcântara, E., Baião, C. F., Guimarães, Y. C., Mantovani, J. R., & Marengo, J. A. (2025). Machine learning reveals lithology and soil as critical parameters in landslide susceptibility for Petrópolis (Rio de Janeiro State, Brazil). Natural Hazards Research. Advance online publication. https://doi.org/10.1016/j.nhres.2025.01.008
Alemdag, S., Akgun, A., Kaya, A., & Gokceoglu, C. (2014). A large and rapid planar failure: Causes, mechanism, and consequences (Mordut, Gumushane, Turkey). Arabian Journal of Geosciences, 7, 1205–1221. https://doi.org/10.1007/s12517-012-0821-1
Alkan Akinci, H., Akinci, H., & Zeybek, M. (2024). Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye. Advances in Space Research, 74, 647–667. https://doi.org/10.1016/j.asr.2024.04.018
Arabameri, A., Saha, S., Roy, J., Chen, W., Blaschke, T., & Tien Bui, D. (2020). Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River watershed, Iran. Remote Sensing, 12(3), Article 475. https://doi.org/10.3390/rs12030475
Barman, J., Ali, S. S., Biswas, B., & Das, J. (2023). Application of index of entropy and geospatial techniques for landslide prediction in Lunglei district, Mizoram, India. Natural Hazards Research, 3(3), 508–521.
Bayrak, T., Ulukavak, M., & Açar, S. (2010). Gümüşhane heyelanları. Harita Teknolojileri Elektronik Dergisi, 2(1), 1–12.
Bhandari, A. (2025, May 1). Guide to AUC ROC curve in machine learning. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/
Bragagnolo, L., da Silva, R. V., & Grzybowski, J. M. V. (2020). Artificial neural network ensembles applied to the mapping of landslide susceptibility. Catena, 184, Article 104240. https://doi.org/10.1016/j.catena.2019.104240
Can, R., Kocaman, S., & Gokceoglu, C. (2021). A comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk Dam, Turkey. Applied Sciences, 11(11), Article 4993. https://doi.org/10.3390/app11114993
Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J., Zhu, A.-X., Pei, X., & Duan, Z. (2018). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Science of the Total Environment, 626, 1121–1135. https://doi.org/10.1016/j.scitotenv.2018.01.124
Demir, G. (2019). GIS-based landslide susceptibility mapping for a part of the North Anatolian Fault Zone between Reşadiye and Koyulhisar (Turkey). Catena, 183, Article 104211. https://doi.org/10.1016/j.catena.2019.104211
Erener, A., Mutlu, A., & Düzgün, H. S. (2016). A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Engineering Geology, 203, 45–55. https://doi.org/10.1016/j.enggeo.2015.09.007
Fidan, S., Tanyaş, H., Akbaş, A., Lombardo, L., Petley, D. N., & Görüm, T. (2024). Understanding fatal landslides at global scales: A summary of topographic, climatic, and anthropogenic perspectives. Natural Hazards, 120, 6437–6455.
Gómez-Miranda, I. N., Restrepo-Estrada, C., Builes-Jaramillo, A., & de Albuquerque, J. P. (2025). Advanced AI techniques for landslide susceptibility mapping and spatial prediction: A case study in Medellín, Colombia. Applied Computing and Geosciences, 25, Article 100226. https://doi.org/10.1016/j.acags.2025.100226
Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81, 166–184. https://doi.org/10.1016/j.geomorph.2006.04.007
Gümüşhane Afet ve Acil Durum İl Müdürlüğü. (2021). İl afet risk azaltma planı. https://gumushane.afad.gov.tr/kurumlar/gumushane.afad/E-Kutuphane/Gumushane-IRAP-.pdf
Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.
Huang, F., Cao, Z., Guo, J., Jiang, S.-H., Li, S., & Guo, Z. (2020). Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena, 191, Article 104580. https://doi.org/10.1016/j.catena.2020.104580
Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7, 186–190.
Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11, 352–359.
Kavzoğlu, T., Şahin, E. K., & Çölkesen, İ. (2012a). Heyelan duyarlılığının incelenmesinde regresyon ağaçlarının kullanımı: Trabzon örneği. Harita Dergisi, 147, 21–33.
Kavzoğlu, T., Çölkesen, İ., & Şahin, E. K. (2012b, 16–19 Ekim). Heyelan duyarlılık haritasının üretilmesinde kullanılan faktörlerin etkilerinin araştırılması: Düzköy örneği. IV. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu (UZAL-CBS 2012), Zonguldak, Türkiye.
Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11(3), 425–439.
Kavzoglu, T., & Teke, A. (2022). Predictive performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arabian Journal for Science and Engineering, 47, 7367–7385.
