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Landslide Susceptibility Mapping in Bitlis Province using GIS-based AHP method

Year 2022, Volume: 3 Issue: 2, 160 - 171, 18.09.2022
https://doi.org/10.48123/rsgis.1119723

Abstract

This study presents the landslide susceptibility assessment of the region by considering the landslide-susceptible hazard factors such as slope, precipitation, soil, lithology, distance to the river, land use, elevation, aspect, and distance to active faults as well as historical landslide events and population throughout the province of Bitlis. For this purpose, a GIS-based Analytical Hierarchy Process (AHP) was used as an effective method in multiple decision-making methods. The results showed that approximately 25% of the study area has moderate to high landslide susceptibility. Accordingly, the landslide susceptibility of the study area is high, especially in the southwest and southeast parts of the study area which have mountainous and deep river valleys, and the partially mountainous regions in the north. Compared with previous landslide records and similar susceptibility maps in the literature, the results were found to be quite successful in determining landslide susceptibility of the study area. However, risk assessment wasn’t made within the scope of the study.

References

  • Acar, E. (2019). Production of landslide susceptibility maps by using AHP method and GIS analyses (Master's thesis). Hacettepe University, Graduate School of Natural and Applied Sciences, Department of Geological Engineering, Ankara.
  • AFAD. (2021, July 01). 2020 yılı doğa kaynaklı olay istatistikleri. Retrieved from https://www.afad.gov.tr/kurumlar/ afad.gov.tr/e_Kutuphane/Istatistikler/2020yilidogakaynakliolayistatistikleri.pdf
  • Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1), 21–44.
  • Althuwaynee, O.F., Pradhan, B., & Lee, S. (2016). A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. International Journal of Remote Sensing, 37(5), 1190–1209.
  • Aydın, M.C., & Işık, E. (2015). Evaluation of Ground Snow Loads in the Micro-climate Regions. Russian Meteorology and Hydrology, 40(11), 741–748.
  • Birincioğlu-Sevgi, E. (2021). Disaster risk analysis of Bitlis province using geographical information systems and analytical hierarchy method (Master’s thesis). Bitlis Eren University Graduate Education Institute, Department of Emergency and Disaster Management, Bitlis.
  • Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16, 427– 445.
  • Carrara, A., Cardinali, M., Guazzetti, F., & Reichenbach, P. (1995). GIS techniques in mapping landslide hazard. In A. Carrara, & F. Guzzetti (Eds.), Geographical Information Systems in Assessing Natural Hazards (pp. 135–175). The Netherlands: Kluwer Academic Publishers.
  • Chen, W., Li, W., Chai, H., Hou, E., Li, X., & Ding, X. (2016). GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environmental Earth Sciences, 75(1), 63. doi: 10.1007/s12665-015-4795-7.
  • Climate-Data. (2021, December 16). Climate data for cities around the world. Retrieved from https://tr.climate-data.org
  • Copernicus. (2021, December 16). Data of land use from Copernicus Land Monitoring Service. Retrieved from https://land.copernicus.eu.
  • Çellek, S. (2020). Morphological parameters causing landslides: a case study of elevation. Bulletin of the Mineral Research and Exploration, 162, 197-224.
  • Ekinci, R., Büyüksaraç, A., Ekinci, Y.L., & Işık, E. (2020a). Bitlis ilinin doğal afet çeşitliliğinin değerlendirilmesi. Doğal Afetler ve Çevre Dergisi, 6(1), 1-11.
  • Ekinci, Y.L., Büyüksaraç, A., Bektaş, O., & Ertekin, C. (2020b). Geophysical Investigation of Mount Nemrut Stratovolcano (Bitlis, Eastern Turkey) Through Aeromagnetic Anomaly Analyses. Pure and Applied Geophysics, 177, 3243–326.
  • El Jazouli, A., Barakat, A., & Khellouk, R. (2019). GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters, 6(1), 3. doi: 10.1186/s40677-019-0119-7.
  • Ermini, L., Catani, F., & Casagli, N. (2005). Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology, 66, 327–343.
  • Geofabrik. (2021, December 16). Maps and Data. Retrieved from https://www.geofabrik.de/data.
  • Göksu, A. E. (2017). Landslide Susceptibility Analysis Report. Disaster and Emergency Management Presidency (AFAD), Bitlis, Turkey.
  • Göncüoğlu, M. C., & Turhan, N. (1983). New Results on the Age of Bitlis Metamorphics. Bulletin of the Mineral, Research and Exploration, 95-96, 44-48.
