Taşkın duyarlılık analizinde kullanılan parametreler üzerine bir değerlendirme

Yıl 2023, Sayı: 84, 67 - 83, 31.12.2023

İmren Kuşcu , Hasan Özdemir

https://doi.org/10.17211/tcd.1345962

Cited By: 2

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.

Anahtar Kelimeler

Taşkın hazırlayıcı parametreler, Taşkın duyarlılık analizi, Taşkın envanteri, Bursa

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.

An evaluation on the parameters used in flood susceptibility analysis

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.

Anahtar Kelimeler

Flood causative factors, Flood susceptibility analysis, Flood inventory, Bursa

Proje Numarası

SDK-2022-1081

Kaynakça

• Adiat, K. A. N., Nawawi, M. N. M., & Abdullah, K. (2012). Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool–a case of predicting potential zones of sustainable groundwater resources. Journal of Hydrology, 440, 75-89.
• Ahmadlou M., Karimi M., Alizadeh S., Shirzadi A., Parvinnejhad D., Shahabi H., & Panahi M. (2018). Flood susceptibility assessment using integration of Adaptive Network-Based Fuzzy İnference System (Anfıs) and Biogeography-Based Optimization (BBO) and BAT algorithms (BA). Geocarto International, 34(11), 1252–1272.
• Akbaş, A., & Özdemir, H. (2018). Marmara Denizi havzasının hidroklimatolojik dinamiklerinin belirlenmesi. Türk Coğrafya Dergisi, (70), 123-131.
• Al-Aizari, A. R., Al-Masnay, Y. A., Aydda, A., Zhang, J., Ullah, K., Islam, A. R. M. T., & Liu, X. (2022). Assessment analysis of flood susceptibility in Tropical Desert area: a case study of Yemen. Remote Sensing, 14(16), 4050.
• Alam, A., Ahmed, B., & Sammonds, P. (2021). Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh. Quaternary International, 575, 295-307.
• Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojteková, J., Costache, R., & Ghorbani, M. A. (2020). GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia. Ecological Indicators, 117, 106620. https://doi.org/10.1016/j.ecolind.2020.106620
• Arabameri A., Rezaei K., Cerdà A., Conoscenti C., & Kalantari Z. A. (2019). Comparison of Statistical Methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Science of the Total Environment, 660, 443–458.https://doi.org/10.1016/j.scitotenv.2019.01.021
• Ardel, A. (1943). Marmara bölgesinin güneydoğu havzalarının morfolojik karakterleri. Türk Coğrafya Dergisi, (2), 160-173.
• Ardel, A. (1956). Marmara bölgesinde coğrafi müşahedeler. İstanbul Üniversitesi Coğrafya Enstitüsü Dergisi, 4(7), 1-16.
• Ardel, A. (1960). Marmara bölgesinin yapı ve reliefi. Türk Coğrafya Dergisi, (20), 1-22. Ateş, Ş., & Aktimur T. (2019). Arazi kullanım planlaması yerbilim verileri ve araştırma yöntemleri: Bursa örneği, TMMOB jeoloji Mühendisleri Odası, 27. Dönem, 1. Çevre Jeolojisi ve Çevresel Etki Değerlendirmesi (ÇED) Eğitim Semineri, 47-49.
• Aydın, M. (2014). Bursa ili jeomorfoturizm özellikleri. Doktora Tezi, İstanbul Üniversitesi, Sosyal Bilimler Enstitüsü, İstanbul.
• 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
• Bisht, S., Sharma, S., & Chaudhry, S. (2016). Flash flood risk susceptibility in Gagas River Watershed–Kumaun Lesser Himalaya. Int. J. Adv. Remote Sens. GIS, 5(5), 1709-1725. DOI: https://doi.org/10.23953/cloud.ijarsg.55
• Bui, D. T., Ngo, P. T. T., Pham, T. D., Jaafari, A., Minh, N. Q., Hoa, P. V., & Samui, P. (2019). A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena, 179, 184-196.https://doi.org/10.1016/j.catena.2019.04.009
• Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability, 8(9), 948.
• Cao, Q., Mehran, A., Ralph, F.M., & Lettenmaier, D.P. (2019). The role of hydrological initial conditions on Atmospheric River floods in the Russian River basin. J. Hydrometeorol., 20, 1667–1686. https://doi.org/10.1175/JHM-D-19-0030.1
• Carlston, C. W. (1963). Drainage density and streamflow. US Government Printing Office.
• Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., Bui, D. T., Pham, B. T., & Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental modelling & software, 95, 229-245. https://doi.org/10.1016/j.envsoft.2017.06.012