Kaya, A., Alemdağ, S., Dağ, S., & Gürocak, Z. (2016). Stability assessment of high-steep cut slope debris on a landslide (Gumushane, NE Turkey). Bulletin of Engineering Geology and the Environment, 75, 89–99. https://doi.org/10.1007/s10064-015-0753-6
Kaya Topaçli, Z., Ozcan, A. K., & Gokceoglu, C. (2024). Performance comparison of landslide susceptibility maps derived from logistic regression and random forest models in the Bolaman Basin, Türkiye. Natural Hazards Review, 25(1), Article 04023054. https://doi.org/10.1061/NHREFO.NHENG-1771
Keskin, İ. (2013). 1:100.000 ölçekli Türkiye jeoloji haritaları serisi – Artvin E47 ve F47 paftaları (No. 179). Maden Tetkik ve Arama Genel Müdürlüğü, Jeoloji Etütleri Dairesi Başkanlığı.
Keskin, İ. (2016). 1:100.000 ölçekli Türkiye jeoloji haritaları serisi – Trabzon H43 paftası (No. 239). Maden Tetkik ve Arama Genel Müdürlüğü, Jeoloji Etütleri Dairesi Başkanlığı.
Kilicoglu, C. (2021). Investigation of the effects of approaches used in the production of training and validation data sets on the accuracy of landslide susceptibility mapping models: Samsun (Turkey) example. Arabian Journal of Geosciences, 14, Article 2106. https://doi.org/10.1007/s12517-021-08312-8
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1–26. https://doi.org/10.18637/jss.v028.i05 Kurt, İ., Kılınç, M. F., Uysal, Ş., & Bedi, Y. (1995). Koyulhisar (Sivas) dolayının jeolojisi. Maden Tetkik ve Arama Genel Müdürlüğü, Jeoloji Etütleri Dairesi Başkanlığı.
Liu, L. L., Zhang, J., Li, J., Huang, F., & Wang, L. (2022). A bibliometric analysis of the landslide susceptibility research (1999–2021). Geocarto International, 37(26), 14309–14334. https://doi.org/10.1080/10106049.2022.2087753
Lokesh, P., Madhesh, C., Mathew, A., & Shekar, P. R. (2025). Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India. HydroResearch, 8, 113–126.
Meteoroloji Genel Müdürlüğü. (2025). İllere ait genel istatistik verileri. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=A&m=GUMUSHANE
Moayedi, H., Xu, M., Naderian, P., Dehrashid, A. A., & Thi, Q. T. (2024). Validation of four optimization evolutionary algorithms combined with artificial neural network (ANN) for landslide susceptibility mapping: A case study of Gilan, Iran. Ecological Engineering, 201, Article 107214. https://doi.org/10.1016/j.ecoleng.2024.107214
Orhan, O., Bilgilioğlu, S. S., Kaya, Z., Ozcan, A. K., & Bilgilioğlu, H. (2022). Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto International, 37(10), 2795–2820.
Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180–197. https://doi.org/10.1016/j.jseaes.2012.12.014
Pal, S. C., & Chowdhuri, I. (2019). GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Applied Sciences, 1, Article 416. https://doi.org/10.1007/s42452-019-0422-7
Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25, 747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Roy, D., Sarkar, A., Kundu, P., Paul, S., & Sarkar, B. C. (2023). An ensemble of evidence belief function (EBF) with frequency ratio (FR) using geospatial data for landslide prediction in Darjeeling Himalayan region of India. Quaternary Science Advances, 11, Article 100092. https://doi.org/10.1016/j.qsa.2023.100092
Sahin, E. K. (2022). Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping. Geocarto International, 37(9), 2441–2465. https://doi.org/10.1080/10106049.2020.1831623
Segue, W. S., Njilah, I. K., Fossi, D. H., & Nsangou, D. (2024). Advancements in mapping landslide susceptibility in Bafoussam and its surroundings area using multi-criteria decision analysis, statistical methods, and machine learning models. Journal of African Earth Sciences, 213, Article 105237. https://doi.org/10.1016/j.jafrearsci.2024.105237
Sevgen, E., Kocaman, S., Nefeslioglu, H. A., & Gokceoglu, C. (2019). A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression, ANN and random forest. Sensors, 19(18), Article 3940. https://doi.org/10.3390/s19183940
Shahzad, N., Ding, X., & Abbas, S. (2022). A comparative assessment of machine learning models for landslide susceptibility mapping in the rugged terrain of Northern Pakistan. Applied Sciences, 12, Article 2280. https://doi.org/10.3390/app12052280
Song, Y., Yang, D., Wu, W., Zhang, X., Zhou, J., Tian, Z., Wang, C., & Song, Y. (2023). Evaluating landslide susceptibility using sampling methodology and multiple machine learning models. ISPRS International Journal of Geo-Information, 12(5), Article 197. https://doi.org/10.3390/ijgi12050197
