Shannon Entropi (SE) ve AHP Metoduyla Artvin (Arhavi) Kapisre Taşkınının İncelenmesi

Year 2024, , 611 - 631, 31.08.2024

Ufuk Yükseler , Ömerul Faruk Dursun

https://doi.org/10.53433/yyufbed.1395065

Abstract

Küresel iklim değişikliği etkilerinin giderek daha fazla hissedilmesi ile birlikte, taşkın alanlarının belirlenmesi ve zararlarının azaltılmasına yönelik yapılan çalışmaların önemi de artmaktadır. Bu çalışmada, Artvin ili Arhavi ilçesinin sınırları içerisinde 2021 yılında yaşanan, can ve mal kaybına neden olan taşkın incelenmiş ve nedenleri ortaya konulmaya çalışılmıştır. Taşkın çalışmalarında sıklıkla kullanılan 9 adet taşkına etki eden parametrelerin yanısıra Çok Kriterli Karar Verme yöntemlerinden AHP ve Shannon Entropi (SE) metoduyla çalışma sahasının risk haritası çıkarılmıştır.
Çalışma sonucunda, her iki metoda göre taşkına en etkili parametreler yükselti ve yağış olarak belirlenmiştir. Taşkının yaşandığı lokasyonların düşük yükselti değerleri, yoğun yağış ve alüvyal toprak tipi özelliği gösterdiği çalışmaların bulguları arasındadır. Ayrıca SE, AHP metotlarıyla havzanın risk haritası çıkarılmış ve 4 adet doğrulama metoduyla bu iki metodun doğruluk değerleri hesaplanmıştır. Shannon Entropi metodu AHP metoduna göre daha iyi sonuçlar verdiği tespit edilmiştir.

Keywords

Taşkın, Coğrafi Bilgi Sistemleri, Shannon Entropi Metodu, AHP, Artvin İli

Ethical Statement

Bu araştırma İnönü Üniversitesi Bilimsel Araştırma Projeleri Birimi Araştırma Fonu (proje no: FDK-2022-2796) tarafından desteklenmiştir.

Supporting Institution

İnönü Üniversitesi Bilimsel Araştırma Projeleri Birimi Araştırma Fonu

Project Number

proje no: FDK-2022-2796

Thanks

Bu araştırma İnönü Üniversitesi Bilimsel Araştırma Projeleri Birimi Araştırma Fonu (proje no: FDK-2022-2796) tarafından desteklenmiştir.

Investigation of Artvin (Arhavi) Kapisre Flood using Shannon Entropy (SE) and AHP Method

Abstract

As the effects of global climate change are increasingly felt, the importance of studies aimed at identifying flood areas and reducing their damage is increasing. In this study, the flood that occurred in 2021 within the borders of Arhavi district of Artvin province and caused loss of life and property was examined and its causes were tried to be revealed. The risk map of the study area was prepared using 9 parameters affecting floods, which are frequently used in flood studies, and the AHP and Shannon Entropy (SE) methods, which are among the Multi-Criteria Decision Making methods. As a result of the study, the most effective parameters for floods according to both methods were determined as elevation and precipitation. Among the findings of the studies, the locations where the flood occurred have low altitude values, heavy rainfall and alluvial soil type. In addition, the risk map of the basin was prepared using SE and AHP methods, and the accuracy values of these two methods were calculated with 4 verification methods. It has been determined that the Shannon Entropy method gives better results than the AHP method.

