Araştırma Makalesi
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Taşkın duyarlılık analizinde kullanılan parametreler üzerine bir değerlendirme

Yıl 2023, Sayı: 84, 67 - 83, 31.12.2023
https://doi.org/10.17211/tcd.1345962

Öz

Taşkınlar her geçen gün artan büyüklük ve sıklıklarına bağlı olarak dünyada ve ülkemizde önemi giderek artan afetlerden birisidir. Bu çalışmadaki temel amaç, taşkın duyarlılık ile ilgili uluslararası ve ulusal literatürün değerlendirilmesi ve duyarlılık çalışmalarına yeni bir yaklaşım olarak sel ve taşkınların meydana geldiği yerleşmelerin su toplama havzaları temelli taşkın duyarlılık parametrelerinin belirlenmesini gerçekleştirmektir. Bu kapsamda çalışmada tarihsel taşkın envanterine bağlı olarak Bursa ili sınırları içerisinde vadi tabanı ve akarsu kenarında sel ve taşkınların yaşandığı yerleşmelerin havzalarına bağlı olarak taşkın duyarlılık analizi parametreleri belirlenmiştir. Çalışmada kullanılan temel altlık veriler, Bursa iline ait 5m çözünürlüklü Sayısal Yükseklik Modeli (SYM), 1956-2022 yılları arasını kapsayan envanter verileri, litoloji, hidrolojik toprak grupları (HTG) ve yağış (WorldClim) verileridir. Bursa il sınırları içerisinde meydana gelen tarihsel sel ve taşkın envanterine bağlı olarak 28 yerleşme ve bu yerleşmelerin su toplama havzaları belirlenmiş ve bu havzalara sel ve taşkının oluşmasında hazırlayıcı 12 parametre uygulanmıştır. Taşkın hazırlayıcı parametreler sınıflandırma aşamasında 0-1 arasında normalize edilerek ortaya çıkan sonuca göre taşkın duyarlılık için parametre katsayıları oluşturulmuştur. Sonuç olarak envantere göre maksimum etkiye sahip parametreler; çatallanma oranı (R_b), drenaj yoğunluğu (D_d), akım toplanma zamanı (T_c), eğim, topografik nemlilik indeksi, akarsu güç indeksi, hidrolojik toprak grupları, olarak belirlenmiştir. Bu çalışma ile taşkın duyarlılık analizinde önceki çalışmalardan farklı olarak envantere bağlı ve yerleşim temelli havzalardan taşkın duyarlılık parametreleri belirlenmiştir.

Destekleyen Kurum

Bursa Uludağ Üniversitesi Bilimsel Araştırma Projeleri Birimi

Proje Numarası

SDK-2022-1081

Teşekkür

Çalışma sırasında verdiği desteklerden dolayı Dr. Öğr. Üyesi Abdullah AKBAŞ’a teşekkür ederiz.

Kaynakça

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An evaluation on the parameters used in flood susceptibility analysis

Yıl 2023, Sayı: 84, 67 - 83, 31.12.2023
https://doi.org/10.17211/tcd.1345962

Öz

Floods are increasingly important disasters worldwide and Türkiye due to their increasing magnitude and frequency. The main purpose of this study is to evaluate the international and national literature on flood susceptibility and to determine the flood susceptibility parameters based on the watersheds of the settlements where floods occur, as a new approach to susceptibility studies. Accordingly, in study, depending the historical flood inventory, flood susceptibility analysis parameters were determined according to the basins of the settlements where floods are experienced on the valley floor and riverside within the borders of Bursa province. The primary data used in the study are inventory data covering the period between 1956-2022, 5m resolution Digital Elevation Model (DEM) of Bursa province, hydrological soil groups (HSG) and precipitation data (WorldClim). According to the historical flood inventory occurring within the borders of Bursa province, 28 settlements and their watersheds were identified and 12 flood causative parameters were applied to these basins. These flood causative parameters were normalised to between 0-1 in the classification stage and parameter coefficients for flood susceptibility were created according to the result. In this direction, bifurcation ratio, drainage density, time of concentrations, slope, topographic wetness index, stream power index and hydrologic soil groups were determined as the parameters with maximum effect according to the inventory. As a result, in this study, unlike previous studies in flood susceptibility analysis, flood susceptibility parameters were determined from the basins of the settlements where floods occur in the inventory.

