Aksaray ili obruk duyarlılık haritasının Coğrafi Bilgi Sistemleri (CBS) ve Analitik Hiyerarşi Süreci (AHS) yöntemleri ile oluşturulması

Year 2023, Volume: 12 Issue: 2, 612 - 625, 15.04.2023

Süleyman Sefa Bilgilioğlu Hacer Bilgilioğlu

https://doi.org/10.28948/ngumuh.1222497

Cited By: 1

Abstract

Afetler, can ve mal kayıpları gibi büyük zararlara yol açan beklenmedik ve istenmedik durumlardır. Doğal afetlere örnek olarak deprem, sel, heyelan, çığ ve obruklar gösterilebilir. Önemli bir afet türü olan obruklar meydana geldiği alanda ciddi güvenlik sorunları meydana getirmektedir. Tüm dünyada olduğu gibi Türkiye’de de, yeraltı su kaynaklarının giderek azalması, iklim özelliklerini hesaba katmadan yapılan yoğun tarımsal faaliyetler ve bunlara ek olarak ilgili bölgelerin jeolojik yapısı gibi faktörler obruk oluşma riski bulunan alanlarda sorunlar ortaya çıkarmaktadır. Can ve mal kaybına yol açan, kontrol edilemeyen ve aniden gelişen obruk olayları tamamen engellenemese de, önlem alabilmek mümkündür. Bu çalışmada Aksaray ilinde hızla sayısı artan obrukların mekânsal olabilirliğini tahmin eden ve gösteren duyarlılık haritalarının oluşturulması amacı ile Coğrafi Bilgi Sistemleri (CBS) ve Analitik Hiyerarşi Süreci (AHS) yöntemleri kullanılmıştır. Literatür çalışmaları ve uzman görüşleri dikkate alınarak duyarlılık haritası oluşturmak için 12 kriter belirlenmiş, kriter önem dereceleri AHS ile hesaplanmış ve obruk duyarlılık haritası oluşturulmuştur.

Keywords

Afet yönetimi, Coğrafi Bilgi Sistemleri (CBS), Obruk duyarlılık, Analitik Hiyerarşi Süreci (AHS)

Creation of sinkhole susceptibility map using Geographic Information Systems (GIS) and Analytical Hierarchy Process (AHP) methods in Aksaray province

Abstract

Disasters are unexpected and undesirable situations that cause significant damage, such as loss of life and property. Examples of natural disasters are earthquakes, floods, landslides, avalanches, and sinkholes, etc. can be displayed. Sinkholes, an essential type of disaster, create serious security problems in the area where they occur. In Turkey, as in the whole world, factors such as the gradual decrease of groundwater resources, intensive agricultural activities without taking into account the climatic characteristics, and in addition to these, the geological structure of the relevant regions cause problems in areas where there is a risk of sinkhole formation. Although uncontrollable and suddenly developing sinkhole events that cause property loss and life cannot be wholly prevented, it is possible to take precautions. In this study, Geographic Information Systems (GIS) and Analytical Hierarchy Process (AHS) methods were used to create susceptibility maps that predict and show the spatial likelihood of the rapidly increasing number of sinkholes in Aksaray. Considering the literature studies and expert opinions, 12 criteria were determined to create a sensitivity map, criteria importance levels were calculated with AHP, and a sinkhole sensitivity map was created.

Keywords

Disaster management, Geographic Information Systems (GIS), Sinkhole susceptibility, Analytical Hierarchy Process (AHP)

