Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 8 Sayı: 1, 42 - 54, 10.04.2023
https://doi.org/10.29128/geomatik.1108735

Öz

Kaynakça

  • Abeysiriwardana, H. D., & Gomes, P. I. A. (2022). Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: using logistic regression. Journal of Mountain Science, 19(2), 477–492. https://doi.org/10.1007/s11629-021-6988-8
  • Aditian, A., Kubota, T., & Shinohara, Y. (2018). Geomorphology Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and arti fi cial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
  • Acar, U., Yilmaz, O. S., Çelen, M., Ateş, A. M., Gülgen, F. & Şanli, F. B. (2021). Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine. International Journal of Environment and Geoinformatics, 8(4), 423–434. doi:10.30897/ijegeo.
  • Aghlmand, M., Onur, M. İ., & Talaei, R. (2020). Heyelan Duyarlılık Haritalarının Üretilmesinde Analitik Hiyerarşi Yönteminin Ve Coğrafi Bilgi Sistemlerinin Kullanımı. European Journal of Science and Technology, 224–230. https://doi.org/10.31590/ejosat.araconf28
  • Akıncı, H., Özalp, A. Y., & Kılıçer, S. T. (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.
  • Al Kalbani, K., & Rahman, A. A. (2022). 3D city model for monitoring flash flood risks in Salalah, Oman. International Journal of Engineering and Geosciences, 7(1), 17-23.
  • Alptekin, A., & Yakar, M. (2020). Türkiye İnsansız Hava Araçları Dergisi Heyelan Bölgesinin İHA Kullanarak M odellenmesi Modelling of a Landslide Site Using a UAV. Türkiye İnsansız Hava Araçları Dergisi, 2(1), 17–21.
  • Alqadhi, S., Mallick, J., Talukdar, S., Bindajam, A. A., Van Hong, N., & Saha, T. K. (2022). Selecting optimal conditioning parameters for landslide susceptibility: experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environmental Science and Pollution Research, 29(3), 3743–3762. https://doi.org/10.1007/s11356-021-15886-z
  • Avcı, V. (2016). Gökdere Havzası ve Çevresinin (Bingöl Güneybatısı) Frekans Oranı Metoduna Göre Heyelan Duyarlılık Analizi. Marmara Coğrafya Dergisi, 34, 160–177.
  • Aydınoğlu, A., & Altürk, G. (2021). Heyelan Duyarlılık Haritalarının İstatistik ve Makine Öğrenmesi Teknikleri Kullanılarak Üretilmesi: Taşlıdere Havzası Örneği (Rize). Coğrafya Dergisi / Journal of Geography, 43, 159–176. https://doi.org/10.26650/jgeog2021-814561
  • Berna, T., Orhan, O., & Tekin, S. (2021). Yapay Sinir Ağları Yöntemi ile Adıyaman Gölbaşı-Adıyaman Merkez Arasının Heyelan Duyarlılık Değerlendirmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(3), 701–708.
  • Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability (Switzerland), 8(9), 948. https://doi.org/10.3390/su8090948
  • Chandra, S., & Indrajit, P. (2019). GIS ‑ based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Applied Sciences, 1(5), 1–25. https://doi.org/10.1007/s42452-019-0422-7
  • Chen, W., & Zhang, S. (2021). GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling. Catena, 203, 105344. https://doi.org/10.1016/j.catena.2021.105344
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  • Kılıçoğlu, C. (2020). Frekans Oranı Metodu ve Bayesyen Olasılık Modeli Kullanılarak Samsun İli Vezirköprü İlçesinin Heyelan Duyarlılık Haritasının Üretilmesi. Afyon Kocatepe University Journal of Sciences and Engineering, 20(1), 138–154. https://doi.org/10.35414/akufemubid.658662
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Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği

Yıl 2023, Cilt: 8 Sayı: 1, 42 - 54, 10.04.2023
https://doi.org/10.29128/geomatik.1108735

