Çoklu Afet Tehlike Analizi, Elazığ İli Örneği

Abstract

Bu çalışmada, Doğu Anadolu Fay Zonu'nun geçtiği Elazığ ilinde, afet tehlikelerinin çoklu değerlendirmesi yapılmıştır. Çoklu afet tehlike haritası oluşturmak için deprem, sel, heyelan, kaya düşmesi, çığ, çölleşme ve erozyona ilişkin tehlike haritaları kullanılmıştır. Analitik Hiyerarşi Süreci (AHP) ve Rastgele Orman (RO) algoritması da dahil olmak üzere çeşitli yöntemler, her bir tehlike türü için maruziyetin ciddiyetini ve olasılığını değerlendirmek amacıyla kullanılmıştır. Bağımsız değişkenler, ilgili literatüre ve çalışma alanına dayalı olarak seçilmiş ve sonuçta VS30, sıvılaşma potansiyeli, sayısal yükselti modelinden türetilmiş veriler ve iklim verileri de dahil olmak üzere 30 değişken elde edilmiştir. Deprem ve erozyon tehlikeleri için bağımlı değişken bulunmamasından dolayı sezgisel modeller kullanılmıştır. Çölleşme haritası, Çevre Şehircilik ve İklim Değişikliği Bakanlığından alınmıştır. Tüm tehlike haritaları, Coğrafi Bilgi Sistemleri yazılımındaki ağırlıklandırılmış katman aracıyla hiyerarşik bir yaklaşım kullanılarak birleştirilmiştir. Araştırma ile Elâzığ ilinde afetlere karşı hazırlıklı olmayı sağlama, kaynakları etkin bir şekilde kullanma ve afet sonrası toparlanma sürecini hızlandırma çalışmaları için öngörü sağlayabilecek mekânsal bir sentez ve veri tabanı oluşturulmuştur. Böylece yerel yönetimlere ve acil müdahale ekiplerine yardımcı olması amaçlanmıştır. Deprem açısından ildeki kentsel yerleşmelerin önemli bir bölümü, kırsal yerleşmelerin ise çoğunluğu yüksek tehlike altındadır. Sel ve taşkın tehlikesi başta depresyon sahaları olmak üzere, il genelinde çok sayıda bulunan barajların mansap kesimlerinde ve mevsimlik akarsu yataklarının birleştiği yerlerde yüksektir. Heyelan tehlikesi, DAF boyunca uzanan engebeli alanlar ile akarsular tarafından aşındırılan yüksek eğimli arazilerde fazladır. Kaya düşmeleri özellikle Hazarbaba-Akdağ ekseni boyunca yer alan dağlık arazilerde, erozyona ve fiziksel çözülmeye bağlı olarak artmaktadır. Erozyon ve çölleşme il genelindeki en önemli yavaş ilerleyen tehlikelerdendir. Erozyon yüksek eğimli ve çıplak arazilerde artarken, çölleşme özellikle ilin batısında düşük yükseltiye sahip Baskil ve çevresinde etkisini göstermektedir. Çoklu tehlike açısından; Elazığ, Baskil, Kovancılar, Karakoçan ve Behrimaz ovaları gibi yerleşme ve iktisadi faaliyetlerin yoğunlaştığı alanlar çok yüksek ve yüksek tehlikeli alanlar arasında yer almaktadır.

Keywords

• Çoklu Afet Tehlike Analizi
• Elazığ İli
• Deprem
• Sel ve Taşkın
• Heyelan
• Erozyon
• Kaya Düşmesi
• Çığ
• Çölleşme.

