Frekans Oranı ve Yapay Sinir Ağları Yöntemleri İle Samanlı Dağları Kütlesinde Heyelan Duyarlılık Analizi

Yıl 2025, Cilt: 11 Sayı: 2, 538 - 556, 27.07.2025

Murat Uzun

https://doi.org/10.21324/dacd.1663672

Öz

Can ve mal kaybına yol açan, oluşum sıklığı yüksek ve birçok faktörden etkilenerek meydana gelen heyelanların Coğrafi Bilgi Sistemleri (CBS) yardımıyla duyarlılık analizlerin oluşturulması, olası heyelanların tahmininde önemli rol oynamaktadır. Bu çalışmada Marmara Denizi’nin doğu kesimindeki Samanlı Dağları kütlesinin heyelan duyarlılık analizi Frekans Oranı (FO) ve Yapay Sinir Ağları (YSA) yöntemleri kullanılarak oluşturulmuştur. Araştırmada ilk olarak farklı heyelan veri tabanları, uydu görüntüleri ve arazi çalışmalarından elde edilen bilgiler kullanılarak heyelan envanteri üretilmiştir. Daha sonra jeoloji, eğim, göreceli rölyef, topografik nemlilik indisi (TWI), faylara mesafe, akarsulara mesafe, Normalleştirilmiş Fark Bitki İndisi (NDVI), topografik pürüzlülük indisi (TRI), yağış ve yollara mesafe parametreleri kullanılarak FO ve YSA yöntemleri ile sahanın heyelan duyarlılık analizi yapılmıştır. Samanlı Dağları heyelan duyarlılık sonuçları her iki yöntemde de 5 seviyeli olarak üretilmiştir. Modellerin doğruluk analizi için alıcı çalışma karakteristiği (ROC) kullanılmıştır. Frekans Oranı yöntemine göre çalışma sahasının %38’î çok düşük-düşük, % 43’ü orta, % 19’u yüksek-çok yüksek düzeyde heyelan duyarlılığını barındırmaktadır. YSA modeline göre ise çalışma sahasının %27’î çok düşük-düşük, % 45’i orta, % 27’i yüksek-çok yüksek düzeyde heyelan duyarlılığını sahiptir. Her iki modelde de heyelan duyarlılığın yüksek olduğu sahalar, Çınarcık-Yalova-Karamürsel arasındaki havzalarda, Gölcük-Karamürsel kıyı kuşağı ve İznik-Pamukova-Geyve-Arifiye hattında yoğunlaşmaktadır. Çalışma sahasında heyelanı tetikleyen temel parametreler ise tortul litolojik birimler, fay ve yollara yakınlık ile eğim değerleridir. Araştırmada ROC doğruluk modeline göre örneklem sahası olan Samanlı Dağları kütlesi kapsamında heyelan duyarlılık modellerinden YSA’nın FO’ya göre daha başarılı olduğu saptanmıştır.

Anahtar Kelimeler

Heyelan Duyarlılık Analizi , Frekans Oranı (FO) , Yapay Sinir Ağları (YSA) , Samanlı Dağları , Coğrafi Bilgi Sistemleri (CBS)

Landslide Susceptibility Analysis in Samanlı Mountains Massif by Frequency Ratio and Artificial Neural Networks Methods

Öz

Susceptibility analyses of landslides, which cause loss of life and property, have a high frequency of occurrence and are affected by many factors, play an important role in the prediction of possible landslides with the help of Geographic Information Systems (GIS). In this study, the landslide susceptibility analysis of the Samanlı Mountains massif in the eastern part of the Marmara Sea was created using Frequency Ratio (FR) and Artificial Neural Networks (ANN) methods. Firstly, landslide inventory was produced by using information obtained from different landslide databases, satellite images and field studies. Then, landslide susceptibility analysis of the site was carried out with FR and ANN methods using geology, slope, relative relief, topographic moisture index (TWI), distance to faults, distance to streams, Normalised Difference Vegetation Index (NDVI), topographic roughness index (TRI), rainfall and distance to roads parameters. Samanlı Mountains landslide susceptibility results were produced with 5 levels in both methods. Receiver operating characteristic (ROC) was used for accuracy analysis of the models. According to the Frequency Ratio method, 38% of the study area has very low-low landslide susceptibility, 43% has medium landslide susceptibility and 19% has high-high landslide susceptibility. According to ANN model, 27% of the study area has very low-low landslide susceptibility, 45% has medium landslide susceptibility, and 27% has high-high landslide susceptibility. In both models, the areas with high landslide susceptibility are concentrated in the basins between Çınarcık-Yalova-Karamürsel, Gölcük-Karamürsel coastal belt and İznik-Pamukova-Geyve-Arifiye line. The main parameters that trigger landslides in the study area are sedimentary lithological units, proximity to faults and roads and slope values. In the study, according to the ROC accuracy test, ANN was found to be more successful than FO among the landslide susceptibility models within the scope of the Samanlı Mountains mass, which is the sample area.