• Chen, W., Hong, H., Li, S., Shahabi, H., Wang, Y., Wang, X., & Ahmad, B. B. (2019). Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. Journal of Hydrology, 575, 864-873.
• Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi A. (2019). An ensemble predic-tion of flood susceptibility using multivariate discriminant analysis, classification and Regression Trees and Support Vector Machines. Sci Total Environ, 651(2), 2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064
• Costache, R. (2019). Flood susceptibility assessment by using bivariate statistics and machine learning models-a useful tool for flood risk management. Water Resources Management, 33(9), 3239-3256.
• Costache, R., Pham Q. B., Sharifi, E., Linh N. T. T., Abba, S. I., Vojtek M., Vojteková J., Nhi P. T. T., & Khoi D. N. (2020). Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and gıs techniques. Remote Sensing,12(1), 106.https://doi.org/10.3390/RS12010106
• Das, S. (2018). Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arabian Journal of Geosciences, 11(19), 576. https://doi.org/10.3390/su8090948 https://doi.org/10.1016/j.jhydrol.2019.05.089
• Das, S., & Gupta, A. (2021). Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geoscience Frontiers, 12(5), 101206. https://doi.org/10.1016/j.gsf.2021.101206
• Desalegn, H., & Mulu, A. (2021). Flood vulnerability assessment using GIS at Fetam watershed, upper Abbay basin, Ethiopia. Heliyon, 7(1).
• Dutta, M., Saha, S., Saikh, N. I., Sarkar, D., & Mondal, P. (2023). Application of bivariate approaches for flood susceptibility mapping: a district level study in Eastern India. HydroResearch, 6, 108-121.
• Elbaşı, E., & Özdemir, H. (2018). Marmara denizi akarsu havzalarının morfometrik analizi. Coğrafya Dergisi, (36), 63-84. https://doi.org/10.26650/JGEOG418790
• Erinç, S. (1996). Klimatoloji ve metodları (4. Baskı). İstanbul: Alfa Basım Yayım Dağıtım. Fang, Z., Wang, Y., Peng, L., & Hong, H. (2021). Predicting flood susceptibility using LSTM neural networks. Journal of Hydrology, 594, 125734.
• Fang, X., Thompson, D. B., Cleveland, T. G., & Pradhan, P. (2007). Variations of time of concentration estimates using NRCS velocity method. Journal of Irrigation and Drainage Engineering, 133(4), 314-322.
• 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.
• Haque, M. N., Siddika, S., Sresto, M. A., Saroar, M. M., & Shabab, K. R. (2021). Geo-spatial analysis for flash flood susceptibility mapping in the North-East Haor (Wetland) Region in Bangladesh. Earth Systems and Environment, 5(2), 365-384.
• Hategekimana, Y., Yu, L., Nie, Y., Zhu, J., Liu, F., & Guo, F. (2018). Integration of multi-parametric fuzzy analytic hierarchy process and GIS along the UNESCO World Heritage: a flood hazard index, Mombasa County, Kenya. Natural Hazards, 92, 1137-1153.
• Hong, H., Panahi, M., Shirzadi A., MA Tianwu, L., Junzhi, Z., A, Xing., Chen W., Kougias L., & Kazakis N. (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive Neuro-Fuzzy İnference System with genetic algorithm and differential evolution. Science of the Total Environment,621(15), 1124-1141. https://doi.org/10.1016/j.scitotenv.2017.10.114
• Horton, R. E. (1932). Drainage-basin characteristics. Transactions, American geophysical union, 13(1), 350-361. https://doi.org/10.1029/TR013i001p00350
• Horton, R. E. (1945). Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geological society of America bulletin, 56(3), 275-370.https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2
• Islam, A. R. M. T., Talukdar, S., Mahato, S., Kundu, S., Eibek, K. U., Pham, Q. B., & Linh, N. T. T. (2021). Flood susceptibility modelling using advanced ensemble machine learning models. Geoscience Frontiers, 12(3), 101075. https://doi.org/10.1016/j.gsf.2020.09.006
• Janizadeh, S., Avand, M., Jaafari, A., Phong, T. V., Bayat, M., Ahmadisharaf, E., ... & Lee, S. (2019). Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran. Sustainability, 11(19), 5426. https://doi.org/10.3390/su11195426