Multi-Disaster Hazard Analysis, The Case of Elazığ Province

Abstract

In this study, a comprehensive assessment of disaster hazards in Elazığ province, where the Eastern Anatolian Fault Zone passes through, was conducted. Hazard maps for earthquakes, floods, landslides, rockfalls, avalanches, desertification, and erosion were integrated to create a multi-hazard map. Various methods, such as the Analytic Hierarchy Process (AHP) and machine learning models, including the Random Forest algorithm, were employed to assess the severity and probability of exposure for each hazard type. Independent variables, including VS30, liquefaction potential, Digital Elevation Model (DEM)-derived data, and climatic data, were selected based on relevant literature and the study area. For earthquake and erosion hazards, intuitive models were used due to the absence of a single dependent variable. The desertification map was obtained from the Ministry of Environment, Urban Planning, and Climate Change. The Random Forest model was used for other disaster hazard maps. All hazard maps were combined using a hierarchical approach with the Weighted Overlay tool. The study generated a spatial synthesis and database intended to offer proactive insights into disaster preparedness, optimizing resource allocation, and expediting recovery efforts post-disaster within the Elazığ Province. Its primary objective is to provide assistance to local authorities and emergency response teams. In the province, a significant portion of urban settlements and the majority of rural areas face high earthquake hazards. Floods pose a considerable risk, particularly in low-lying areas downstream of numerous dams scattered across the province, as well as at the confluence points of seasonal riverbeds. The hazard of landslides is high in the rugged areas along the EAF and in steep terrains eroded by rivers. Moreover, rock falls occur more frequently in mountainous areas along the Hazarbaba-Akdağ axis due to erosion and physical dissolution. Erosion and desertification represent significant slow-moving hazards, with erosion intensifying on steep slopes and barren lands, while desertification notably affects Baskil and its surrounding low-lying areas in the western part of the province. Considering multiple hazards, areas with concentrated settlements and economic activities such as Elazığ, Baskil, Kovancılar, Karakoçan, and Behrimaz plains are categorized as very high and high-risk zones.

Keywords

• Multi-Disaster Hazard Analysis
• Elazığ Province
• Earthquake
• Flooding
• Landslide
• Erosion
• Rockfall
• Avalanche
• Desertification

References

1. Abdulkadhim, A. H. (2019). Estimating snow cover area in south of Turkey using the Normalized Difference Snow Index (NDSI) form MODIS Satellite Images. Journal of Physics: Conference Series, 1279(1), Article 12047. https://doi.org/10.1088/1742-6596/1279/1/012047.
2. Aksha, S. K., Resler, L. M., Juran, L., & Carstensen, L. W. (2020). A geospatial analysis of multi-hazard risk in Dharan, Nepal. Geomatics, Natural Hazards and Risk, 11(1), 88–111. https://doi.org/10.1080/19475705.2019.1710580
3. Albayrak, D. (2021). The impact of climate change on future extreme precipitation in Turkey [Master’s thesis, Istanbul Technical University]. CoHE Thesis Center. https://tez.yok.gov.tr/UlusalTezMerkezi
4. Avci, V., & Sunkar, M. (2018). The relationship of landslides with lithological units and fault lines occurring on the East Anatolian Fault Zone, between Palu (Elazığ) and Bingöl, Turkey. Bulletin of the Mineral Research and Exploration, 157, 23–38. https://doi.org/10.19111/bulletinofmre.428277
5. Bilgiç, T. (2002). 1: 500 000 scale Turkey Geological Map Series, Sivas sheet. Maden Tetkik ve Arama Genel Müdürlüğü, Ankara. Retrieved June 9, 2023, https://www.mta.gov.tr/v3.0/hizmetler/500bas
6. Boğaziçi Üniversitesi Kandilli Rasathanesi ve Deprem Araştırma Enstitüsü. (2023). Regional Earthquake-Tsunami Monitoring and Evaluation Center - Earthquake Catalog. Retrieved June 6, 2023, http://www.koeri.boun.edu.tr/sismo/zeqdb/
7. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
8. Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., et al. (2022). Dynamic World, near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 1–17. https://doi.org/10.1038/s41597-022-01307-4