  • Hasekioğulları, G. D., & Ercanoğlu, M. (2012). A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabük, NW Turkey). Natural Hazards, 63(2), 1157-1179.
  • HGM. (2021, December 16). Republic of Türkiye Ministry of National Defense General Directorate of Mapping. Turkish administrative borders data. Retrieved from https://www.harita.gov.tr.
  • Işık, E. (2010). Bitlis City Earthquake Performance Analysis (Doctoral dissertation). Sakarya University, Graduate School of Natural and Applied Science, Sakarya.
  • Işık, E., Aydın, M. C., Bakış, A., & Özlük, M. H. (2012). The faults near Bitlis and seismicity of the region. BEU Journal of Science, 1(2), 153-169.
  • Işık, E., Aydın, M. C., & Büyüksaraç, A. (2020). 24 January 2020 Sivrice (Elazığ) earthquake damages and determination of earthquake parameters in the region. Earthquakes and Structures, 19(2), 145-156.
  • Kumar, R., & Anbalagan, R. (2016). Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. Journal of the Geological Society of India, 87(3), 271-286.
  • Mansouri Daneshvar, M. R. (2014). Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran. Landslides, 11(6), 1079-1091.
  • Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., & Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225. doi: 10.1016/j.earscirev.2020.103225.
  • MGM. (2021, December 16). Precipitation Data, Climate-Data, Turkish State Meteorological Service. Retrieved from https://www.mgm.gov.tr.
  • Mokarram, M., & Zarei, A.R. (2018). Landslide susceptibility mapping using fuzzy-AHP. Geotechnical and Geological Engineering, 36(6), 3931-3943.
  • Myronidis, D., Papageorgiou, C., & Theophanous, S. (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Natural Hazards, 81(1), 245-263.
  • MTA. (2021, December 16). Data of geological structure from geoscience map viewer and drawing editor, General Directorate of Mineral Research and Exploration of Turkey. Retrieved from http://yerbilimleri.mta.gov.tr/ anasayfa.aspx.
  • Nguyen, T. T. N., & Liu, C. C. (2019). A new approach using AHP to generate landslide susceptibility maps in the Chen-Yu-Lan Watershed, Taiwan. Sensors, 19(3), 505. doi: 10.3390/s19030505.
  • Özşahin, E. (2015). Coğrafi Bilgi Sistemleri yardımıyla heyelan duyarlılık analizi: Ganos Dağı örneği (Tekirdağ). Harita Teknolojileri Elektronik Dergisi, 7(1), 47-63.
  • Pradhan, B., Mansor, S., Pirasteh, S., & Buchroithner, M. F. (2011). Landslide Hazard and Risk Analyses at a Landslide Prone Catchment Area Using Statistical Based Geospatial Model. International Journal of Remote Sensing, 32, 4075–4087.
  • 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.
  • Rahim, I., Ali, S. M., & Aslam, M. (2018). GIS Based landslide susceptibility mapping with application of analytical hierarchy process in District Ghizer, Gilgit Baltistan Pakistan. Journal of Geoscience and Environment Protection, 6(2), 34-49.
  • Rawat, J. S., & Joshi, R. C. (2012). Remote-Sensing and GIS-Based Landslide-Susceptibility Zonation Using the Landslide Index Method in Igo River Basin, Eastern Himalaya, India. International Journal of Remote Sensing, 33(12), 3751–3767.
  • Saaty, T. L. (1980). The analytic hierarchy processes. New York, NY: McGraw-Hill.
  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.
  • Saygılı, R. (2015). Coğrafya şekil. Retrieved from https://www.harita.gov.tr.
  • Subramanian, N., & Ramanathan, R. (2012). A review of applications of Analytic Hierarchy Process in operations management. International Journal of Production Economics, 138, 215-241.
  • TAD. (2021, December 16). Agricultural Land Evaluation Portal (TAD Portal), Republic of Turkey Ministry of Agriculture and Forestry General Directorate of Agricultural Reform. Retrieved from https://www.tarimorman.gov.tr.
  • Ulusoy, İ., Çubukçu, H.E., Mouralis, D., & Aydar, E. (2019). Nemrut Caldera and Eastern Anatolian Volcanoes: Fire in the Highlands. In C. Kuzucuoğlu, A. Çiner & N. Kazancı (Eds.), Landscapes and Landforms of Turkey (pp. 589–599). Springer, Cham.
  • USGS. (2021, December 16). EarthData and Digital Elevation Model (DEM) for Bitlis province, United States Geological Survey (USGS). Retrieved from https://www.usgs.gov.
  • Wang, Y., Liu J., & Elhag, T. (2008). An Integrated AHP-DEA methodology for bridge risk assessment. Computers and Industrial Engineering, 54(3), 513-525.