Keywords

Flood, Geographic İdentity Systems, Shannon Entropy Method, AHP, Artvin Province

Project Number

proje no: FDK-2022-2796

References

• AFAD. (2019). Afet yönetimi kapsamında 2019 yılına bakış ve doğa kaynaklı olay istatistikleri. Erişim tarihi: 25.07.2022. https://www.afad.gov.tr/kurumlar/afad.gov.tr/35429/xfiles/turkiye_de_afetler.pdf
• AFAD. (2021). Artvin il risk azaltma planı. Erişim tarihi: 08.05.2022. https://artvin.afad.gov.tr/kurumlar/artvin.afad/ARTVIN-IRAP/ARTVIN-IRAP-SON_22102021.pdf
• Ahmad, D., & Afzal, M. (2020). Flood hazards and factors influencing household flood perception and mitigation strategies in Pakistan. Environmental Science and Pollution Research, 27(13), 15375-15387. https://doi.org/10.1007/s11356-020-08057-z
• Akkartal, A., Türüdü, O., & Erbek, S. F. (2005). Çok zamanlı uydu görüntüleri ile bitki örtüsü değişim analizi.TMMOB Harita ve Kadastro Mühendisleri Odası, 10. Türkiye Harita Bilimsel ve Teknik Kurultayı, Ankara, Türkiye.
• Anonim. (2021). Arhavi’de yaşanan selde taşan Kabisre Deresi’nde taşkın önlemleri alınıyor. Sabah Gazetesi. Erişim tarihi: 25.10.2023. https://www.sabah.com.tr/artvin/2021/08/11/arhavide-yasanan-selde-tasan-kabisre-deresinde-taskin-onlemleri-aliniyor
• Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrological Sciences Journal, 24(1), 43-69. https://doi.org/10.1080/02626667909491834
• Bui, D. T., Pradhan, B., Nampak, H., Bui, Q. T., Tran, Q. A., & Nguyen, Q. P. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317-330. https://doi.org/10.1016/j.jhydrol.2016.06.027
• Çiçek, İ., & Ataol, M. (2009). Türkiye’nin su potansiyelinin belirlenmesinde yeni bir yaklaşım. Coğrafi Bilimler Dergisi, 7(1), 51-65. https://doi.org/10.1501/Cogbil_0000000094
• Çınaklı, M. (2008). Doğu Karadeniz bölümünde meydana gelen taşkınlar. (Yüksek Lisans Tezi), Ankara Üniversitesi, Sosyal Bilimler Enstitüsü, Ankara, Türkiye.
• Dastorani, M. T., Moghadamnia, A., Piri, J., & Rico-Ramirez, M. (2010). Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment, 166, 421-434. https://doi.org/10.1007/s10661-009-1012-8
• Debnath, J., Debbarma, J., Debnath, A., Meraj, G., Chand, K., Singh, S. K., ... & Saikia, A. (2024). Flood susceptibility assessment of the Agartala Urban Watershed, India, using machine learning algorithm. Environmental Monitoring and Assessment, 196(2), 110. https://doi.org/10.1007/s10661-023-12240-3
• DSİ. (1996). Doğu Karadeniz taşkınları raporu (1970-1995). DSİ Yayınları, Trabzon, Türkiye.
• DSİ. (2006a). Su dünyası dergisi, Sayı: 34. DSİ Vakfı Yayınları, Ankara.
• DSİ. (2006b). Trabzon taşkınları raporu (2004, 2005, 2006). DSİ Yayınları, Trabzon.
• El-Magd, S. A. A., Ahmed, H., Pham, Q. B., Linh, N. T. T., Anh, D. T., Elkhrachy, I., & Masoud, A. M. (2022). Possible factors driving groundwater quality and its vulnerability to land use, floods, and droughts using hydrochemical analysis and GIS approaches. Water, 14(24), 4073. https://doi.org/10.3390/w14244073
• Ghosh, A., & Kar, S. K. (2018). Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Natural Hazards, 94, 349-368. https://doi.org/10.1007/s11069-018-3392-y
• Gigović, L., Pamučar, D., Bajić, Z., & Drobnjak, S. (2017). Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas. Water, 9(6), 360. https://doi.org/10.3390/w9060360
• Hammami, S., Zouhri, L., Souissi, D., Souei, A., Zghibi, A., Marzougui, A., & Dlala, M. (2019). Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arabian Journal of Geosciences, 12, 1-16. https://doi.org/10.1007/s12517-019-4754-9
• Hanson, P. R., Mason, J. A., & Goble, R. J. (2006). Fluvial terrace formation along Wyoming's Laramie Range as a response to increased late Pleistocene flood magnitudes. Geomorphology, 76(1-2), 12-25.
• Hava Kuvvetleri Komutanlığı. (2021). Sayısal yükselti modeli (SYM). Harita Genel Müdürlüğü, Ankara, Türkiye.
• 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
• Koç, E., & Küçükönder, M. (2021). Erkenez havzası CBS matris yöntemi ile heyelan duyarlılık değerlendirmesi. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(1), 141-154. https://doi.org/10.21605/cukurovaumfd.933874
• Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151. https://doi.org/10.1109/18.61115