9. Caiola, G., & Reiter, J. P. (2010). Random forests for generating partially synthetic, categorical data. Transactions on Data Privacy, 3(1), 27–42.
10. Corominas, J., Copons, R., Vilaplana, J. M., Altimir, J., & Amigó, J. (2003). Integrated landslide susceptibility analysis and hazard assessment in the Principality of Andorra. Natural Hazards, 30(3), 421–435.
11. Çelik, M. A., Kopar, İ., & Bayram, H. (2018). Doğu Anadolu Bölgesi’nin Mevsimlik Kuraklık Analizi. ATASOBED, 22(3), 1741–1761.
12. Das, S. (2019). Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sensing Applications: Society and Environment, 14, 60–74. https://doi.org/10.1016/j.rsase.2019.02.006
13. Disaster and Emergency Management Presidency. (2023). Earthquake Catalog. Disaster and Emergency Management Presidency (AFAD). Retrieved June 6, 2023, https://deprem.afad.gov.tr/event-catalog
14. Dönmez, K. (2023). Future changes in hourly extreme precipitation, return levels, and non-stationary impacts in Türkiye [Master’s thesis, Istanbul Technical University]. CoHE Thesis Center. https://tez.yok.gov.tr/UlusalTezMerkezi.
15. Duman, T., Emre, Ö., Özalp, S., Elmacı, H., & Olgun, Ş. (2012). 1:250000 scale Turkey Active Fault Map Series, NJ37-7. Maden Tetkik ve Arama Genel Müdürlüğü, Ankara. Retrieved June 9, 2023, https://www.mta.gov.tr/v3.0/hizmetler/diri-fay-haritalari
16. Earth Resources Observation and Science Center. (2017). Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global. Retrieved June 6, 2023, https://earthexplorer.usgs.gov
17. Efiong, J., Eni, D. I., Obiefuna, J. N., & Etu, S. J. (2021). Geospatial modelling of landslide susceptibility in Cross River State of Nigeria. Scientific African, 14, Article e01032. https://doi.org/10.1016/j.sciaf.2021.e01032
18. Ekmekcioğlu, Ö., Koc, K., & Özger, M. (2021). Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey. International Journal of Disaster Risk Reduction, 60, Article 102327. https://doi.org/10.1016/j.ijdrr.2021.102327
19. Elazığ Provincial Directorate of Disaster and Emergency. (2021). Elazığ İl Afet Risk Azaltma Planı. Retrieved February 2, 2023, https://elazig.afad.gov.tr/kurumlar/elazig.afad/E-Kutuphane/Il-Planlari/Elazig_irap__3103.pdf
20. Elmastaş, N., & Özcanlı, M. (2011, 3-5 Kasım). Bitlis İlinde Çığ Afet Alanlarının Tespiti ve Çığ Risk Analizi [Bildiri Sunumu]. VI. Ulusal Coğrafya Sempozyumu, Ankara, Türkiye.
21. Environmental Systems Research Institute. (2023). Sentinel-2 10m Land Use/Land Cover Time Series. ESRI. Retrieved June 6, 2023, https://www.arcgis.com/home/item.html ?id=d3da5dd386d140cf93fc9ecbf8da5e31
22. Gall, M., Nguyen, K. H., & Cutter, S. L. (2015). Integrated research on disaster risk: Is it really integrated? International Journal of Disaster Risk Reduction, 12, 255–267. https://doi.org/10.1016/j.ijdrr.2015.01.010
23. General Directorate of Mineral Research and Exploration. (2023). GeoScience Map Viewer. General Directorate of Mineral Research and Exploration (MTA). Retrieved June 6, 2023, http://yerbilimleri.mta.gov.tr/
24. GEOFABRIK. (2023). OpenStreetMap Data Extracts. Retrieved June 6, 2023, https://download.geofabrik.de/europe/turkey.html
25. Gill, J. C., & Malamud, B. D. (2014). Reviewing and visualizing the interactions of natural hazards. Reviews of Geophysics, 52(4), 680–722. https://doi.org/10.1002/2013RG000445
26. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis. Pearson new international seventh edition. Pearson.
27. Harff, J., Meschede, M., Petersen, S., & Thiede, J. (Eds.). (2016). Encyclopedia of marine geosciences. Springer Science+Business Media.
28. Heath, D. C., Wald, D. J., Worden, C. B., Thompson, E. M., & Smoczyk, G. M. (2020). A global hybrid V S30 map with a topographic slope-based default and regional map insets. Earthquake Spectra, 36(3), 1570–1584. https://doi.org/10.1177/8755293020911137
29. Hibert, C., Provost, F., Malet, J. P., Maggi, A., Stumpf, A., & Ferrazzini, V. (2017). Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a Random Forest algorithm. Journal of Volcanology and Geothermal Research, 340, 130–142. https://doi.org/10.1016/j.jvolgeores.2017.04.015