• Janizadeh, S., Vafakhah, M., Kapelan, Z., & Dinan, N. M. (2021). Novel Bayesian additive regression tree methodology for flood susceptibility modeling. Water Resources Management, 35, 4621-4646.
• Jenson, S. K., & Domingue, J. O. (1988). Extracting topographic structure from digital elevation data for geographic information system analysis. Photogrammetric engineering and remote sensing, 54(11), 1593-1600.
• Jothimani, M., Dawit, Z., & Mulualem, W. (2021). Flood susceptibility modeling of Megech river catchment, lake tana basin, north western Ethiopia, using morphometric analysis. Earth Systems and Environment, 5, 353-364.
• Kanani-Sadat, Y., Arabsheibani, R., Karimipour, F., & Nasseri, M. (2019). A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. Journal of hydrology, 572, 17-31.
• Kazakis, N., Kougias, I., & Patsialis, T. (2015). Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope–Evros region. Greece. Science of the Total Environment, 538, 555-563. https://doi.org/10.1016/j.scitotenv.2015.08.055
• 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
• Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., & Prakash, I. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311-323. https://doi.org/10.1016/j.jhydrol.2019.03.073
• Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental earth sciences, 67, 251-264.
• Kirkby, E. A. (1979). Maximizing calcium uptake by plants. Comhttps:// doi.org/10.1061/(ASCE)0733- munications in Soil Science and Plant Analysis, 10(1-2), 89-113. https://doi.org/10.1080/00103627909366881
• Kirpich, Z. P. (1940). Time of concentration of small agricultural watersheds. Civil engineering, 10(6), 362.
• Köpük, G. (2003). Bursa Ovası ve yakın çevresinin jeomorfolojisi. Doktora Tezi, İstanbul Üniversitesi, Sosyal Bilimler Enstitüsü, İstanbul.
• Lee, M.J., Kang, J., & Jeon S. (2012). Applıcatıon of Frequency Ratıo Model and valıdatıon for predıctıve flooded area susceptıbılıty mappıng usıng GIS Korea adaptation center for climate change. Geoscience and Remote Sensing Symposium (IGARSS), IEEE International. IEEE, 1, 895–898. https://doi.org/ 10.1109/IGARSS.2012.6351414
• Lin, L., Wu, Z., & Liang, Q. (2019). Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework. Natural Hazards, 97, 455-475.
• Liu, Y. B., & De Smedt, F. (2005). Flood modeling for complex terrain using GIS and remote sensed information. Water resources management, 19, 605-624.
• Mahmood, S., & Rahman, A. U. (2019). Flash flood susceptibility modelling using geomorphometric approach in the Ushairy Basin, eastern Hindu Kush. Journal of Earth System Science, 128, 1-14.
• Mahmoud, S. H., & Gan, T. Y. (2018). Urbanization and climate change implications in flood risk management: Developing an efficient decision support system for flood susceptibility mapping. Science of the Total Environment, 636, 152-167. https://doi.org/10.1016/j.scitotenv.2018.04.282
• Mattivi, P., Franci, F., Lambertini, A., & Bitelli, G. (2019). TWI computation: a comparison of different open-source GISs. Open Geospatial Data, Software and Standards, 4(1), 1-12.
• Merz, B., Thieken, A. H., & Gocht, M. (2007). Flood risk mapping at the local scale: concepts and challenges. Flood risk management in Europe: innovation in policy and practice, 231-251.
• Miller, A. J. (1990). Flood hydrology and geomorphic effectiveness in the central Appalachians. Earth surface processes and landforms, 15(2), 119-134.
• Mind’je, R., Li, L., Amanambu, A. C., Nahayo, L., Nsengiyumva, J. B., Gasirabo, A., & Mindje, M. (2019). Flood susceptibility modeling and hazard perception in Rwanda. International journal of disaster risk reduction, 38, 101211. https://doi.org/10.1016/j.ijdrr.2019.101211
• Mitra, S., & Das, M. (2022). Exploring the Impact of Robotisation on Economic Development. International Journal of Economics, Business and Management Studies, 9(1), 13-27. https://doi.org/10.55284/ijebms.v9i1.626
• Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1), 3-30. https://doi.org/10.1002/hyp.3360050103
• Mudashiru, R. B., Sabtu, N., & Abustan, I. (2021). Quantitative and semi-quantitative methods in flood hazard/susceptibility mapping: a review. Arabian Journal of Geosciences, 14(11), 941.