References

• F. Gutiérrez, M. Parise, J. De Waele and H. Jourde, A review on natural and human-induced geohazards and impacts in karst. Earth-Science Reviews, 138, 61–88, 2014. https://doi.org/10.1016/j.earscırev.2014.08.002.
• K. Taheri, T. M. Missimer, H. Mohseni, M. D. Fidelibus, M. Fathollahy and M. Taheri, Enhancing spatial prediction of sinkhole susceptibility by mixed waters geochemistry evaluation: application of ROC and GIS. Environmental Earth Sciences, 80 (14), 2021. https://doi.org/10.1007/s12665-021-09763-8.
• E. Bruno, D. Calcaterra and M. Parise, Development and morphometry of sinkholes in coastal plains of Apulia, southern Italy. Preliminary sinkhole susceptibility assessment. Engineering Geology, 99, (3–4), 198-209, 2008. https://doi.org/10.1016/j.enggeo. 2007.11. 017.
• F. Gutiérrez, Sinkhole Hazards. Oxford Research Encyclopedia of Natural Hazard Science, 2016. https://doi.org/10.1093/acrefore/9780199389407.013.40.
• S. T. Kidanu, N. L. Anderson and J. D. Rogers, Using GIS-based spatial analysis to determine factors influencing the formation of sinkholes in Greene County, Missouri. Environmental and Engineering Geoscience, 24 (3), 251–261, 2018. https://doi.org/ 10.2113/eeg-2014.
• A. B. Tihansky, Sinkholes, west-central Florida. in: D. R. Galloway, Devin, Jones and S. E. Ingebritsen (Eds.), Land subsidence in the United States, U.S. Geological Survey Circular, pp. 121-140, Florida, 1999.
• Y. J. Kim, B. H. Nam, Y. H. Jung, X. Liu, S. Choi, D. Kim and S. Kim, Probabilistic spatial susceptibility modeling of carbonate karst sinkhole. Engineering Geology, 306, 106728, 2022. https://doi.org/ 10.1016/j.enggeo.2022. 106728.
• R. Bozyiğit ve T. Tapur, Konya ovası ve çevresinde yeraltı sularının obruk oluşumlarına etkisi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 21, 137–155, 2009.
• G. Günay, İ. Çörekçioğlu, S. O. Eroskay and G. Övül, Konya Karapınar Obruks (Sinkholes) of Turkey. in: B. Andreo, F. Carrasco, J. Durán and J. LaMoreaux, (Eds.), Advances in Research in Karst Media. Environmental Earth Sciences, Springer, Heidelberg, pp. 367-372, 2010. https://doi.org/10.1007/978-3-642-12486-0_57.
• C. Gezgin, The influence of groundwater levels on land subsidence in Karaman (Turkey) using the PS-InSAR technique. Advances in Space Research, 70 (11), 3568-3581, 2022. https://doi.org/ 10.1016/j.asr.2022.08.003.
• K. Törk, B. Erduran ve P. N. Yılmaz, Konya Havzası’nda karstik çöküntü alanlarının belirlenmesi ve tehlike değerlendirmesi. MTA Raporu, Konya, Türkiye, Ocak 2013.
• M. Yılmaz, Karapınar çevresinde yeraltı suyu seviye değişimlerinin yaratmış olduğu çevre sorunları. Ankara Üniversitesi Çevre Bilimleri Dergisi, 2 (2), 145–163, 2010. https://doi.org/ 10.1501/csaum_ 0000000033.
• A. Üstün, E. Tuşat, S. Yalvaç, İ. Özkan, Y. Eren, A. Özdemir, İ. Ö. Bildirici, T. Üstüntaş, O. S. Kırtıloğlu, M. Mesutoğlu, S. Doğanalp, F. Canaslan, R. A. Abbak, N. B. Avşar and F. F. Şimşek, Land subsidence in Konya Closed Basin and its spatio-temporal detection by GPS and DInSAR. Environmental Earth Sciences, 73, 6691-6703, 2015. https://doi.org/10.1007/s12665-014-3890-5.
• A. Ozdemir, Sinkhole susceptibility mapping using logistic regression in Karapınar (Konya, Turkey). Bulletin of Engineering Geology and the Environment, 75 (2), 681–707, 2016. https://doi.org/10.1007/s10064-015-0778-x.
• F. Caló, D. Notti, J. P. Galve, S. Abdikan, T. Görüm, A. Pepe and F. Balik Şanli, Dinsar-Based detection of land subsidence and correlation with groundwater depletion in Konya Plain, Turkey. Remote sensing, 9(1), 83. 2017.https://doi.org/ 10.3390/rs9010083.
• O. Orhan, M. Yakar and S. Ekercin, An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems. Arabian Journal of Geosciences, 13 (17), 1–17, 2020. https://doi.org/10.1007/s12517-020-05841-6
• F. Sarı, M. Kahveci, M. Somay-Altas and E. Tuşat, Evaluating sinkhole formation with multicriteria decision analysis: a case study in Karapınar-Konya, Turkey. Arabian Journal of Geosciences, 14 (4), 1–15, 2021. https://doi.org/10.1007/s12517-021-06560-2
