FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ

Topografyanın eğimli ve dik olması, yaz aylarında meydana gelen ekstrem yağışlar ve dere yataklarında yapılaşmanın artışı nedeniyle Fatsa (Ordu) ilçe merkezi ve yakın çevresi son yıllarda giderek daha fazla taşkına maruz kalmaktadır. Bu nedenle taşkın yayılış alanlarının doğru ve tutarlı bir şekilde oluşturulabilmesi için frekans oran metodu, analitik hiyerarşi süreci ve lojistik regresyon modelleri kullanılmıştır. Taşkın alanları AFAD ve Meteoroloji Genel Müdürlüğünden elde edilmiş, taşkını etkileyen 11 bağımsız değişkenle taşkın tehlike tahmin modelleri oluşturulmuştur. Buna göre frekans oran metoduna göre 19,5 km2, analitik hiyerarşi sürecine göre 30,7 km2 ve lojistik regresyon modeline göre 14 km2 alan, yüksek ve çok yüksek riskli taşkın alanı olarak hesaplanmıştır. Bu alanlar nüfus ve yerleşmenin yoğun olduğu Fatsa ilçe merkezine ve vadi tabanlarına karşılık gelmektedir. Çalışmada kullanılan üç yöntemden en yüksek doğruluk oranına sahip model, frekans oran metodudur (%95,9). Ancak arazi gözlemleri neticesinde lojistik regresyon modeli ile oluşturulan taşkın tehlike tahmini haritası, diğer yöntemlere göre doğruya en yakın olduğu tespit edilmiştir. Akarsu mecrasındaki yerleşim alanlarında taşkınların önlenmesi ve iyileştirilmesi için öncelik verilmesi gerekmektedir.

COMPARATIVE USE OF FREQUENCY RATIO, ANALYTICAL HIERARCHY AND LOGISTIC REGRESSION MODELS IN FLOOD HAZARD ESTIMATION, EXAMPLE OF FATSA DISTRICT CENTER AND ITS ENVIRONS

The topography is inclined and upright, excessive rainfall in the summer increase and filling of stream beds with settlement, the Fatsa (Ordu) district center and the near circumference is increasingly exposed to more stones in recent years. For this reason, the frequency rate method, analytical hierarchy process and logistic regression models were used so that torrent and flooding areas can be formed correctly and consistently. Flood areas were obtained from AFAD and the General Directorate of Meteorology. Flood hazard prediction models were created with 11 independent variables affecting the floods. Accordingly, 19.5 km2 according to the frequency ratio method, 30.7 km2 according to the analytical hierarchy process and 14 km2 according to the logistic regression model were calculated as a high and very high risk flood area. These fields correspond to the Fatsa County Center and Valley floor where the population and settlement are intensive. The model with the highest accuracy rate of three methods used in the study is the frequency rate method (95.9%). However, the flood hazard estimated map, created with the logistic regression model, is calculated to be more accurate than other methods as a result of land observations. It is necessary to give priority to the prevention and improvement of floods in the settlements in the course of the rivers.

Kaynakça

Aalbers, E. E., Lenderink, G., van Meijgaard, E., & van den Hurk, B. J. (2018). Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?. Climate Dynamics, 50(11), 4745-4766.

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.

Aniya, M. (1985). Landslide-susceptibility mapping in the Amahata river basin, Japan. Annals of the Association of American Geographers, 75(1), 102-114.

Atkinsson, P. M., & Massari, R. (1998). Generalized linear modeling of susceptibility to landsliding in the central appennines, Italy. Computer & Geoscience, 24, 373-385.

Barker, D. M., Lawler, D. M., Knight, D. W., Morris, D. G., Davies, H. N., & Stewart, E. J. (2009). Longitudinal distributions of river flood power: the combined automated flood, elevation and stream power (CAFES) methodology. Earth Surface Processes and Landforms, 34(2), 280-290.

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.

Bonham-Carter, G. F., & Bonham-Carter, G. (1994). Geographic information systems for geoscientists: modelling with GIS (No. 13). Elsevier.

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.

Chung, C. J. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451-472.