• Natarajan, L., Usha, T., Gowrappan, M., Palpanabhan Kasthuri, B., Moorthy, P., & Chokkalingam, L. (2021). Flood susceptibility analysis in chennai corporation using frequency ratio model. Journal of the Indian Society of Remote Sensing, 49, 1533-1543.
• Nicholls, N., & Wong, K. K. (1990). Dependence of rainfall variability on mean rainfall, latitude, and the Southern Oscillation. Journal of climate, 163-170.
• Ogato, G. S., Bantider, A., Abebe, K., & Geneletti, D. (2020). Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West shoa zone, oromia regional State, Ethiopia. Journal of Hydrology: Regional Studies, 27, 100659. https://doi.org/10.1016/j.ejrh.2019.100659
• Özdemir, H., & Bird, D. (2009). Evaluation of morphometric parameters of drainage networks derived from topographic maps and DEM in point of floods. Environmental geology, 56, 1405-1415. 10.1007/s00254-008-1235-y
• Özdemir, H. (2011). Havza morfometrisi ve taşkınlar, Fiziki Coğrafya Araştırmaları Sistematik Bölgesel. Babil Yayınevi.
• Özdemir, H., & Akbaş, A. (2023). Sayısal yükseklik modellerindeki mekânsal çözünürlük değişkenliğinin taşkın tehlike analizine etkileri. Journal of Geography, (46), 10.26650/JGEOG2023-1177718
• Özdemir, H., & Akbas, A. (2023). Is there a consistency in basin morphometry and hydrodynamic modelling results in terms of the flood generation potential of basins? A case study from Ulus River Basin (Türkiye). Journal of Hydrology, 129926. https://doi.org/10.1016/j.jhydrol.2023.129926
• Özer, Z. (1990). Su yapılarının projelendirilmesinde hidrolojik ve hidrolik esaslar, Tarım Orman ve Köyişleri Bakanlığı, Köy Hizmetleri Genel Müdürlüğü.
• Öztürk, M. (2010). Uludağ (Zirve) ve Bursa Meteoroloji İstasyonlarının Karşılaştırmalı İklimi. Türk Coğrafya Dergisi, (55), 13-24.
• Öztürk, M. Z. (2012). Uludağ’daki periglasiyal süreçlerin, periglasiyal yerşekillerinin ve bunları denetleyen etmenlerin incelenmesi. Nilüfer Akkılıç Kütüphanesi Yayınları.
• Patton, P. C. (1988). Drainage basin morphometry and floods. Flood Geomorphology. John Wiley & Sons New York. 1988. p 5164. 11 fig, 1 tab, 67 ref.
• Patton, P. C., & Baker, V. R. (1976). Morphometry and floods in small drainage basins subject to diverse hydrogeomorphic controls. Water resources research, 12(5), 941-952. https://doi.org/10.1029/WR012i005p00941
• Penki, R., Basina, S. S., & Tanniru, S. R. (2022). Application of geographical information system-based analytical hierarchy process modeling for flood susceptibility mapping of Krishna District in Andhra Pradesh. Environmental Science and Pollution Research, 1-14.
• Pham, B. T., Avand, M., Janizadeh, S., Phong, T. V., Al-Ansari, N., Ho, L. S., & Prakash, I. (2020). GIS based hybrid computational approaches for flash flood susceptibility assessment. Water, 12(3), 683.
• Pradhan, B. (2010). Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology, 9(2).
• Rahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H. R., & Zeinivand, H. (2015). Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, 8, 7059-7071.
• Rahmati, O., Pourghasemi, H. R., & Zeinivand, H. (2016). Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1), 42-70.
• Rahmati, O., Darabi, H., Panahi, M., Kalantari, Z., Naghibi, S. A., Ferreira, C. S. S., & Haghighi, A. T. (2020). Development of novel hybridized models for urban flood susceptibility mapping. Scientific reports, 10(1), 12937.
• Reddy, G. P. O., Maji, A. K., & Gajbhiye, K. S. (2004). Drainage morphometry and its influence on landform characteristics in a basaltic terrain, Central India–a remote sensing and GIS approach. International Journal of Applied Earth Observation and Geoinformation, 6(1), 1-16.
• Rezaie, F., Bateni, S. M., Heggy, E., & Lee, S. (2021, July). Utilizing the sar, gis, and novel hybrid metaheuristic-gmdh algorithm for flood susceptibility mapping. In 2021 IEEE international geoscience and remote sensing symposium IGARSS (8612-8615). IEEE.
• 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.