Öz

Bu çalışmada 2009 yılında meydana gelen ve afet bölgesi olarak ilan edilen Manisa ili, Demirci ilçesi sınırlarında bulunan Tekeleler köyünün heyelan duyarlılık haritası coğrafi bilgi sistemi tabanlı frekans oranı yöntemi kullanılarak üretilmiştir. Heyelan duyarlılık analizinde yağış, eğim, bakı, yükseklik, akarsuya uzaklık, yola uzaklık, arazi kullanımı, litoloji, eğrisellik, topografik nemlilik indeksi, normalize edilmiş fark bitki örtüsü indeksi koşullandırma faktörleri olarak seçilmiştir. Heyelan olan bölgeden Google Earth görüntüleri kullanılarak örnek rastgele noktalar belirlenmiş, belirlenen noktalar %70’i eğitim %30’u test için iki sınıfa bölünmüştür. Üretilen heyelan duyarlılık haritası çok düşük, düşük, orta, yüksek ve çok yüksek olmak üzere beş farklı sınıfa ayrılmıştır. Bu sınıflar içerisinde kalan alanlar sırasıyla tüm alanın %11,36, %39,61, %34,32, %12,89 ve %1,81’ini kapladığı görülmüştür. Heyelan duyarlılık haritasının doğruluğu alıcı işletim karakteristiği eğrisi altında kalan alan dikkate alınarak hesaplanmıştır. AUC değeri başarı oranı %95,14 ve tahmin oranı %94,11 olarak bulunmuştur. Bu çalışma ile frekans oranı yöntemi kullanılarak heyelan duyarlılık haritalarının başarılı bir şekilde üretilebileceği gösterilmiştir. Ayrıca bulunan sonuç haritanın olası muhtemel heyelanlar için bir öngörü niteliğinde olduğu, afet yönetim ve planlama çalışmalarına entegre edilebileceği sonucuna varılmıştır.