CBS Tabanlı AHP Yöntemi Kullanılarak Bitlis İlinin Heyelan Duyarlılık Haritalaması

Year 2022, Volume: 3 Issue: 2, 160 - 171, 18.09.2022
https://doi.org/10.48123/rsgis.1119723

Abstract

Bu çalışma, Bitlis ili genelinde eğim, yağış, toprak, litoloji, akarsuya olan mesafe, arazi kullanımı, yükseklik, bakı ve aktif faylara olan uzaklık gibi heyelana duyarlı tehlike faktörlerinin yansıra tarihsel heyelan olayları ve nüfus yoğunluğu dikkate alarak bölgenin heyelan duyarlılık değerlendirmesini sunmaktadır. Bu amaçla çoklu karar verme yöntemlerinde etkin bir yöntem olarak CBS tabanlı Analitik Hiyerarşi Süreci (AHS) kullanılmıştır. Sonuçlar, çalışma alanının yaklaşık %25'inin orta ila yüksek heyelan duyarlılığına sahip olduğunu göstermiştir. Buna göre, çalışma alanının özellikle dağlık ve derin akarsu vadilerinin bulunduğu güneybatı ve güneydoğu kesimlerinde ve kuzeyde kısmen dağlık bölgelerde heyelan duyarlılığı yüksektir. Literatürdeki önceki heyelan kayıtları ve benzer duyarlık haritaları ile karşılaştırıldığında, sonuçların çalışma alanının heyelan duyarlılığını belirlemede oldukça başarılı olduğu görülmüştür. Ancak çalışma kapsamında risk değerlendirmesi yapılmamıştır.