Sun, D., Wang, J., Wen, H., Ding, Y. K., & Mi, C. (2024). Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China. Journal of Rock Mechanics and Geotechnical Engineering, 16(8), 3221–3232. https://doi.org/10.1016/j.jrmge.2023.09.037
Şahin, E. K. (2018). Heyelan duyarlılık haritası için adımsal regresyona dayalı faktör seçme yönteminin etkinliğinin araştırılması. Harita Dergisi, 159, 1–15.
Tang, Y., Feng, F., Guo, Z., Feng, W., Li, Z., Wang, J., Sun, Q., Ma, H., & Li, Y. (2020). Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the Loess Plateau area in Shanxi (China). Journal of Cleaner Production, 277, Article 124159. https://doi.org/10.1016/j.jclepro.2020.124159
Teke, A., & Kavzoglu, T. (2023). Explainable artificial intelligence empowered landslide susceptibility mapping using extreme gradient boosting (XGBoost). Advanced Engineering Days, 6, 74–76.
Teke, A., & Kavzoglu, T. (2024). Exploring the decision-making process of ensemble learning algorithms in landslide susceptibility mapping: Insights from local and global explainable AI analyses. Advances in Space Research, 74, 3765–3785. https://doi.org/10.1016/j.asr.2024.06.082
Tekin, S., & Çan, T. (2019). Yapay sinir ağları yöntemi ile Ermenek Havzası’nın (Karaman) kayma türü heyelan duyarlılık değerlendirmesi. Bilge International Journal of Science and Technology Research, 3(1), 21–28.
Tezel, K., & Akgün, A. (2024). Comparing shallow landslide susceptibility maps in Northeastern Türkiye (Beşikdüzü, Trabzon): A multivariate statistical, machine learning, and physical data-based analysis. Environmental Earth Sciences, 83, Article 335. https://doi.org/10.1007/s12665-024-11627-w
Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naive Bayes models. Mathematical Problems in Engineering, 2012, Article 974638. https://doi.org/10.1155/2012/974638
Tunçdemir, V. (2018). 1:100.000 ölçekli Türkiye jeoloji haritaları serisi – Trabzon H42 paftası (No. 243). Maden Tetkik ve Arama Genel Müdürlüğü.
Türkiye İstatistik Kurumu. (2025). Adrese dayalı nüfus kayıt sistemi sonuçları. https://biruni.tuik.gov.tr/medas/?locale=tr
Xu, K., Zhao, Z., Chen, W., Ma, J., Liu, F., Zhang, Y., & Ren, Z. (2024). Comparative study on landslide susceptibility mapping based on different ratios of training samples and testing samples by using RF and FR-RF models. Natural Hazards Research, 4, 62–74. https://doi.org/10.1016/j.nhres.2023.07.004
Ullah, I., Aslam, B., Shah, S. H. I. A., Tariq, A., Qin, S., Majeed, M., & Havenith, H.-B. (2022). An integrated approach of machine learning, remote sensing, and GIS data for the landslide susceptibility mapping. Land, 11(8), Article 1265. https://doi.org/10.3390/land11081265
Usta, Z., Akıncı, H., & Akın, A. T. (2024). Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey. Earth Science Informatics, 17, 1459–1481. https://doi.org/10.1007/s12145-024-01259-w
World Health Organization. (2025). Landslides. https://www.who.int/health-topics/landslides#tab=tab_1
Wubalem, A. (2021). Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia. Geoenvironmental Disasters, 8, Article 1. https://doi.org/10.1186/s40677-020-00170-y
Yavuz Ozalp, A., Akinci, H., & Zeybek, M. (2023). Comparative analysis of tree-based ensemble learning algorithms for landslide susceptibility mapping: A case study in Rize, Turkey. Water, 15(14), Article 2661. https://doi.org/10.3390/w15142661
Yergök, A. F., Kara, H., Keskin, İ., Arslan, M., & Dönmez, M. (1998). Ünye-Fatsa, Kumru-Korgan (Ordu ili) dolayının jeolojisi. Maden Tetkik ve Arama Genel Müdürlüğü, Jeoloji Etütleri Dairesi Başkanlığı.
Yu, X., & Chen, H. (2024). Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results. Scientific Reports, 14, Article 1549. https://doi.org/10.1038/s41598-024-52145-w
Zhao, Z., Liu, Z.-Y., & Xu, C. (2021). Slope unit-based landslide susceptibility mapping using certainty factor, support vector machine, random forest, CF-SVM and CF-RF models. Frontiers in Earth Science, 9, Article 589630. https://doi.org/10.3389/feart.2021.589630
Zhou, C., Wang, Y., Cao, Y., Singh, R. P., Ahmed, B., Motagh, M., Wang, Y., Chen, L., Tan, G., & Li, S. (2024). Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique. Geocarto International, 39(1), Article 2327463. https://doi.org/10.1080/10106049.2024.2327463