• Lowe, D., Ebi, K. L., & Forsberg, B. (2013). Factors increasing vulnerability to health effects before, during and after floods. International Journal of Environmental Research and Public Health, 10(12), 7015-7067. https://doi.org/10.3390/ijerph10127015
• Mileti, D. S. (1995, November). Factors related to flood warning response. US-Italy Research Workshop on the Hydrometeorology, Impacts, and Management of Extreme Floods, Perugia, Italy.
• Minglei, R., Liuqian, D., Gang, W., Guangyuan, K., Xiaodi, F., YaFeng, Z., & Liping, Z. (2021). Identification of the inter-basin water diversion project-effected local flood risk factor by using the fishbone-diagram method. IOP Conference Series: Earth and Environmental Science, 826(1), 012011. https://doi.org/10.1088/1755-1315/826/1/012011
• Ouma, Y. O., & Tateishi, R. (2014). Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water, 6(6), 1515-1545. https://doi.org/10.3390/w6061515
• Özcan, O. (2008). Sakarya nehri alt havzası’nın taşkın riski analizinin uzaktan algılama ve CBS ile belirlenmesi. (Doktora Tezi), İstanbul Teknik Üniversitesi, Bilişim Enstitüsü, İstanbul, Türkiye.
• Özdemir, H. (2007). Farklı senaryolara göre taşkın risk analizi: Havran Çayı örneği (Balıkesir). TMMOB Afet Sempozyumu, Ankara, Türkiye.
• Pham, B. T., Luu, C., Van Phong, T., Nguyen, H. D., Van Le, H., Tran, T. Q., ... & Prakash, I. (2021). Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam. Journal of Hydrology, 592, 125815. https://doi.org/10.1016/j.jhydrol.2020.125815
• 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, 104364. https://doi.org/10.1016/j.catena.2019.104364
• Rashidpour, K. (2013). Using improved AHP method in maintenance approach selection. (PhD), Mälardalen University, School of Innovation, Design and Engineering, Swedan.
• Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7, 725-742. https://doi.org/10.1007/s12517-012-0807-z
• Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073-1076.
• Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. https://doi.org/10.1504/IJSSCI.2008.017590
• 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, 1-27. https://doi.org/10.1007/s12665-019-8285-1
• Sarkar, D., Saha, S., & Mondal, P. (2022). 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, 19(9), 8911-8932. https://doi.org/10.1007/s13762-021-03627-1
• Senan, C. P., Ajin, R. S., Danumah, J. H., Costache, R., Arabameri, A., Rajaneesh, A., ... & Kuriakose, S. L. (2023). Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models. Stochastic Environmental Research and Risk Assessment, 37(2), 527-556. https://doi.org/10.1007/s00477-022-02267-2
• Shaikh, M. P., Yadav, S. M., & Manekar, V. L. (2024). Flood hazards mapping by linking CF, AHP, and fuzzy logic techniques in urban areas. Natural Hazards Review, 25(1), 04023048. https://doi.org/10.1061/NHREFO.NHENG-1716
• Stefanidis, S., & Stathis, D. (2013). Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Natural Hazards, 68, 569-585. https://doi.org/10.1007/s11069-013-0639-5
• Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of Geo-Information, 9(12), 720. https://doi.org/10.3390/ijgi9120720
• 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
• 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
• Tunay, M., & Ateşeoğlu, A. (2008). Çok zamanlı uydu görüntüleri ile amasra ve yakın çevresine ait bitki örtüsü değişim analizi. Bartın Orman Fakültesi Dergisi, 10(13), 71-80.
• 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
• Vestby, J., Schutte, S., Tollefsen, A. F., & Buhaug, H. (2024). Societal determinants of flood-induced displacement. Proceedings of the National Academy of Sciences, 121(3), e2206188120. https://doi.org/10.1073/pnas.2206188120
• Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S., & Bai, X. (2015). Flood hazard risk assessment model based on random forest. Journal of Hydrology, 527, 1130-1141. https://doi.org/10.1016/j.jhydrol.2015.06.008
• Werner, M. G. F., Hunter, N. M., & Bates, P. D. (2005). Identifiability of distributed floodplain roughness values in flood extent estimation. Journal of Hydrology, 314(1-4), 139-157. https://doi.org/10.1016/j.jhydrol.2005.03.012
• Yukseler, U., Toprak, A., Gul, E., & Dursun, O. F. (2023). Flood hazard mapping using M5 tree algorithms and logistic regression: a case study in East Black Sea Region. Earth Science Informatics, 16(3), 2033-2047. https://doi.org/10.1007/s12145-023-01013-8