References

  • Acar, E. (2019). Production of landslide susceptibility maps by using AHP method and GIS analyses (Master's thesis). Hacettepe University, Graduate School of Natural and Applied Sciences, Department of Geological Engineering, Ankara.
  • AFAD. (2021, July 01). 2020 yılı doğa kaynaklı olay istatistikleri. Retrieved from https://www.afad.gov.tr/kurumlar/ afad.gov.tr/e_Kutuphane/Istatistikler/2020yilidogakaynakliolayistatistikleri.pdf
  • Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58(1), 21–44.
  • Althuwaynee, O.F., Pradhan, B., & Lee, S. (2016). A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. International Journal of Remote Sensing, 37(5), 1190–1209.
  • Aydın, M.C., & Işık, E. (2015). Evaluation of Ground Snow Loads in the Micro-climate Regions. Russian Meteorology and Hydrology, 40(11), 741–748.
  • Birincioğlu-Sevgi, E. (2021). Disaster risk analysis of Bitlis province using geographical information systems and analytical hierarchy method (Master’s thesis). Bitlis Eren University Graduate Education Institute, Department of Emergency and Disaster Management, Bitlis.
  • Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16, 427– 445.
  • Carrara, A., Cardinali, M., Guazzetti, F., & Reichenbach, P. (1995). GIS techniques in mapping landslide hazard. In A. Carrara, & F. Guzzetti (Eds.), Geographical Information Systems in Assessing Natural Hazards (pp. 135–175). The Netherlands: Kluwer Academic Publishers.
  • Chen, W., Li, W., Chai, H., Hou, E., Li, X., & Ding, X. (2016). GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environmental Earth Sciences, 75(1), 63. doi: 10.1007/s12665-015-4795-7.
  • Climate-Data. (2021, December 16). Climate data for cities around the world. Retrieved from https://tr.climate-data.org
  • Copernicus. (2021, December 16). Data of land use from Copernicus Land Monitoring Service. Retrieved from https://land.copernicus.eu.
  • Çellek, S. (2020). Morphological parameters causing landslides: a case study of elevation. Bulletin of the Mineral Research and Exploration, 162, 197-224.
  • Ekinci, R., Büyüksaraç, A., Ekinci, Y.L., & Işık, E. (2020a). Bitlis ilinin doğal afet çeşitliliğinin değerlendirilmesi. Doğal Afetler ve Çevre Dergisi, 6(1), 1-11.
  • Ekinci, Y.L., Büyüksaraç, A., Bektaş, O., & Ertekin, C. (2020b). Geophysical Investigation of Mount Nemrut Stratovolcano (Bitlis, Eastern Turkey) Through Aeromagnetic Anomaly Analyses. Pure and Applied Geophysics, 177, 3243–326.
  • El Jazouli, A., Barakat, A., & Khellouk, R. (2019). GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters, 6(1), 3. doi: 10.1186/s40677-019-0119-7.
  • Ermini, L., Catani, F., & Casagli, N. (2005). Artificial Neural Networks applied to landslide susceptibility assessment. Geomorphology, 66, 327–343.
  • Geofabrik. (2021, December 16). Maps and Data. Retrieved from https://www.geofabrik.de/data.
  • Göksu, A. E. (2017). Landslide Susceptibility Analysis Report. Disaster and Emergency Management Presidency (AFAD), Bitlis, Turkey.
  • Göncüoğlu, M. C., & Turhan, N. (1983). New Results on the Age of Bitlis Metamorphics. Bulletin of the Mineral, Research and Exploration, 95-96, 44-48.
  • Hasekioğulları, G. D., & Ercanoğlu, M. (2012). A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabük, NW Turkey). Natural Hazards, 63(2), 1157-1179.
  • HGM. (2021, December 16). Republic of Türkiye Ministry of National Defense General Directorate of Mapping. Turkish administrative borders data. Retrieved from https://www.harita.gov.tr.
  • Işık, E. (2010). Bitlis City Earthquake Performance Analysis (Doctoral dissertation). Sakarya University, Graduate School of Natural and Applied Science, Sakarya.
  • Işık, E., Aydın, M. C., Bakış, A., & Özlük, M. H. (2012). The faults near Bitlis and seismicity of the region. BEU Journal of Science, 1(2), 153-169.
  • Işık, E., Aydın, M. C., & Büyüksaraç, A. (2020). 24 January 2020 Sivrice (Elazığ) earthquake damages and determination of earthquake parameters in the region. Earthquakes and Structures, 19(2), 145-156.
  • Kumar, R., & Anbalagan, R. (2016). Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. Journal of the Geological Society of India, 87(3), 271-286.