30. Hutter, B. M. (Ed.). (2017). Risk, resilience, inequality and environmental law. Edward Elgar Publishing.
31. Jena, R., Pradhan, B., Beydoun, G., Nizamuddin, Ardiansyah, Sofyan, H., & Affan, M. (2020). Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia. Geoscience Frontiers, 11(2), 613–634. https://doi.org/10.1016/j.gsf.2019.07.006
32. Jha, A. K., Miner, T. W., & Stanton-Geddes, Z. (2013). Building urban resilience: Principles, tools, and practice. World Bank.
33. Kappes, M. S., Keiler, M., von Elverfeldt, K., & Glade, T. (2012). Challenges of analyzing multi-hazard risk: A review. Natural Hazards, 64(2), 1925–1958. https://doi.org/10.1007/s11069-012-0294-2
34. 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
35. Knijff, J. M., Jones, R. J. A., & Montanarella, L. (2000). Soil erosion risk assessment in Europe. European Soil Bureau, European Commission.
36. Koks, E. E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S. A., et al. (2019). A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1), Article 2677. https://doi.org/10.1038/s41467-019-10442-3
37. Korkmaz, M. (2022). Taşkın risk analizinde HEC-RAS modellemesinin kullanımı. NWSA Engineering Sciences, 17(4), 54–66. https://doi.org/10.12739/NWSA.2022.17.4.1A0482
38. Korup, O., Clague, J. J., Hermanns, R. L., Hewitt, K., Strom, A. L., & Weidinger, J. T. (2007). Giant landslides, topography, and erosion. Earth and Planetary Science Letters, 261(3-4), 578–589. https://doi.org/10.1016/j.epsl.2007.07.025
39. Li, Y., Osei, F. B., Hu, T., & Stein, A. (2023). Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustainable Cities and Society, 88, Article 104307. https://doi.org/10.1016/j.scs.2022.104307
40. Marzocchi, W., Garcia-Aristizabal, A., Gasparini, P., Mastellone, M. L., & Di Ruocco, A. (2012). Basic principles of multi-risk assessment: A case study in Italy. Natural Hazards, 62(2), 551–573. https://doi.org/10.1007/s11069-012-0092-x
41. Özdağoğlu, A., & Özdağoğlu, G. (2007). Comparison of AHP and fuzzy AHP for the multi-criteria decision making processes with linguistic evaluations. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 6(11), 65–85.
42. Özkazanç, S., Sıddıquı, D. S., & Güngör, M. (2020). Sensitivity analysis of earthquake using the Analytic Hierarchy Process (AHP) method: Sample of Adana. İDEALKENT, 11(30), 570–591.
43. Palutoğlu, M., & Tanyolu, E. (2006). Elazığ İl Merkezi Yerleşim Alanının Depremselliği. Fırat University, Journal of Science and Engineering, 18(4), 577-588.
44. Panagos, P., Borrelli, P., & Meusburger, K. (2015). A new European slope length and steepness factor (LS-Factor) for modeling soil erosion by water. Geosciences, 5(2), 117–126. https://doi.org/10.3390/geosciences5020117
45. Panagos, P., Hengl, T., Wheeler, I., Marcinkowski, P., Rukeza, M. B., Yu, B., et al. (2023). Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution. Data in Brief, 50, Article 109482. https://doi.org/10.1016/j.dib.2023.109482
46. Pektezel, H. (2015). Coğrafi bilgi sistemleri (CBS) ve analitik hiyerarşi sistemine (AHS) göre Gelibolu Yarımadası’nın deprem duyarlıklık analizi. The Journal of Academic Social Science Studies, 36, 179–201.
47. Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7, 217–240. https://doi.org/10.5194/soil-7-217-2021
48. Pourghasemi, H. R., Gayen, A., Panahi, M., Rezaie, F., & Blaschke, T. (2019). Multi-hazard probability assessment and mapping in Iran. The Science of the Total Environment, 692, 556–571. https://doi.org/10.1016/j.scitotenv.2019.07.203
49. Rehman, A., Song, J., Haq, F., Mahmood, S., Ahamad, M. I., Basharat, M., et al. (2022). Multi-hazard susceptibility assessment using the Analytical Hierarchy Process and Frequency Ratio techniques in the Northwest Himalayas, Pakistan. Remote Sensing, 14(3), Article 554. https://doi.org/10.3390/rs14030554
50. Renard, K. G. (1997). Predicting soil erosion by water: A guide to conservation planning with the revised universal soil loss equation (RUSLE). U.S. Dept. of Agriculture, Agricultural Research Service.