Proje Numarası

SDK-2022-1081

Kaynakça

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  • Ritter, D. F., Kochel, R. C., & Miller, J. R. (1995). Process Geomorphology. Wm. C. C. Brown, Dubuque, IA. Santangelo, N., Santo, A., Di Crescenzo, G., Foscari, G., Liuzza, V., Sciarrotta, S., & Scorpio, V. (2011). Flood susceptibility assessment in a highly urbanized alluvial fan: the case study of Sala Consilina (southern Italy). Natural Hazards and Earth System Sciences, 11(10), 2765-2780. https://doi.org/10.5194/nhess-11-2765-2011, 2011.
  • Saravanan, S., Abijith, D., Reddy, N. M., Parthasarathy, K. S. S., Janardhanam, N., Sathiyamurthi, S., & Sivakumar, V. (2023). Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India. Urban Climate, 49, 101503.
  • Schumm, S. A. (1956). Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geological society of America bulletin, 67(5), 597-646.
  • Seydi, S. T., Kanani-Sadat, Y., Hasanlou, M., Sahraei, R., Chanussot, J., & Amani, M. (2022). Comparison of machine learning algorithms for flood susceptibility mapping. Remote Sensing, 15(1), 192. https://doi.org/10.3390/rs15010192
  • Seymen İ. H. (2020, 24 Haziran). Bursa’da sel bilançosu: 33 bina ve 30 bin 500 dönüm tarım arazisi zarar gördü. Hürriyet Haber. https://www.hurriyet.com.tr/gundem/bursada-sel-bilancosu-33-bina-ve-30-bin-500-donum-tarim-arazisi-zarar-gordu-41549066
  • Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., & Shirzadi, A. (2018). Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Journal of environmental management, 217, 1-11. https://doi.org/10.1016/j.jenvman.2018.03.089
  • Shahiri Tabarestani, E., & Afzalimehr, H. (2022). A comparative assessment of multi-criteria decision analysis for flood susceptibility modelling. Geocarto International, 37(20), 5851-5874. https://doi.org/10.1080/10106049.2021.1923834
  • 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
  • Skilodimou, H., Livaditis, G., Bathrellos, G., & Verikiou-Papaspiridakou, E. (2003). Investigating the flooding events of the urban regions of Glyfada and Voula, Attica, Greece: a contribution to Urban Geomorphology. Geografiska Annaler: Series A, Physical Geography, 85(2), 197-204. https://doi.org/10.1111/1468-0459.00198
  • Souissi, D., Zouhri, L., Hammami, S., Msaddek, M. H., Zghibi, A., & Dlala, M. (2020). GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto International, 35(9), 991-1017. https://doi.org/10.1080/10106049.2019.1566405
  • Su, J., Lü, H., Zhu, Y., Cui, Y., & Wang X. (2019). Evaluating the hydrological utility of latest IMERG products over the Upper Huaihe River Basin China. Atmos. Res., 225, 17-29. https://doi.org/10.1016/j.atmosres.2019.03.025.
  • Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of GeoInformation, 9(12), 720.
  • Şengör, A.M.C. and Yılmaz, Y. (1981). Tethyan evolution of Turkey: A plate tectonic approach. Tectonophysics, 75, 181-241.
  • Talukdar, S., Ghose, B., Shahfahad, Salam, R., Mahato, S., Pham, Q. B., & Avand, M. (2020). Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stochastic Environmental Research and Risk Assessment, 34, 2277-2300.
  • Tang, Z., Yi, S., Wang, C., & Xiao, Y. (2018). Incorporating probabilistic approach into local multi-criteria decision analysis for flood susceptibility assessment. Stochastic environmental research and risk assessment, 32, 701-714. https://doi.org/10.1007/s00477-017-1431-y
  • Tehrany, M. S., Lee, M. J., Pradhan, B., Jebur, M. N., & Lee, S. (2014). Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental earth sciences, 72, 4001-4015.
  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule-based decision tree (DT) and a novel ensemble bivariate and multivariate statistical model in GIS. Journal of hydrology, 504, 69-79. https://doi.org/10.1016/j.jhydrol.2013.09.034
  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2015). Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic environmental research and risk assessment, 29, 1149-1165. https://doi.org/10.1007/s00477-015-1021-9
  • Tella, A., & Balogun, A. L. (2020). Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria. Natural Hazards, 104(3), 2277-2306.
  • Utlu M., & Özdemir, H. (2018). Havza morfometrik özelliklerinin taşkın üretmedeki rolü Biga Çayı havzası örneği. Coğrafya Dergisi, (36), 49-62.
  • Vojtek, M., & Vojteková J. (2019). Flood susceptibility mapping on a national scale in slovakia using the Analytical Hierarchy Process. Water (Switzerland),11(2), 364-391. https://doi.org/10.3390/w11020364
  • Wang, Y., Hong, H., Chen, W., Li, S., Pamučar, D., Gigović, L., & Duan, H. (2018). A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sensing, 11(1), 62. https://doi.org/10.3390/rs11010062
  • Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., & Costache, R. (2019). Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography-based optimization and imperialistic competitive algorithm. Journal of environmental management, 247, 712-729. https://doi.org/10.1016/j.jenvman.2019.06.102
  • Withanage, N. S., Dayawansa, N. D. K., & De Silva, R. P. (2014). Morphometric analysis of the Gal Oya River Basin using spatial data derived from GIS. Trop Agric Res, 26(1), 175-188.
  • Yalçın, M. (2012). Afet yönetimi-hazırlık bileşeni için konumsal veri altyapısı tasarlanması, sel ve taşkına duyarlı alanlar: İstanbul Avrupa yakası örneği. Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Yaltırak, C., (2002). Tectonic evolution of the Marmara Sea and its surroundings. Marine Geology 190(1-2), 493–529.
  • Youssef, A. M., Pradhan, B., & Hassan, A. M. (2011). Flash flood risk estimation along the St. Katherine Road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environmental Earth Sciences, 62(3), 611-623.
  • Youssef, A. M., Pradhan, B., & Sefry, S. A. (2016). Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences, 75(1), 12.
  • Zaharia, L., Costache, R., Prăvălie, R., & Ioana-Toroimac, G. (2017). Mapping flood and flooding potential indices: a methodological approach to identifying areas susceptible to flood and flooding risk. Case study: the Prahova catchment (Romania). Frontiers of Earth Science, 11, 229-247.
Toplam 109 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Drenaj, Hidrografi, Fiziki Coğrafya
Bölüm Araştırma Makalesi
Yazarlar

İmren Kuşcu 0000-0002-7810-3507

Hasan Özdemir 0000-0001-8885-9298

Proje Numarası SDK-2022-1081
Yayımlanma Tarihi 31 Aralık 2023
Kabul Tarihi 10 Eylül 2023
Yayımlandığı Sayı Yıl 2023 Sayı: 84

Kaynak Göster

APA Kuşcu, İ., & Özdemir, H. (2023). Taşkın duyarlılık analizinde kullanılan parametreler üzerine bir değerlendirme. Türk Coğrafya Dergisi(84), 67-83. https://doi.org/10.17211/tcd.1345962

Yayıncı: Türk Coğrafya Kurumu