Çınaklı, M. (2008). Doğu Karadeniz bölümü'nde meydana gelen taşkınlar. Ankara Üniversitesi Sosyal Bilimler Enstitüsü Coğrafya Anabilim Dalı Fiziki Coğrafya Bilim Dalı Yüksek Lisan Tezi.

Diakakis, M. (2011). A method for flood hazard mapping based on basin morphometry: application in two catchments in Greece. Natural hazards, 56(3), 803-814.

Dirican, A. (2001). Evaluation of the diagnostic test’s performance and their comparisons. Cerrahpaşa J Med, 32(1), 25-30.

Dölek İ. (2008). Bolaman Çayı Havzasının (Ordu) Uygulamalı Jeomorfoloji Etüdü. İstanbul Üniversitesi Sosyal Bilimler Enstitüsü Coğrafya Anabilim Dalı Doktora Tezi İstanbul-2008.

Ermiş, I. S. (2015). Akarsu havzalarında topoğrafik nem indeksleri ile taşkına meyilli alanların belirlenmesi. İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü İnşaat Mühendisliği Anabilim Dalı Yüksek Lisans Tezi.

Ertorsun, A. D., Bağ, B., Uzar, G., & Turanoğlu, M. A. (2009) ROC (Receiver Operating Characteristic) Eğrisi Yöntemiyle Tanı Testlerinin performanslarının Değerlendirilmesi.

Fernandez, D. S., & Lutz, M. A. (2010). Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111(1-4), 90-98.

Fischer, E. M., & Knutti, R. (2016). Observed heavy precipitation increase confirms theory and early models. Nature Climate Change, 6(11), 986-991.

Florinsky, I. V. (2012). Digital Terrain Analysis in Soil Science and Geology. Digital Terrain Analysis in Soil Science and Geology. Elsevier Inc. https://doi.org/10.1016/C2010-0-65718-X.

Forman, E. H. (1990). Random indices for incomplete pairwise comparison matrices. European journal of operational research, 48(1), 153-155.

GeoFabrik (2021, 1 Haziran). Geofabrik: openstreetmap data, Web Tabanlı Uygulama adresi https://download.geofabrik.de/europe/turkey.html

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(1), 349-368.

Giovannettone, J., Copenhaver, T., Burns, M., & Choquette, S. (2018). A statistical approach to mapping flood susceptibility in the Lower Connecticut River Valley Region. Water Resources Research, 54(10), 7603-7618.

Horton, R. E. (1932). Drainage Basin Characteristics. American Geophysics Union. 350–361.

Innocenti, S., Mailhot, A., Leduc, M., Cannon, A. J., & Frigon, A. (2019). Projected changes in the probability distributions, seasonality, and spatiotemporal scaling of daily and subdaily extreme precipitation simulated by a 50‐member ensemble over northeastern North America. Journal of Geophysical Research: Atmospheres, 124(19), 10427-10449.

IPCC (2012), Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, pp. 582, Cambridge University Press, Cambridge, UK, and New York, NY, USA.

IPCC, (2014). In: Core Writing Team, Pachauri, R.K., Meyer, L.A. (Eds.), Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switz.

Kanık, E. A., & Erden, S. (2003). Tanı Testlerinin değerlendirilmesinde ROC (Receive Operating Characteristics) Eğrisinin Kullanımı. Mersin Üniversitesi Tıp Fakültesi Dergisi, 3, 260-264.

Karagiozi, E., Fountoulis, I., Konstantinidis, A., Andreadakis, E., & Ntouros, K. (2011). Flood hazard assessment based on geomorphological analysis with GIS tools-the case of Laconia (Peloponnesus, Greece).

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.

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(1), 251-264.

Kirkby, M. J., Atkinson, K., & Lockwood, J. O. H. N. (1990). Aspect, vegetation cover and erosion on semi-arid hillslopes (pp. 25-39). John Wiley and Sons Ltd..

Lee, S., & Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental geology, 40(9), 1095-1113.

Lee, S., & Pradhan, B. (2006). Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal of Earth System Science, 115(6), 661-672.

Lei, H., Yang, D., & Huang, M. (2014). Impacts of climate change and vegetation dynamics on runoff in the mountainous region of the Haihe River basin in the past five decades. Journal of Hydrology, 511, 786-799.