Ayrıntılar

Birincil Dil

Türkçe

Konular

Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme

Bölüm

Araştırma Makalesi

Yazarlar

Hasan Tahsin Bostancı ^*
0000-0001-6975-6701
Türkiye

Yayımlanma Tarihi

26 Mart 2026

Gönderilme Tarihi

4 Aralık 2025

Kabul Tarihi

10 Şubat 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 7 Sayı: 1

DOI

https://doi.org/10.48123/rsgis.1836037

IZ

https://izlik.org/JA79MB63YE

APA

Bostancı, H. T. (2026). Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi. Türk Uzaktan Algılama ve CBS Dergisi, 7(1), 195-211. https://doi.org/10.48123/rsgis.1836037

AMA

1.Bostancı HT. Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi. Turk J Remote Sens GIS. 2026;7(1):195-211. doi:10.48123/rsgis.1836037

Chicago

Bostancı, Hasan Tahsin. 2026. “Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi”. Türk Uzaktan Algılama ve CBS Dergisi 7 (1): 195-211. https://doi.org/10.48123/rsgis.1836037.

EndNote

Bostancı HT (01 Mart 2026) Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi. Türk Uzaktan Algılama ve CBS Dergisi 7 1 195–211.

IEEE

[1]H. T. Bostancı, “Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi”, Turk J Remote Sens GIS, c. 7, sy 1, ss. 195–211, Mar. 2026, doi: 10.48123/rsgis.1836037.

ISNAD

Bostancı, Hasan Tahsin. “Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi”. Türk Uzaktan Algılama ve CBS Dergisi 7/1 (01 Mart 2026): 195-211. https://doi.org/10.48123/rsgis.1836037.

JAMA

1.Bostancı HT. Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi. Turk J Remote Sens GIS. 2026;7:195–211.

MLA

Bostancı, Hasan Tahsin. “Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi”. Türk Uzaktan Algılama ve CBS Dergisi, c. 7, sy 1, Mart 2026, ss. 195-11, doi:10.48123/rsgis.1836037.

Vancouver

1.Hasan Tahsin Bostancı. Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi. Turk J Remote Sens GIS. 01 Mart 2026;7(1):195-211. doi:10.48123/rsgis.1836037

Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.

Assessment of Landslide Susceptibility of Gümüşhane Using Machine Learning Algorithms

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Gümüşhane’nin Heyelan Duyarlılığının Makine Öğrenmesi Algoritmaları Kullanılarak Değerlendirilmesi

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