51. Rodriguez, F., Maire, E., Courjault-Radé, P., & Darrozes, J. (2002). The Black Top Hat function applied to a DEM: A tool to estimate recent incision in a mountainous watershed (Estibère Watershed, Central Pyrenees). Geophysical Research Letters, 29(6), Article 1085. https://doi.org/10.1029/2001GL014412
52. Rogelis, M. C. (2015). Flood Risk in Road Networks. Retrieved August 12, 2023, http://hdl.handle.net/10986/2298
53. Rusydi, H., Effendi, R., & Rahmawati, R. (2017). Vulnerability zoning of earthquake disaster of Palu. International Journal of Science and Applied Science: Conference Series, 1(2), 137–143.
54. Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill International.
55. 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
56. Schicker, R., & Moon, V. (2012). Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology, 161-162, 40–57. https://doi.org/10.1016/j.geomorph.2012.03.036
57. Sertel, S. (2017). Elazığ’da meydana gelen afetler (1931-1980). Akademik Sosyal Araştırmalar Dergisi, 49, 132-162.
58. Sevgen, C. S., & Tanrıvermiş, A. Y. (2020). Mass appraisal with a machine learning algorithm: Random forest regression. Bilişim Teknolojileri Dergisi, 13(3), 301–311. https://doi.org/10.17671/gazibtd.555784
59. Shin, G. J. (1999). The Analysis of Soil Erosion Analysis in Watershed Using GIS [Ph.D. Dissertation, Gang-Won National University]. Retrieved August 12, 2023, https://www.scirp.org/reference/referencespapers?referenceid=674937
60. Sielenou, P. D., Viallon-Galinier, L., Hagenmuller, P., Naveau, P., Morin, S., Dumont, M., et al. (2021). Combining random forests and class-balancing to discriminate between three classes of avalanche activity in the French Alps. Cold Regions Science and Technology, 187, Article 103276. https://doi.org/10.1016/j.coldregions.2021.103276
61. Sivakumar, M. V. K., & Ndiang'ui, N. (2007). Climate and land degradation. Springer.
62. Sunkar, M. (2014). 8 Mart 2010 Kovancılar-Okçular (Elazığ) depremi; yapı malzemesi ve yapı tarzının can ve mal kayıpları üzerindeki etkisi. Turkish Geography Review, 56, 23–37. https://doi.org/10.17211/tcd.18070
63. Sunkar, M., & Bağcı, H. R. (Eds.). (2014). Uluova'nın kuzeydoğusunda (Elazığ) yaşanan sel ve taşkın olaylarının çevresel etkileri. In M. Ertürk, A. Uzun, & Ş. Danacıoğlu (Eds.), Türkiye Coğrafyacılar Derneği Uluslararası Kongresi. Türkiye Coğrafyacılar Derneği Uluslararası Kongresi, Muğla, Türkiye.
64. Sutley, E. J., van de Lindt, J. W., & Peek, L. (2017). Multihazard analysis: Integrated engineering and social science approach. Journal of Structural Engineering, 143(9), Article 04017107. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001846
65. Şengün, M, T., Şaman, B., & Karadeniz, E. (2019). Kalaba Road (Sivrice-Elazığ) landslide and susceptibility analysis. In B. Gonencgil, T. A. Ertek, I. Akova & E. Elbasi (Eds.), 1st Istanbul International Geography Congress Proceedings Book (pp. 642-652). Istanbul University Press.
66. Taalab, K., Cheng, T., & Zhang, Y. (2018). Mapping landslide susceptibility and types using Random Forest. Big Earth Data, 2(2), 159–178. https://doi.org/10.1080/20964471.2018.1472392
67. Tarhan, N. (2002). 1: 500 000 scale Turkey Geological Map Series, Erzurum sheet. Maden Tetkik ve Arama Genel Müdürlüğü, Ankara. Retrieved June 9, 2023, https://www.mta.gov.tr/v3.0/hizmetler/500bas
68. Tehrany, M. S., Jones, S., & Shabani, F. (2019). Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena, 175, 174–192. https://doi.org/10.1016/j.catena.2018.12.011
69. Tonbul, S., Karadogan, S., & Özcan, N. (2005, April, 27-29). Elazığ kenti ve yakın çevresi için CBS ortamında olası doğal risk değerlendirmesi ve afet bilgi sistemi örnek uygulaması [Bildiri Sunumum], Ege Coğrafi Bilgi Sistemleri Sempozyumu, İzmir, Türkiye.