Li, X. H., Zhang, Q., Shao, M., & Li, Y. L. (2012). A comparison of parameter estimation for distributed hydrological modelling using automatic and manual methods. In Advanced Materials Research (Vol. 356, pp. 2372-2375). Trans Tech Publications Ltd.

Liu, Y. B., & De Smedt, F. (2005). Flood modeling for complex terrain using GIS and remote sensed information. Water resources management, 19(5), 605-624.

Malik, M. I., Bhat, M. S., & Kuchay, N. A. (2011). Watershed based drainage morphometric analysis of Lidder catchment in Kashmir valley using geographical information system. Recent Research in Science and Technology, 3(4), 118-126.

Manandhar, B. (2010). Flood Plain Analysis and Risk Assessment of Lothar Khola. MSc Thesis. Tribhuvan University. Phokara. Nepal. pp. 64.

Marconi, M., Gatto, B., Magni, M., & Marincioni, F. (2016). A rapid method for flood susceptibility mapping in two districts of Phatthalung Province (Thailand): present and projected conditions for 2050. Natural Hazards, 81(1), 329-346.

Martel, J. L., Mailhot, A., & Brissette, F. (2020). Global and regional projected changes in 100-yr subdaily, daily, and multiday precipitation extremes estimated from three large ensembles of climate simulations. Journal of Climate, 33(3), 1089-1103.

Menard, S. (2002). Applied logistic regression analysis (Vol. 106). SageSaaty, T. L., Vargas, L. G., & Dellmann, K. (2003). The allocation of intangible resources: the analytic hierarchy process and linear programming. Socio-Economic Planning Sciences, 37(3), 169-184.

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.

Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080-1102.

Nag, S. K. (1998). Morphometric analysis using remote sensing techniques in the Chaka sub-basin, Purulia district, West Bengal. Journal of the Indian society of remote sensing, 26(1), 69-76.

Özalp, D. (2009). Dere taşkın risk haritalarının cbs kullanılarak oluşturulması ve cbs ile taşkın risk analizi (Doctoral dissertation, Fen Bilimleri Enstitüsü).

Özlü, T. (2012). Elekçi Deresi (Fatsa) Havzası’nın Hidrolojik Sorunları ve Bunların İklim Şartları İle İlişkileri. ODÜ Sosyal Bilimler Araştırmaları Dergisi (ODÜSOBİAD), 3(6), 282-299.

Özyörük, B., & Özcan, E. C. (2008). Analitik hiyerarşi sürecinin tedarikçi seçiminde uygulanmasi: otomotiv sektöründen bir örnek. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(1), 133-144.

Pradhan, B. (2010a). Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology, 9(2)

Pradhan, B. (2010b). Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in space research, 45(10), 1244-1256.

Sambaziotis, E., & Fountoulis, I. (2007). Estimation of flash flood hazard in the Pidima-Arfara area (Messinia, SW Greece), based on the study of instantaneous unitary hydrographs, longitudinal profilesand stream power. Bulletin of the Geological Society of Greece, 40(4), 1621-1633.

Saaty, L. T. (1980). The Analytic Hierarchy Process. McGraw-Hill Comp.

Saaty, T. L., Vargas, L. G., & Dellmann, K. (2003). The allocation of intangible resources: the analytic hierarchy process and linear programming. Socio-Economic Planning Sciences, 37(3), 169-184.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.

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.

Sahana, M., Rehman, S., Sajjad, H., & Hong, H. (2020). Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India. Catena, 189, 104450.

Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X., & Bronaugh, D. (2013). Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. Journal of Geophysical Research: Atmospheres, 118(6), 2473-2493.

Sunkar, M., & Tonbul, S. (2010). İluh Deresi Havzası’na (Batman) Yönelik Sel ve Taşkın Riski Analizleri. Nature Sciences, 5(4), 255-273.

Strahler, A. N. (1964). Quantitative geomorphology of drainage basin and channel networks. Handbook of applied hydrology.

Şen Z. (2002). Su Bilimi Temel Konuları. Su Vakfı Yayınları İstanbul İ.T.Ü İnşaat Fakültesi Hidroloji Anabilim Dalı

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 models in GIS. Journal of Hydrology, 504, 69-79.

Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91-101.

Tehrany, M.S., Kumar, L., (2018). The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environ. Earth Sci. 77, 490.