Anahtar Kelimeler

Landslide Susceptibility Analysis , Frequency Ratio (FR) , Artificial Neural Networks (ANN) , Samanlı Mountains , Geographic Information Systems (GIS)

Kaynakça

• Ado, M., Amitab, K., Maji, A. K., Jasińska, E., Gono, R., Leonowicz, Z. & Jasiński, M. (2022). Landslide susceptibility mapping using machine learning: a literature survey. Remote Sensing, 14(13), Article 3029. https://doi.org/10.3390/rs14133029
• Akgün, A., Dağ, S., & Bulut, F. (2008). Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environmental Geology, 54, 1127–1143. https://doi.org/10.1007/s00254-007-0882-8
• Akıncı, H., Yavuz Özalp, A., & Temuçin Kılıçer, S. (2015). 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
• Akıncı, H., Doğan, S. & Kılıçoğlu, C. (2017). Landslide susceptıbılıty mapping of Canik (Samsun) dıstrıct usıng bayesıan probabılıty and frequency ratio models. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 5(3), 283–299. https://doi.org/10.15317/Scitech.2017.89
• Aleotti, P., & Chowdhury, R. N. (1999). Landslide hazard assessments: summary review and new perspectives. Bulletin of Engineering Geology and the Environment. 58, 21–44. https://doi.org/10.1007/s100640050066
• Ali, A. (2024). Landslide susceptibility mapping using modified frequancy ratio method in Correb area, South Wollo, North-Western Ethiopia. Discover Geoscience, 2, Article 45. https://doi.org/10.1007/s44288-024-00053-x
• Alvioli, M., Loche, M., Jacobs, L., Grohmann, C. H., Abraham, M. T., Gupta, K., Satyam, N., Scaringi, G., Bornaetxea, T., Rossi, M., Marchesini, I., Lombardo, L., Moreno, M., Steger, S., Camera, C. A. S., Bajni, G., Samodra, G., Wahyudi, E. E., Susyanto, N., & Rivera-Rivera, J. (2024). A benchmark dataset and workflow for landslide susceptibility zonation. Earth-Science Reviews, 258, Article 104927. https://doi.org/10.1016/j.earscirev.2024.104927
• Aydoğan, E., & Dağ, S. (2023). İstatistiksel Yöntemlerle Yukarı Karasu Havzası’nın Kuzeydoğu Bölümünün (Erzurum) Heyelan Duyarlılık Analizi. Türk Uzaktan Algılama ve CBS Dergisi, 4(1), 64–82. https://doi.org/10.48123/rsgis.1202140
• Bargu, S. (1997). İzmit Körfezindeki pleistosen taraçaları ve tektonik özellikler. İstanbul Üniversitesi Mühendislik Fakültesi Yerbilimleri Dergisi, 10, 1–33.
• Basheer, M., & Oommen, T. (2024). PyLandslide: A python tool for landslide susceptibility mapping and uncertainty analysis. Environmental Modelling and Software, 177, Article 106055. https://doi.org/10.1016/j.envsoft.2024.106055
• Bayrakdar, C., & Görüm, T. (2012). Yeşil Göl heyelanının jeomorfolojik özellikleri ve oluşum mekanizması. Türk Coğrafya Dergisi, (59), 1–10. https://doi.org/10.17211/tcd.06398
• Bhandari, B. P., Dhakal, S., & Tsou, C.-Y. (2024). Assessing the prediction accuracy of frequency ratio, weight of evidence, shannon entropy, and ınformation value methods for landslide susceptibility in the Siwalik Hills of Nepal. Sustainability, 16(5), Article 2092. https://doi.org/10.3390/su16052092
• Bilgin, T. (1967). Samanlı Dağları: Coğrafi Etüd. İstanbul Üniversitesi Edebiyat Fakültesi Yayını.
• Bisht, S., Rawat, K. S., & Singh, S. K. (2024). Earth observation data and GIS based landslide susceptibility analysis through frequency ratio model in lesser Himalayan region, India. Quaternary Science Advances, 13, Article 100141. https://doi.org/10.1016/j.qsa.2023.100141
• Bragagnolo, L, da Silva, R. V., Grzybowski, J. M. V. (2020). Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks, Environmental Modelling and Software, 123, Article 104565. https://doi.org/10.1016/j.envsoft.2019.104565