• V. Demir, Trend analysis of lakes and sinkholes in the Konya Closed Basin, in Turkey. Natural Hazards, 112 (3), 2873–2912, 2022. https://doi.org/10.1007/s11069-022-05327-6
• J. P. Galve, F. Gutiérrez, J. Remondo, J. Bonachea, P. Lucha and A. Cendrero, Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain). Geomorphology, 111 (3–4), 160–172, 2009. https://doi.org/ 10.1016/j.geomorph.2009.04.017.
• B. Pradhan, S. Mansor, S. Pirasteh and M. F. Buchroithner, Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. International Journal of Remote Sensing, 32 (14), 4075-4087. https://doi.org/ 10.1080/01431161.2010.484433.
• K. Taheri, H. Shahabi, K. Chapi, A. Shirzadi, F. Gutiérrez and K. Khosravi, Sinkhole susceptibility mapping: A comparison between Bayes-based machine learning algorithms. Land Degradation and Development, 30 (7), 730-745. 2019. https://doi.org/ 10.1002/ldr.3255.
• S. I. Elmahdy, M. M. Mohamed, T. A. Ali, J. E. D. Abdalla and M. Abouleish, Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain, UAE. Geocarto International, 37 (1), 315-331, 2020. https://doi.org/ 10.1080/10106049.2020.1716398.
• J. Malczewski, GIS‐based multicriteria decision analysis: a survey of the literatüre. International Journal of Geographical Information Science, 20 (7), 703-726, 2006. https://doi.org/ 10.1080/13658810600661508.
• L. Ayalew and H. Yamagishi, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65 (1–2), 15–31, 2005. https://doi.org/ 10.1016/j.geomorph.2004.06.010.
• H. R. Pourghasemi, B. Pradhan and C. Gokceoglu, Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63 (2), 965–996, 2012. https://doi.org/10.1007/s11069-012-0217-2.
• T. Kavzoglu, E. K. Sahin and I. Colkesen, Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11 (3), 425–439, 2014. https://doi.org/10.1007/s10346-013-0391-7
• O. Orhan, S. S. Bilgilioglu, Z. Kaya, A. K. Ozcan and H. Bilgilioglu, Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto International, 37 (10), 2795-2820, 2020. https://doi.org/0.1080/10106049.2020.1837258.
• M. Tiryaki and O. Karaca, Flood susceptibility mapping using GIS and multicriteria decision analysis: Saricay-Çanakkale (Turkey). Arabian Journal of Geosciences, 11 (14), 2018. https://doi.org/10.1007/s 12517-018-3675-3
• R. Abedi, R. Costache, H. Shafizadeh-Moghadam and Q. B. Pham, Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International, 37(19), 5479-5496, 2021. https://doi.org/10.1080/10106049.2021.1920636.
• H. E. Aydin and M. C. Iban, Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations. Natural Hazards, 1–35, 2022. https://doi.org/ 10.1007/S11069-022-05793-Y.
• K. Hepdeniz and O. Cengiz, GIS-Based avalanche susceptibility mapping for Davraz Mountain, Isparta, Turkey. Karaelmas Science and Engineering Journal, 9 (1), 62–68, 2019. https://doi.org/10.7212/zkufbd.v9ı1. 1217.
• H. Akay, Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques. Catena, 206, 2021. https://doi.org/ 10.1016/j.catena.2021.105524.
• N. Varol, 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, 103439, 2022. https://doi.org/ 10.1016/j.coldregıons.2021.103439.
• F. Sari, Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, 480, 118644, 2021. https://doi.org/ 10.1016/j.foreco.2020. 118644.
• M. C. Iban and A. Sekertekin, Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics, 69, 101647, 2022. https://doi.org/ 10.1016/j.ecoınf.2022.101647.
• K. Taheri, K., Gutiérrez, F., Mohseni, H., Raeisi, E. and M. Taheri, Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude-frequency relationships: A case study in Hamadan province, Iran. Geomorphology, 234, 64–79, 2015. https://doi.org/ 10.1016/j.geomorph.2015.01.005.
• P. Subedi, K. Subedi, B. Thapa and P. Subedi, Sinkhole susceptibility mapping in Marion County, Florida: Evaluation and comparison between analytical hierarchy process and logistic regression based approaches. Scientific Reports, 9 (1), 1–18, 2019. https://doi.org/ 10.1038/s41598-019-43705-6.