70. Tonini, M., D’Andrea, M., Biondi, G., Degli Esposti, S., Trucchia, A., & Fiorucci, P. (2020). A machine learning-based approach for wildfire susceptibility mapping: The case study of the Liguria region in Italy. Geosciences, 10(3), Article 105. https://doi.org/10.3390/geosciences10030105
71. Toprak, A., & Canpolat, F. A. (2022). Frekans oran, analitik hiyerarşi ve lojistik regresyon modellerinin taşkın tehlike tahmininde karşılaştırmalı kullanımı, Fatsa ilçe merkezi ve yakın çevresi örneği. International Journal of Geography and Geography Education, 45, 349–379. https://doi.org/10.32003/igge.998492
72. Turkish Statistical Institute. (2023). Demographic Statistics in Turkey. Turkish Statistical Institute (TURKSTAT). Retrieved May 12, 2023 https://biruni.tuik.gov.tr/medas/?kn=95&locale=en
73. United Nations. (1999). United Nations Convention to Combat Desertification. In those countries experiencing serious drought and/or desertification, particularly in Africa. https://catalogue.unccd.int/936_UNCCD_Convention_ENG.pdf
74. United Nations Department of Public Information. (1994). Agenda 21: Programme of action for sustainable development; Rio Declaration on Environment and Development; Statement of forest principles. United Nations Environment Programme (UNEP). https://unesdoc.unesco.org/ark:/48223/pf0000116639
75. United Nations Office for Disaster Risk Reduction. (2022). Global Assessment Report on Disaster Risk Reduction 2022: Our world at risk: Transforming governance for a resilient future. United Nations Office for Disaster Risk Reduction (UNDRR). https://www.undrr.org/gar/gar2022-our-world-risk-gar
76. UNEP. (2004). Guidelines for the application of environmental risk assessment in the European Union. Earthprint.
77. Varol, N. (2022). Avalanche susceptibility mapping with the use of frequency ratio, fuzzy and classical analytical hierarchy process for Uzungol area, Turkey. Cold Regions Science and Technology, 194, Article 103439. https://doi.org/10.1016/j.coldregions.2021.103439
78. Weiss, A. (2001). Topographic position and landforms analysis [Conference presentation]. ESRI User Conference, San Diego, United States. Retrieved May 12, 2023, http://jennessent.com/downloads/TPI-poster-TNC_18x22.pdf
79. Wischmeier, W. H., & Smith, D. D. (1958). Rainfall energy and its relationship to soil loss. Eos, Transactions American Geophysical Union, 39(2), 285–291. https://doi.org/10.1029/TR039i002p00285
80. World Settlement Footprint. (2023). World Settlement Footprint (WSF) Evolution - Landsat-5/-7 - Global. Retrieved December 12, 2023, https://download.geoservice.dlr.de/WSF_EVO/#details
81. Yanar, T., Kocaman, S., & Gokceoglu, C. (2020). Use of Mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). International Journal of Geo-Information, 9(2), Article 114. https://doi.org/10.3390/ijgi9020114
82. Yanis, M., & Furumoto, Y. (2019). Lithological identification of devastated area by Pidie Jaya earthquake through Poisson’s ratio analysis. International Journal of GEOMATE, 17(63), 210-216. https://doi.org/10.21660/2019.63.77489
83. Yariyan, P., Zabihi, H., Wolf, I. D., Karami, M., & Amiriyan, S. (2020). Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran. International Journal of Disaster Risk Reduction, 50, Article 101705. https://doi.org/10.1016/j.ijdrr.2020.101705
84. Yeon, Y. K., Han, J. G., & Ryu, K. H. (2010). Landslide susceptibility mapping in Injae, Korea, using a decision tree. Engineering Geology, 116(3-4), 274–283. https://doi.org/10.1016/j.enggeo.2010.09.009
85. Zengin, B., & Aydin, F. (2023). The effect of material quality on buildings moderately and heavily damaged by the Kahramanmaraş earthquakes. Applied Sciences, 13(19), Article 10668. https://doi.org/10.3390/app131910668
86. Zhou, J., Huang, S., Wang, M., & Qiu, Y. (2022). Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: A multi-dataset investigation. Engineering with Computers, 38(S5), 4197–4215. https://doi.org/10.1007/s00366-021-01418-3
87. Zhu, Z., & Zhang, Y. (2022). Flood disaster risk assessment based on random forest algorithm. Neural Computing & Applications, 34(5), 3443–3455. https://doi.org/10.1007/s00521-021-05757-6