• Cantarino, I., Carrion, M. A., Martínez-Ibáñez, V., & Gielen, E. (2023). Improving landslide susceptibility assessment through frequency ratio and classification methods—case study of Valencia Region (Spain). Applied Sciences, 13(8), Article 5146. https://doi.org/10.3390/app13085146
• Cihangir, M. E., & Görüm, T. (2016). Kelkit vadisinin aşağı çığırında gelişmiş heyelanların dağılım deseni ve oluşumlarını kontrol eden faktörler. Türk Coğrafya Dergisi, (66), 19–28. https://doi.org/10.17211/tcd.84731
• Cihangir, M. E., Görüm, T., & Nefeslioğlu, H. A. (2018). Heyelan tetikleyici faktörlerine bağlı mekânsal hassasiyet değerlendirmesi. Türk Coğrafya Dergisi, (70), 133–142. https://doi.org/10.17211/tcd.410998
• Cihangir, M. E. (2024). Aras Nehri havzasında lojistik regresyon yöntemiyle kayma ve akma tip heyelan duyarlılığı değerlendirmesi. Türk Coğrafya Dergisi, (85), 55–68. https://doi.org/10.17211/tcd.1475065
• Chen, W., Wang, J., Xie, X., Hong, H., Van Trung, N., Bui, D.T., Wang, G., & Li, X. (2016). Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environmental Earth Sciences, 75 (20), Article 1344. https://doi.org/10.1007/s12665-016-6162-8
• Dağ, S., Akgün, A., Kaya, A., Alemdağ, A., & Bostancı, H. T. (2020). Medium scale earthflow susceptibility modelling by remote sensing and geographical information systems based multivariate statistics approach: an example from Northeastern Turkey. Environmental Earth Sciences, 79, Article 468. https://doi.org/10.1007/s12665-020-09217-7
• Dalkes, M., & Korkmaz, M. S. (2023). Analitik Hiyerarşi Süreci ve Frekans Oranı yöntemlerinin heyelan duyarlılık analizinde karşılaştırılması: Trabzon İli Akçaabat ve Düzköy İlçeleri Örneği. Doğal Afetler ve Çevre Dergisi, 9(1), 16–38. https://doi.org/10.21324/dacd.1105000
• Demirbilek, S., & Turoğlu, H. (2024). Frekans Oranı yöntemi kullanılarak Arsuz Çayı Havzası heyelan duyarlılık analizi. Jeomorfolojik Araştırmalar Dergisi, 13, 23–39. https://doi.org/10.46453/jader.1496249
• Erol, O., & Kayacılar, C., (1994). İzmit Körfezi Güneyi Karamürsel-Halıdere Çevresinin Jeomorfolojisi. Türk Coğrafya Dergisi, 29, 11–17.
• Froude M. J., & Petley D. (2018). Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences, 18, 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018
• Fidan, S., & Görüm, T. (2020). Türkiye’de ölümcül heyelanların dağılım karakteristikleri ve ulusal ölçekte öncelikli alanların belirlenmesi. Türk Coğrafya Dergisi, 74, 123–134. https://doi.org/10.17211/tcd.731596
• Gong, W., Hu, M., Zhang, Y., Tang, H., Liu, D. & Song, Q. (2021). GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China. International Journal of Environmental Science and Technology, 19, 7803–7820. https://doi.org/10.1007/s13762-021-03572-z
• Gökçeoğlu, C., & Ercanoğlu, M., (2001). Heyelan duyarlılık haritalarının hazırlanmasında kullanılan parametrelere ilişkin belirsizlikler. Yerbilimleri, 23, 189–206.
• Gönençgil, B., & Halis, O. (2021). Samanlı Dağları’nın jeomorfolojik gelişimine ve uzun dönemli erozyon süreçlerine morfometrik yaklaşım. Türk Coğrafya Dergisi, 78, 109–126. https://doi.org/10.17211/tcd.1008678
• Gorum, T., Gonencgil, B., Gokceoglu, C., & Nefeslioglu, H. A. (2008). Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey). Natural Hazards, 46(3), 323–351. https://doi.org/10.1007/s11069-007-9190-6
• Görüm, T., & Fidan, S. (2021). Spatiotemporal variations of fatal landslides in Turkey. Landslides 18, 1691–1705. https://doi.org/10.1007/s10346-020-01580-7