• D. AlSanea and W. Abdullah, Sinkhole susceptibility hazard mapping using analytical hierarchy process and GIS tools for the State of Kuwait. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7(3), 04021029, 2021. https://doi.org/ 10.1061/ajrua6.0001136.
• A. Ozdemir, Sinkhole susceptibility mapping using a -frequency ratio method and GIS technology near Karapınar, Konya-Turkey. Procedia Earth and Planetary Science, 15, 502–506, 2015. https://doi.org/ 10.1016/j.proeps.2015.08.059.
• T. M. Tharp, Cover-collapse sinkhole formation and soil plasticity. Sinkholes and the engineering and environmental impacts of karst, 9, 110–123, 2003. https://doi.org/ 10.1061/40698(2003)9.
• K. He, C. Liu, and S. Wang, Karst collapse related to over-pumping and a criterion for its stability. Environmental Geology, 43 (6), 720–724, 2003. https://doi.org/ 10.1007/S00254-002-0669-x/metrıcs.
• T. M. Tharp, Mechanics of upward propagation of cover-collapse sinkholes. Engineering Geology, 52 (1–2), 23–33, 1999, https://doi.org/ 10.1016/S0013-7952(98)00051-9.
• H. J. Oh, M. Syifa, C. W. Lee, and S. Lee, Land subsidence susceptibility mapping using bayesian, functional, and meta-ensemble machine learning models. Applied Sciences, 9 (6), 1248, 2019. https://doi.org/ 10.3390/app9061248.
• S. Bianchini, P. Confuorto, E. Intrieri, P. Sbarra, D. Di Martire, D. Calcaterra and R. Fanti. Machine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy. Geocarto International, 1-29, 2022. https://doi.org/ 0.1080/10106049.2022.2113455.
• G. Bausilio, M. Annibali Corona, D. Di Martire, L. Guerriero, R. Tufano, D. Calcaterra, M. Di Napoli and M. Francioni, Comparison of two machine learning algorithms for anthropogenic sinkhole susceptibility assessment in the city of Naples (Italy). in: R. Lancellotta, C. Viggiani, A. Flora, F. de Silva, L. Mele (Eds.), Geotechnical Engineering for the Preservation of Monuments and Historic Sites III, CRC Press, London, 2022. https://doi.org/ 10.1201/97810033088 67-88.
• M. T. J. Terlien, C. J. Van Westen and T. W. J. van Asch, Deterministic modelling in GIS based landslide hazard assessment. Geographical Information Systems in Assessing Natural Hazards, 57–77, 1995. https://doi.org/10.1007/978-94-015-8404-3_4.
• M. Ercanoglu and C. Gokceoglu, Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey). Engineering Geology, 75 (3–4), 229–250, 2004. https://doi.org/ 10.1016/j.enggeo.2004.06.001.
• S. S. Bilgilioğlu, Coğrafi Bilgi Sistemleri ve Bulanık Analitik Hiyerarşi Süreci ile elektrikli araç şarj istasyonu yer seçimi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 22 (1), 165–174, 2022. https://doi.org/ 10.35414/akufemubıd.1013244.
• A. M. C. Şengör, Y. Yilmaz and O. Sungurlu, Tectonics of the Mediterranean Cimmerides: Nature and evolution of the western termination of Palaeo-Tethys. Geological Society, 17 (1), 77–112, 1984. https://doi.org/ 10.1144/gsl.sp.1984.017.01.04.
• Y. Mustafa, A. Yıldız, A. Kahya ve S. Gürcan, Kızılkaya (Sevinçli / Aksaray) ignimbiritinin jeolojisi ve yapıtaşı olarak kullanılabilirliğinin araştırılması. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 30 (1), 1–8, 2014.
• İ. Seymen, Kaman (Kirsehir) dolayinda Kirsehir Masifinin metamorfizmasi. Türkiye Jeoleji Kurultayı 35. Bilimsel ve Teknik Kurultayı, İç Anadolu’nun Jeolojisi Sempozyumu, Ankara, Türkiye, 12-16, 1981.
• İRAP, İl Risk Azaltma Planı. AFAD Planlama ve Risk Azaltma Dairesi, Aksaray, 2021.
• Ü. Ulu, H. Öcal, A. K. Bulduk, M. Karakaş, A. Arbas, L. Saçlı ve M. Karabıyıkoğlu, Cihanbeyli-Karapınar yöresi geç Senozoyik çökelme sistemi: Tektonik ve iklimsel önemi. Türkiye Jeoloji Kurultayı Bülteni, 9, 149-163, 1994
• M. Dönmez and A. E. Akçay, 1/100 000 ölçekli Türkiye Jeoloji Haritaları, No: 51, Aksaray L31 paftası, 2005.
• M. Dönmez and A. E. Akçay, 1/100 000 ölçekli Türkiye Jeoloji Haritaları, No: 50, Aksaray L30 paftası, 2005.