Kaynakça

  • Abeysiriwardana, H. D., & Gomes, P. I. A. (2022). Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: using logistic regression. Journal of Mountain Science, 19(2), 477–492. https://doi.org/10.1007/s11629-021-6988-8
  • Aditian, A., Kubota, T., & Shinohara, Y. (2018). Geomorphology Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and arti fi cial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
  • Acar, U., Yilmaz, O. S., Çelen, M., Ateş, A. M., Gülgen, F. & Şanli, F. B. (2021). Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine Determination of Mucilage in The Sea of Marmara Using Remote Sensing Techniques with Google Earth Engine. International Journal of Environment and Geoinformatics, 8(4), 423–434. doi:10.30897/ijegeo.
  • Aghlmand, M., Onur, M. İ., & Talaei, R. (2020). Heyelan Duyarlılık Haritalarının Üretilmesinde Analitik Hiyerarşi Yönteminin Ve Coğrafi Bilgi Sistemlerinin Kullanımı. European Journal of Science and Technology, 224–230. https://doi.org/10.31590/ejosat.araconf28
  • Akıncı, H., Özalp, A. Y., & Kılıçer, S. T. (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.
  • Al Kalbani, K., & Rahman, A. A. (2022). 3D city model for monitoring flash flood risks in Salalah, Oman. International Journal of Engineering and Geosciences, 7(1), 17-23.
  • Alptekin, A., & Yakar, M. (2020). Türkiye İnsansız Hava Araçları Dergisi Heyelan Bölgesinin İHA Kullanarak M odellenmesi Modelling of a Landslide Site Using a UAV. Türkiye İnsansız Hava Araçları Dergisi, 2(1), 17–21.
  • Alqadhi, S., Mallick, J., Talukdar, S., Bindajam, A. A., Van Hong, N., & Saha, T. K. (2022). Selecting optimal conditioning parameters for landslide susceptibility: experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environmental Science and Pollution Research, 29(3), 3743–3762. https://doi.org/10.1007/s11356-021-15886-z
  • Avcı, V. (2016). Gökdere Havzası ve Çevresinin (Bingöl Güneybatısı) Frekans Oranı Metoduna Göre Heyelan Duyarlılık Analizi. Marmara Coğrafya Dergisi, 34, 160–177.
  • Aydınoğlu, A., & Altürk, G. (2021). Heyelan Duyarlılık Haritalarının İstatistik ve Makine Öğrenmesi Teknikleri Kullanılarak Üretilmesi: Taşlıdere Havzası Örneği (Rize). Coğrafya Dergisi / Journal of Geography, 43, 159–176. https://doi.org/10.26650/jgeog2021-814561
  • Berna, T., Orhan, O., & Tekin, S. (2021). Yapay Sinir Ağları Yöntemi ile Adıyaman Gölbaşı-Adıyaman Merkez Arasının Heyelan Duyarlılık Değerlendirmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(3), 701–708.
  • Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability (Switzerland), 8(9), 948. https://doi.org/10.3390/su8090948
  • Chandra, S., & Indrajit, P. (2019). GIS ‑ based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India. SN Applied Sciences, 1(5), 1–25. https://doi.org/10.1007/s42452-019-0422-7
  • Chen, W., & Zhang, S. (2021). GIS-based comparative study of Bayes network, Hoeffding tree and logistic model tree for landslide susceptibility modeling. Catena, 203, 105344. https://doi.org/10.1016/j.catena.2021.105344
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  • Gao, Z. & Ding, M. (2022). Application of convolutional neural network fused with machine learning modeling framework for geospatial comparative analysis of landslide susceptibility. Natural Hazards, 1-26. doi:10.1007/s11069-022-05326-7
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  • 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, 1-18. https://doi.org/10.1007/s13762-021-03572-z
  • Günini Üzel, N., & Ötürk, D. (2021). Van İli̇ Heyelan Duyarliliğinin Frekans Orani Yöntemi̇yle Anali̇zi̇. Bursa Uludağ Üniversitesi Mühendsilik Fakültesi Dergisi, 26(3), 865–884. https://doi.org/10.17482/uumfd.969246
  • Hang, H. T., Hoa, P. D., Tru, V. N., & Phuong, N. V. (2021). Landslide Susceptibility Mapping Along National Highway-6, Hoa Binh Province, Vietnam Using Frequency Ratio Model and Gis. International Journal of GEOMATE, 21(85), 84–90. https://doi.org/10.21660/2021.85.j2222
  • Hepdeniz, K., & Soyaslan, İ. İ. (2018). CBS ve Frekans Oranı Yöntemi Kullanılarak Isparta-Burdur Dağ Yolu Heyelan Duyarlılığının Değerlendirilmesi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 9(2), 179–186. https://doi.org/10.29048/makufebed.414392
  • Huang, W., DeVries, B., Huang, C., Lang, M. W., Jones, J. W., Creed, I. F., & Carroll, M. L. (2018). Automated extraction of surface water extent from Sentinel-1 data. Remote Sensing, 10(5), 1–18. https://doi.org/10.3390/rs10050797
  • Jeyaseelan, A. T. (2003). Droughts & floods assessment and monitoring using remote sensing and GIS. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, 291.
  • 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, 1–19. https://doi.org/10.1007/s13369-022-06560-8
  • Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947–987. https://doi.org/10.1007/s11069-016-2357-2
  • Kılıçoğlu, C. (2020). Frekans Oranı Metodu ve Bayesyen Olasılık Modeli Kullanılarak Samsun İli Vezirköprü İlçesinin Heyelan Duyarlılık Haritasının Üretilmesi. Afyon Kocatepe University Journal of Sciences and Engineering, 20(1), 138–154. https://doi.org/10.35414/akufemubid.658662
  • Kim, H. G., Lee, D. K., Park, C., Ahn, Y., Kil, S. H., Sung, S. & Biging, G. S. (2018). Estimating landslide susceptibility areas considering the uncertainty inherent in modeling methods. Stochastic Environmental Research and Risk Assessment, 32(11), 2987-3019. https://doi.org/10.1007/s00477-018-1609-y
  • Koç, E., & Küçükönder, M. (2021). Erkenez Havzası CBS Matris Yöntemi ile Heyelan Duyarlı lık Değerlendirmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(1), 141–154.
  • Li, B., Wang, N., & Chen, J. (2021). GIS-Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China. Advances in Civil Engineering. https://doi.org/10.1155/2021/4758062
  • Li, L., Nahayo, L., Habiyaremye, G., & Christophe, M. (2022). Applicability and performance of statistical index, certain factor and frequency ratio models in mapping landslides susceptibility in Rwanda. Geocarto International, 37(2), 638–656. https://doi.org/10.1080/10106049.2020.1730451
  • Liang, J., Xie, Y., Sha, Z., & Zhou, A. (2020). Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE). Computers, Environment and Urban Systems, 84, 101542. https://doi.org/10.1016/j.compenvurbsys.2020.101542
  • Mallick, J., Alqadhi, S., Talukdar, S., Alsubih, M., Ahmed, M., Khan, R. A., Kahla, N. Ben, & Abutayeh, S. M. (2021). Risk assessment of resources exposed to rainfall induced landslide with the development of gis and rs based ensemble metaheuristic machine learning algorithms. Sustainability (Switzerland), 13(2), 1–30. https://doi.org/10.3390/su13020457
  • Mandal, S., & Mandal, K. (2018). Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India. Modeling Earth Systems and Environment, 4(1), 69–88. https://doi.org/10.1007/s40808-018-0426-0