Timor, M. (2011). Analitik Hiyerarşi Prosesi, Türkmen Kitabevi, İstanbul.

Toprak, A., (2021). Ağrı ve Doğubayazıt Havzalarında Coğrafi Faktörlerin Sel ve Taşkın Oluşumundaki Etkisi ve Taşkın Risk Analizleri. Fırat Üniversitesi Sosyal Bilimler Enstitüsü / Coğrafya Ana Bilim Dalı / Fiziki Coğrafya Bilim Dalı Doktora Tezi.

Turgut, Ü. (2006). Doğu Karadeniz Bölgesinde Sel Ve Heyelan Felaketine Neden Olan Sinoptik Modellerin Tahmin Tekniği Açısından İncelenmesine Dönük Karşılaştırmalı Bir Araştırma. I. Ulusal Taşkın Sempozyum Kitabı D.S.İ Yayınları Ankara

Wind, Y., & Saaty, T. L. (1980). Marketing applications of the analytic hierarchy process. Management science, 26(7), 641-658.

Wilson, J. P., & Gallant, J. C. (Eds.). (2000). Terrain analysis: principles and applications. John Wiley & Sons.

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.

Zhao, G., Pang, B., Xu, Z., Yue, J., & Tu, T. (2018). Mapping flood susceptibility in mountainous areas on a national scale in China. Science of the Total Environment, 615, 1133-1142.

Ziemer, G. L. (1973). Quantitative geomorphology of drainage basins related to fish production. Alaska Department of Fish and Game, Division of Commercial Fisheries.

Kaynak Göster

Bibtex @araştırma makalesi { igge998492, journal = {lnternational Journal of Geography and Geography Education}, issn = {}, eissn = {2630-6336}, address = {}, publisher = {Marmara Üniversitesi}, year = {2022}, volume = {}, pages = {349 - 379}, doi = {10.32003/igge.998492}, title = {FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ}, key = {cite}, author = {Toprak, Ahmet and Canpolat, Fethi Ahmet} }
APA Toprak, A. & Canpolat, F. A. (2022). FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ . lnternational Journal of Geography and Geography Education , (45) , 349-379 . DOI: 10.32003/igge.998492
MLA Toprak, A. , Canpolat, F. A. "FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ" . lnternational Journal of Geography and Geography Education (2022 ): 349-379 <
Chicago Toprak, A. , Canpolat, F. A. "FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ". lnternational Journal of Geography and Geography Education (2022 ): 349-379
RIS TY - JOUR T1 - FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ AU - Ahmet Toprak , Fethi Ahmet Canpolat Y1 - 2022 PY - 2022 N1 - doi: 10.32003/igge.998492 DO - 10.32003/igge.998492 T2 - lnternational Journal of Geography and Geography Education JF - Journal JO - JOR SP - 349 EP - 379 VL - IS - 45 SN - -2630-6336 M3 - doi: 10.32003/igge.998492 UR - Y2 - 2021 ER -
EndNote %0 lnternational Journal of Geography and Geography Education FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ %A Ahmet Toprak , Fethi Ahmet Canpolat %T FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ %D 2022 %J lnternational Journal of Geography and Geography Education %P -2630-6336 %V %N 45 %R doi: 10.32003/igge.998492 %U 10.32003/igge.998492
ISNAD Toprak, Ahmet , Canpolat, Fethi Ahmet . "FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ". lnternational Journal of Geography and Geography Education / 45 (Ocak 2022): 349-379 .
AMA Toprak A. , Canpolat F. A. FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ. IGGE. 2022; (45): 349-379.
Vancouver Toprak A. , Canpolat F. A. FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ. lnternational Journal of Geography and Geography Education. 2022; (45): 349-379.
IEEE A. Toprak ve F. A. Canpolat , "FREKANS ORAN, ANALİTİK HİYERARŞİ VE LOJİSTİK REGRESYON MODELLERİNİN TAŞKIN TEHLİKE TAHMİNİNDE KARŞILAŞTIRMALI KULLANIMI, FATSA İLÇE MERKEZİ VE YAKIN ÇEVRESİ ÖRNEĞİ", lnternational Journal of Geography and Geography Education, sayı. 45, ss. 349-379, Oca. 2022, doi:10.32003/igge.998492