• K. Törk, N. P. Yılmaz, S. Sülükçü, S. Keleş, Ş. Köklü, L. S. Yeleser, Z. R. Aykaç Özerk, C. Acar, F. Savaş, K. Çakır ve K. Avcı, Konya Ovası Projesi (KOP) bölgesinde (Konya, Karaman, Aksaray, Niğde) karstik çöküntü alanlarının belirlenmesi ve tehlike değerlendirmesi projesi (Final Raporu). MTA Genel Müdürlüğü, Rapor No: 263, Ankara, 2019
• MTA, 1/100 000 ölçekli Türkiye Jeoloji Haritası serisi, 2005.
• C. Gezgi̇n, İ. Tiryakioğlu, S. Ekercin ve E. Gürbüz, Tuz Gölü Fay Zonu (TGFZ) güney kesimine ait tektonik hareketlerin GNSS gözlemleri ile izlenmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 20 (3), 456–464, 2020, https://doi.org/ 10.35414/akufemubıd.690886.
• B. B. Tekocak Yardımlı, Aksaray ili yer altı su kaynaklarının hidrojeokimyasal özelliklerinin coğrafi bilgi sistemi kullanılarak değerlendirilmesi. Aksaray Üniversitesi Fen Bilimleri Enstitüsü, 2017.
• Ş. Tulun, E. Gürbüzü and T. Arsu, Developing a GIS-based landfill site suitability map for the Aksaray province, Turkey. Environmental Earth Sciences, 80 (8), 1–15, 2021. https://doi.org/10.1007/s12665-021-09598-3/ fıgures/12.
• D. Zanaga et al., ESA WorldCover 10 m 2021 v200, 2022. https://doi.org/ 10.5281/ZENODO.7254221.
• I. Yilmaz, GIS based susceptibility mapping of karst depression in gypsum: A case study from Sivas basin (Turkey). Engineering Geology, 90, 1–2, 89–103, 2007. https://doi.org/ 10.1016/j.enggeo.2006.12.004.
• K. Rawal, Exploring the geomechanics of sinkholes: a preliminary numerical study. PhD Thesis, University of Toledo, USA, 2016.
• A. M. S. Pradhan and Y. T. Kim, Relative effect method of landslide susceptibility zonation in weathered granite soil: A case study in Deokjeok-ri Creek, South Korea. Natural Hazards, 72 (2), 1189–1217, 2014. https://doi.org/ 10.1007/s11069-014-1065-z.
• D. Whitman, T. Gubbels and L. Powell, Spatial interrelationships between lake elevations, water tables, and sinkhole occurrence in Central Florida: A GIS approach. Photogrammatric Engineering Remote Sensing, 65 (10), 1169–1178, 1999.
• D. H. Doctor and K. Z. Doctor, Spatial analysis of geologic and hydrologic features relating to sinkhole occurrence in Jefferson County, West Virginia. Carbonates and Evaporites, 27 (2), 143–152, 2012, https://doi.org/10.1007/s13146-012-0098-1
• K. Z. Doctor, D. H. Doctor, B. Kronenfeld, D. W. S. Wong and D. K. Brezinski, Predicting sinkhole susceptibility in Frederick Valley, Maryland using geographically weighted regression. İn: L. B. Yuhr, E. C. Alexander and B. F. Beck, Sinkholes and the Engineering and Environmental Impacts of Karst, ASCE, pp. 243–256, Virginia, 2008. https://doi.org/ 10.1061/41003(327)24.
• T. Saaty, The Analytic Hierarchy Process (AHP) for decision making, 1980.
• M. Uyan ve Ş. Yalpır, Çok Kriterli Karar Verme modeli ve CBS entegrasyonu ile tıbbi atık sterilizasyon tesislerinin yer seçimi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 16 (3), 642–654, 2016. https://doi.org/ 10.1186/S40064-015-1404-X.
• S. S. Bilgilioğlu, Land suitability assessment for Olive cultivation using GIS and multi-criteria decision-making in Mersin City, Turkey. Arabian Journal of Geosciences, 14 (22), 1–16, 2021. https://doi.org/ 10.1007/S12517-021-08768-8.
• S. S. Bilgilioğlu, Site selection for radioactive waste disposal facility by GIS based multi criteria decision making. Annals of Nuclear Energy, 165, 108795, 2022. https://doi.org/ 10.1016/j.anucene.2021.108795.
• S. S. Bilgilioğlu ve C. Gezgi̇n, Nevşehir ili uygun katı atık depolama sahalarının Coğrafi Bilgi Sistemleri (CBS) ve Bulanık Analitik Hiyerarşi Süreci (BAHS) yöntemlerinin entegrasyonu ile belirlenmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 22 (4), 836–849, 2022. https://doi.org/ 10.35414/akufemubıd.1105426.
• F. Bunyan Unel and S. Yalpir, Valuations of building plots using the AHP method, International Journal of Strategic Property Management, 23 (3), 197–212, 2019. https://doi.org/ 10.3846/IJSPM.2019.7952.