• Gulbet, E., & Getahun, B. (2024). Landslide susceptibility mapping using frequency ratio and analytical hierarchy process method in Awabel Woreda, Ethiopia. Quaternary Science Advances, 16, Article 100246. https://doi.org/10.1016/j.qsa.2024.100246
• Guzzetti, F., Carrara, A., Cardinal i, M., & Reichenbac H. P. (1999). Landslide hazard evaluati on: a review of current techniques and their application in a mu lti-scale study, Central Italy. Geomorphology, 31(1), 181–216. https://doi.org/10.1016/S0169-555X(99)00078-1
• Guzzetti, F., Mondini, A.C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K.T. (2012). Landslide inventory maps: New tools for an old problem. Earth-Science Reviews, 112, 42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
• Güneş, B., & Kömüşcü, A. Ü. (2025). Coğrafi Bilgi Sistemleri (CBS) Tabanlı AHS yöntemi ile Erzincan-Sivas Karayolu ve Çevresinin Heyelan Duyarlılık Analizi. Doğal Afetler ve Çevre Dergisi, 11(1), 89–103. https://doi.org/10.21324/dacd.1478173
• Günini, N. Ü., & Öztürk, D. (2021). Van ili heyelan duyarlılığının frekans oranı yöntemiyle analizi. Bursa Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 26(3), 865–884. https://doi.org/10.17482/uumfd.969246
• He, W., Chen, G., Zhao, J., Lin, Y., Qin, B., Yao, W., & Cao, Q. (2023). Landslide susceptibility evaluation of machine learning based on ınformation volume and frequency ratio: A Case Study of Weixin County, China. Sensors, 23(5), Article 2549. https://doi.org/10.3390/s23052549
• Hong, H., Junzhi, L. & Xing, A. Z., (2020). Modeling landslide susceptibility using logit Boost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Science of The Total Environment, 718, 3–15.
• Huang, F., Mao, D., Jiang, S.-H., Zhou, C., Fan, X., Zeng, Z., Catani, F., Yu, C., Chang, Z., Huang, J., Jiang, B., & Li, Y. (2024). Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules. Geoscience Frontiers, 15(6), Article 101886. https://doi.org/10.1016/j.gsf.2024.101886
• Hoşgören, M. Y. (1995). İzmit Körfezi Havzasının Jeomorfolojisi. In E. Meriç (Ed.), İzmit Körfezi Kuvaterner İstifi (pp. 343-348), Deniz Harp Okulu Komutanlığı Basımevi.
• Huangfu, W., Qiu, H., Wu, W., Qin, Y., Zhou, X., Zhang, Y., Ullah, M. & He, Y. (2024). Enhancing the performance of landslide susceptibility mapping with frequency ratio and gaussian mixture model. Land, 13(7), Article 1039. https://doi.org/10.3390/land13071039
• Karaman, M.O., Çabuk, S.N., & Pekkan, E. (2022). Utilization of frequency ratio method for the production of landslide susceptibility maps: Karaburun Peninsula case, Turkey. Environmental Science and Pollution Research, 29, 91285–91305. https://doi.org/10.1007/s11356-022-21931-2
• Ke, C., He, S., & Qin, Y. (2023). Comparison of natural breaks method and frequency ratio dividing attribute intervals for landslide susceptibility mapping. Bulletin of Engineering Geology and the Environment, 82, Article 384. https://doi.org/10.1007/s10064-023-03392-0
• Khan, I., Kainthola, A., Bahuguna, H., & Asgher, M. S., (2024). Comparative landslide susceptibility assessment using information value and frequency ratio bivariate statistical methods: a case study from Northwestern Himalayas, Jammu and Kashmir, India. Arabian Journal of Geosciences, 17, Article 231. https://doi.org/10.1007/s12517-024-12022-2
• Kılıçoğlu, C. (2020). Frekans Oranı Metodu ve Bayesyen Olasılık Modeli Kullanılarak Samsun İli Vezirköprü İlçesinin Heyelan Duyarlılık Haritasının Üretilmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 20(1), Article 138. https://doi.org/10.35414/akufemubid.658662
• Kohonen T. (1988). An Introduction to Neural Computing. Neural Networks, 1, 3–6.
• Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47(7), 982–990. https://doi.org/10.1007/s00254-005-1228-z
• Lippman, R. (1987). An Introduction to Computing with Neural Nets. IEEE ASSP Magazine, 4, 4-22.
• Liu, L. L., Zhang, Y. L., & Xiao, T. (2022). A frequency ratio–based sampling strategy for landslide susceptibility assessment. Bulletin of Engineering Geology and the Environment, 81, Article 360. https://doi.org/10.1007/s10064-022-02836-3
• Livingstone, D. J. (2009). Artificial Neural Networks Methods and Applications. Humana Totowa, NJ.
• Mao, Y., Li, Y., Teng, F., Sabonchi, A. K. S., Azarafza, M., & Zhang, M. (2024). Utilizing hybrid machine learning and soft computing techniques for landslide susceptibility mapping in a drainage basin. Water, 16(3), Article 380. https://doi.org/10.3390/w16030380
• Mark, D.M. (1975). Geomorphometric Parameters: A Review and Evaluation, Geografiska Annaler, 57, 165-177.
• Meten, M., Bhandary, N. P. & Yatabe, R., (2015). GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of debre sina area in Central Ethiopia. Journal of Mountain Science, 12(6), Article 1355. https://doi.org/10.1007/s11629-015-3464-3
• Myneni, R. B., Hall, F. G., Sellers, P. J., & Marshak, A. L. (1995). The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 481–486.
• Oyda, Y., Jothimani, M. & Regasa, H. (2024). Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: a GIS-based frequency ratio analysis. Journal of Degraded and Mining Lands Management, 11(3), Article 5885. https://doi.org/10.15243/jdmlm.2024.113.5885
• Özşahin, E., (2015). Coğrafi Bilgi Sistemleri (CBS) ve Analitik Hiyerarşi Süreci (AHS) Kullanılarak Tekirdağ Şehrinin Heyelan Duyarlılık Analizi. Eurasian Academy of Sciences Social Sciences Journal, 6, 50–71. http://doi.org/10.17740/eas.soc.2015‐V6‐4
• Özgür Kocaeli. (2018, Ekim 29). İlçe halkı heyelan faciasıyla burun buruna yaşıyor. https://www.ozgurkocaeli.com.tr/haber/4571076/ ilce-halki-heyelan-faciasiyla-burun-buruna-yasiyor
• Parker, A. J. (1982). The topographic relative moisture index: an approach to soil moisture assessment in mountain terrain. Physical Geography, 3(2), 160–168.
• Pham, B.T., Nguyen-Thoi, T., Qi, C., Phong, T.V., Dou, J., Ho, L.S., Le, H.V., & Prakash, I. (2020). Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena 195, Article 104805. https://doi.org/10.1016/j.catena.2020.104805
• Pourghasemi, H.R., Kornejady, A., Kerle, N., & Shabani, F. (2020). Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena 187, Article 104364. https://doi.org/10.1016/j.catena.2019.104364
• Riley S. J, De Gloria S. D, & Elliot R. (1999). A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Sciences, 5, 23–27.
• Rodrigues Neto, J. M. d. S., & Bhandary, N. P. (2024). Landslide Susceptibility Assessment by Machine Learning and Frequency Ratio Methods Using XRAIN Radar-Acquired Rainfall Data. Geosciences, 14(6), Article 171. https://doi.org/10.3390/geosciences14060171
• Sabah Gazetesi. (2022, Şubat 11). Çınarcık-Armutlu yolundaki heyelan kaldırma çalışmalarına ara verildi. https://www.sabah.com.tr/ yalova/2022/02/11/cinarcik-armutlu-yolundaki-heyelan-kaldirma-calismalarina-ara-verildi
• Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., Ardizzone, F., & Rossi, M. (2017). Characterization and quantification of path dependency in landslide susceptibility. Geomorphology, 292, 16–24. https://doi.org/10.1016/j.geomorph.2017.04.039
• Shano, L., Raghuvanshi, T.K., & Meten, M. (2021). Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia. Arabian Journal of Geosciences, 14, Article 623. https://doi.org/10.1007/s12517-021-06995-7