  • Maqsoom, A., Aslam, B., Khalil, U., Abbas, Z., Sheheryar, K., & Tahir, A. (2021). Landslide susceptibility mapping along the China Pakistan Economic Corridor (CPEC) route using multi ‑ criteria decision ‑ making method. Modeling Earth Systems and Environment, 1–15. https://doi.org/10.1007/s40808-021-01226-0
  • Melese, T., Belay, T., & Andemo, A. (2022). Application of analytical hierarchal process, frequency ratio, and Shannon entropy approaches for landslide susceptibility mapping using geospatial technology: The case of Dejen district, Ethiopia. Arabian Journal of Geosciences, 15(5), 1–21. https://doi.org/10.1007/s12517-022-09672-5
  • Mersha, T., & Meten, M. (2020). GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental Disasters, 7(1), 1–22. https://doi.org/10.1186/s40677-020-00155-x
  • Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30. https://doi.org/10.1002/hyp.3360050103
  • Nohani, E., Moharrami, M., Sharafi, S., Khosravi, K., Pradhan, B., Pham, B. T., Lee, S., & Melesse, A. M. (2019). Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models. Water, 11(7), 1402. https://doi.org/10.3390/w11071402
  • Oğuz, E., Oğuz, K. & Öztürk, K. (2022). Düzce bölgesi taşkın duyarlılık alanlarının belirlenmesi. Geomatik, 7 (3), 220-234.
  • Özşahin, E. (2015). Landslide Susceptibility Analysis by Geographical Information Systems: The Case of Ganos Mount (Tekirdağ). Harita Teknolojileri Elektronik Dergisi, 2015(1), 47–63. https://doi.org/10.15659/hartek.15.04.68
  • 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(5), 1–25. https://doi.org/10.1007/s42452-019-0422-7
  • Patel, N. N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F. R., Tatem, A. j., & Trianni, G. (2015). Multitemporal settlement and population mapping from Landsat using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 35, 199–208. https://doi.org/10.1016/j.jag.2014.09.005
  • Rana, H., & Babu, G. L. S. (2022). Regional back analysis of landslide events using TRIGRS model and rainfall threshold: an approach to estimate landslide hazard for Kodagu, India. Bulletin of Engineering Geology and the Environment, 81(4), 1–16. https://doi.org/10.1007/s10064-022-02660-9
  • Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring Vegetation Systems in the Great Plains with Erts, NASA Special Publication. Proceedings of the Third Earth Resources Technology Satellite- 1 Symposium, 309–317.
  • Sahana, M., & Patel, P. P. (2019). A comparison of frequency ratio and fuzzy logic models for flood susceptibility assessment of the lower Kosi River Basin in India. Environmental Earth Sciences, 78(10), 1–27. https://doi.org/10.1007/s12665-019-8285-1
  • Şahin, E. K. (2018). Heyelan Duyarlılık Haritası İçin Adımsal Regresyona Dayalı Faktör Seçme Yönteminin Etkinliğinin Araştırılması. Harita Dergisi, 84(159), 1–15.
  • Sarı, F., & Koyuncu, F. (2021). Multi criteria decision analysis to determine the suitability of agricultural crops for land consolidation areas. International Journal of Engineering and Geosciences, 6(2), 64–73
  • Sarkar, D., Saha, S., & Mondal, P. (2021). GIS-based frequency ratio and Shannon’s entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. International Journal of Environmental Science and Technology, 1–22. https://doi.org/10.1007/s13762-021-03627-1
  • Semlali, I., Ouadif, L., & Bahi, L. (2019). Landslide susceptibility mapping using the analytical hierarchy process and GIS. Current Science, 116(5), 773–779. https://doi.org/10.18520/cs/v116/i5/773-779
  • Senouci, R., Taibi, N. E., Teodoro, A. C., Duarte, L., Mansour, H., & Meddah, R. Y. (2021). Gis-based expert knowledge for landslide susceptibility mapping (LSM): Case of Mostaganem coast district, west of Algeria. Sustainability (Switzerland), 13(2), 1–21. https://doi.org/10.3390/su13020630
  • Shafapour Tehrany, M., Kumar, L., Neamah Jebur, M., & Shabani, F. (2019). Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics, Natural Hazards and Risk, 10(1), 79–101. https://doi.org/10.1080/19475705.2018.1506509
  • Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., & Rahmati, O. (2018). Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto International, 33(9), 927–941. https://doi.org/10.1080/10106049.2017.1316780
  • Suppawimut, W. (2021). GIS-Based Flood Susceptibility Mapping Using Statistical Index and Weighting Factor Models. Environment and Natural Resources Journal, 19(6), 1–13. https://doi.org/10.32526/ennrj/19/2021003
  • Tacconi Stefanelli, C., Casagli, N. & Catani, F. (2020). Landslide damming hazard susceptibility maps: a new GIS-based procedure for risk management. Landslides, 17(7), 1635–1648. https://doi.org/10.1007/s10346-020-01395-6
  • Thanh, D. Q., Nguyen, D. H., Prakash, I., Jaafari, A., Nguyen, V. T., Van Phong, T., & Pham, B. T. (2020). GIS based frequency ratio method for landslide susceptibility mapping at da Lat City, Lam Dong Province, Vietnam. Vietnam Journal of Earth Sciences, 42(1), 55–66. https://doi.org/10.15625/0866-7187/42/1/14758
  • Thao, P., Ngo, T., Panahi, M., Khosravi, K. & Ghorbanzadeh, O. (2021). Geoscience Frontiers Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2), 505–519. doi: 10.1016/j.gsf.2020.06.013
  • Thapa, D., & Bhandari, B. P. (2019). GIS-Based Frequency Ratio Method for Identification of Potential Landslide Susceptible Area in the Siwalik Zone of Chatara-Barahakshetra Section, Nepal. Open Journal of Geology, 9(12), 873–896. https://doi.org/10.4236/ojg.2019.912096
  • Trinh, T., Luu, B. T., Le, T. H. T., Nguyen, D. H., Van Tran, T., Van Nguyen, T. H., … Nguyen, L. T. (2022). A comparative analysis of weight-based machine learning methods for landslide susceptibility mapping in Ha Giang area. Big Earth Data, 1–30. https://doi.org/10.1080/20964471.2022.2043520
  • Ullah, K., & Zhang, J. (2020). GIS-based flood hazard mapping using relative frequency ratio method: A case study of panjkora river basin, eastern Hindu Kush, Pakistan. PLoS ONE, 15(3), 1–18. https://doi.org/10.1371/journal.pone.0229153
  • Yalcin, A., & Bulut, F. (2007). Landslide susceptibility mapping using GIS and digital photogrammetric techniques: A case study from Ardesen (NE-Turkey). Natural Hazards, 41(1), 201–226. https://doi.org/10.1007/s11069-006-9030-0
  • Yi, Y., Zhang, Z., Zhang, W., Xu, Q., Deng, C., & Li, Q. (2019). GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province,
  • Yılmaz, O. S., Gülgen, F., Güngör, R., & Kadı, F. (2018). Coğrafi Bilgi Sistemleri ve Uzaktan Algılama Teknikleri ile Arazi Kullanım Değişiminin İncelenmesi, Köprübaşı İlçesi Örneği. Geomatik, 3(3), 233-241.
  • Zhou, B., Okin, G. S., & Zhang, J. (2020). Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring. Remote Sensing of Environment, 236, 111521. https://doi.org/10.1016/j.rse.2019.111521
Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Osman Salih Yılmaz 0000-0003-4632-9349