  • Mansouri Daneshvar, M. R. (2014). Landslide susceptibility zonation using analytical hierarchy process and GIS for the Bojnurd region, northeast of Iran. Landslides, 11(6), 1079-1091.
  • Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., & Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225. doi: 10.1016/j.earscirev.2020.103225.
  • MGM. (2021, December 16). Precipitation Data, Climate-Data, Turkish State Meteorological Service. Retrieved from https://www.mgm.gov.tr.
  • Mokarram, M., & Zarei, A.R. (2018). Landslide susceptibility mapping using fuzzy-AHP. Geotechnical and Geological Engineering, 36(6), 3931-3943.
  • Myronidis, D., Papageorgiou, C., & Theophanous, S. (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Natural Hazards, 81(1), 245-263.
  • MTA. (2021, December 16). Data of geological structure from geoscience map viewer and drawing editor, General Directorate of Mineral Research and Exploration of Turkey. Retrieved from http://yerbilimleri.mta.gov.tr/ anasayfa.aspx.
  • Nguyen, T. T. N., & Liu, C. C. (2019). A new approach using AHP to generate landslide susceptibility maps in the Chen-Yu-Lan Watershed, Taiwan. Sensors, 19(3), 505. doi: 10.3390/s19030505.
  • Özşahin, E. (2015). Coğrafi Bilgi Sistemleri yardımıyla heyelan duyarlılık analizi: Ganos Dağı örneği (Tekirdağ). Harita Teknolojileri Elektronik Dergisi, 7(1), 47-63.
  • Pradhan, B., Mansor, S., Pirasteh, S., & Buchroithner, M. F. (2011). Landslide Hazard and Risk Analyses at a Landslide Prone Catchment Area Using Statistical Based Geospatial Model. International Journal of Remote Sensing, 32, 4075–4087.
  • 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.
  • Rahim, I., Ali, S. M., & Aslam, M. (2018). GIS Based landslide susceptibility mapping with application of analytical hierarchy process in District Ghizer, Gilgit Baltistan Pakistan. Journal of Geoscience and Environment Protection, 6(2), 34-49.
  • Rawat, J. S., & Joshi, R. C. (2012). Remote-Sensing and GIS-Based Landslide-Susceptibility Zonation Using the Landslide Index Method in Igo River Basin, Eastern Himalaya, India. International Journal of Remote Sensing, 33(12), 3751–3767.
  • Saaty, T. L. (1980). The analytic hierarchy processes. New York, NY: McGraw-Hill.
  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.
  • Saygılı, R. (2015). Coğrafya şekil. Retrieved from https://www.harita.gov.tr.
  • Subramanian, N., & Ramanathan, R. (2012). A review of applications of Analytic Hierarchy Process in operations management. International Journal of Production Economics, 138, 215-241.
  • TAD. (2021, December 16). Agricultural Land Evaluation Portal (TAD Portal), Republic of Turkey Ministry of Agriculture and Forestry General Directorate of Agricultural Reform. Retrieved from https://www.tarimorman.gov.tr.
  • Ulusoy, İ., Çubukçu, H.E., Mouralis, D., & Aydar, E. (2019). Nemrut Caldera and Eastern Anatolian Volcanoes: Fire in the Highlands. In C. Kuzucuoğlu, A. Çiner & N. Kazancı (Eds.), Landscapes and Landforms of Turkey (pp. 589–599). Springer, Cham.
  • USGS. (2021, December 16). EarthData and Digital Elevation Model (DEM) for Bitlis province, United States Geological Survey (USGS). Retrieved from https://www.usgs.gov.
  • Wang, Y., Liu J., & Elhag, T. (2008). An Integrated AHP-DEA methodology for bridge risk assessment. Computers and Industrial Engineering, 54(3), 513-525.
There are 45 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing, Geological Sciences and Engineering (Other)
Journal Section Research Articles
Authors

Mehmet Cihan Aydın 0000-0002-5477-1033

Elif Sevgi Birincioğlu 0000-0002-4317-9392

Aydın Büyüksaraç 0000-0002-4279-4158

Publication Date September 18, 2022
Submission Date May 22, 2022
Acceptance Date September 6, 2022
Published in Issue Year 2022 Volume: 3 Issue: 2

Cite

APA Aydın, M. C., Sevgi Birincioğlu, E., & Büyüksaraç, A. (2022). Landslide Susceptibility Mapping in Bitlis Province using GIS-based AHP method. Türk Uzaktan Algılama Ve CBS Dergisi, 3(2), 160-171. https://doi.org/10.48123/rsgis.1119723