• Sharma, A., Sajjad, H., & Rahaman, M.H. (2024). Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region. Journal of Mountain Science, 21, 2368–2393. https://doi.org/10.1007/s11629-024-8651-7
• Silalahi, F. E. S., Pamela, P. & Arifianti, Y. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Official Journal of the Asia Oceania Geosciences Society, 6, Article 10. https://doi.org/10.1186/s40562-019-0140-4
• Sonker, I., Tripathi, J. N., & Swarnim. (2022). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio method in Sikkim Himalaya. Quaternary Science Advances, 8, Article 100067. https://doi.org/10.1016/j.qsa.2022.100067
• Shu, H., Guo, Z., Qi, S., Song, D., Pourghasemi, H.R., & Ma, J. (2021). Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China. Remote Sensing, 13, Article 3623. https://doi.org/10.3390/rs13183623
• Sun, D. L., Wen, H. J., Wang, D. Z., & Xu, J. H. (2020). A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology, 362, Article 107201. https://doi.org/10.1016/j.geomorph.2020.107201
• Şahin, C., Sipahioğlu, Ş, (2007). Doğal afetler ve Türkiye. Gündüz Eğitim ve Yayıncılık.
• Tang, T. C., & Chi, L. C. (2005). Neural networks analysis in business failure prediction of chinese importers: a between-countries approach. Expert Systems with Applications, 29, 244–255. https://doi.org/10.1016/j.eswa.2005.03.003.
• Taşkanat, T. (2012, 17–19 Kasım). Frekans Oranı ve Kanıt Ağırlığı Metotları Kullanılarak Heyelan Duyarlılık Analizi, Kayseri Küpeli Mahallesi Örneği [Bildiri sunumu]. VIII. Uzaktan Algılama ve CBS Sempozyumu, Ankara.
• Taşoğlu, E., Öztürk, M. Z., & Yazıcı, Ö. (2024). High Resolution Köppen-Geiger Climate Zones of Türkiye. International Journal of Climatology, 44(14), 5248–5265. https://doi.org/10.1002/joc.8635
• Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
• Utlu, M. (2023). Frekans Oranı ve Shannon Entropisi Yöntemi Kullanarak Ezine Çayı Havzası Taşkın Duyarlılık Analizi (Kastamonu-Bozkurt). Jeomorfolojik Araştırmalar Dergisi (11), 160–178. https://doi.org/10.46453/jader.1358845
• Uzun, M. (2021a, 17–18 Haziran). Gölcük ve Karamürsel’de (Kocaeli) Antropojenik Kökenli Jeomorfolojik Değişimlerin Potansiyel Risk Analizi [Bildiri sunumu]. 2. İstanbul Uluslararası Coğrafya Kongresi, İstanbul, Türkiye.
• Uzun, M. (2021b). Human-Induced Geomorphological Changes and Processes on the Coasts of the Gulf of Izmit. Journal of Geomorphological Researches, 7, 61–81. https://doi.org/10.46453/jader.983465
• Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3), 112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
• Varnes, D. J. (1978). Slope movement types and processes. Special report, 176, 11–33.
• Wang, Y., Sun, D., Wen, H., Zhang, H., & Zhang, F. (2020). Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China). International Journal of Environmental Research and Public Health, 17, Article 4206. https://doi.org/10.3390/ijerph17124206
• Wang, X., Nie, W., Xie, W. & Zhang, Y., (2024). Incremental learning-random forest model-based landslide susceptibility analysis: A case of Ganzhou City, China. Earth Science Informatics, 17, 1645–1661. https://doi.org/10.1007/s12145-024-01229-2
• Yeni Gölcük. (2016, Aralık 30). Ulaşlı'da heyelan nedeni ile yol kapalı. https://www.yenigolcuk.com/ulaslida-heyelan-nedeni-ile-yol-kapali-21690-haberi
• Zho, Z., He, Y., Yao, S., Yang, W., Wnag, W., Zhang, L., Sun, Q., (2022). A comparative study of different neural network models for landslide susceptibility mapping. Advances in Space Research, 70, 383–401. https://doi.org/10.1016/j.asr.2022.04.055