Yayımlanma Tarihi 10 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 8 Sayı: 1

Kaynak Göster

APA Yılmaz, O. S. (2023). Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği. Geomatik, 8(1), 42-54. https://doi.org/10.29128/geomatik.1108735
AMA Yılmaz OS. Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği. Geomatik. Nisan 2023;8(1):42-54. doi:10.29128/geomatik.1108735
Chicago Yılmaz, Osman Salih. “Frekans Oranı yöntemiyle coğrafi Bilgi Sistemi ortamında Heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği”. Geomatik 8, sy. 1 (Nisan 2023): 42-54. https://doi.org/10.29128/geomatik.1108735.
EndNote Yılmaz OS (01 Nisan 2023) Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği. Geomatik 8 1 42–54.
IEEE O. S. Yılmaz, “Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği”, Geomatik, c. 8, sy. 1, ss. 42–54, 2023, doi: 10.29128/geomatik.1108735.
ISNAD Yılmaz, Osman Salih. “Frekans Oranı yöntemiyle coğrafi Bilgi Sistemi ortamında Heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği”. Geomatik 8/1 (Nisan 2023), 42-54. https://doi.org/10.29128/geomatik.1108735.
JAMA Yılmaz OS. Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği. Geomatik. 2023;8:42–54.
MLA Yılmaz, Osman Salih. “Frekans Oranı yöntemiyle coğrafi Bilgi Sistemi ortamında Heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği”. Geomatik, c. 8, sy. 1, 2023, ss. 42-54, doi:10.29128/geomatik.1108735.
Vancouver Yılmaz OS. Frekans oranı yöntemiyle coğrafi bilgi sistemi ortamında heyelan duyarlılık haritasının üretilmesi: Manisa, Demirci, Tekeler Köyü örneği. Geomatik. 2023;8(1):42-54.