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Frekans Oranı ve Shannon Entropisi Yöntemi Kullanarak Ezine Çayı Havzası Taşkın Duyarlılık Analizi (Kastamonu-Bozkurt)

Year 2023, Issue: 11, 160 - 178, 15.10.2023

Mustafa Utlu

https://doi.org/10.46453/jader.1358845

Abstract

Taşkın olayları, Türkiye’de özellikle Karadeniz Bölgesi’nde yoğun bir şekilde meydana gelen doğal afetlerin başında gelmektedir. Ekstrem yağışlar, Karadeniz Bölgesi akarsu havzalarında, suların ani bir şekilde yüzeysel akışa geçmesi neticesinde taşkın afetinin yaşanmasında etkili olur. Kastamonu Bozkurt sınırları içerisinde yer alan Ezine Çayı havzası da bu taşkın afetinin gerçekleştiği sahalardan biridir. Dar ve derin vadilerde kısıtlı yerleşim alanlarının varlığı ve taşkın yatağı sınırları içerisinde olması nedeniyle, taşkına duyarlı alanların tespit edilmesi kritik önem taşımaktadır. Coğrafi bilgi sistemleri (CBS) bu amaçla taşkına duyarlı sahaların tespit edilmesinde büyük rol oynamaktadır. Bu çalışmada da taşkın duyarlılığının tespit edilmesi amacıyla CBS temelli iki farklı istatistik yöntem kullanılmıştır. Frekans oranı (FR) ve Shannon Entropisi (SE) yöntemi taşkın duyarlılıkların üretilmesinde tercih edilen yöntemlerdir. Taşkın duyarlılık analizlerinin gerçekleştirilmesinde, Sayısal Yükselti Modeli (SYM), Eğim, Bakı, normalize edilmiş bitki örtüsü indeksi (NDVI), Arazi kullanımı, Topografik nemlilik indeksi (TWI), Akarsu aşındırma gücü (SPI), Jeomorfoloji, Normalize edilmiş yerleşim alan indeksi (NDBI), plan eğrisellik, akarsuya mesafe, drenaj yoğunluğu kullanılan parametrelerdir. 2021 yılı ağustos ayı taşkın yayılış alanı verileri dikkate alınarak oluşturulan envanter verisi, çalışmada yapılan analizlerin doğruluğu için tercih edilmiş, bu analiz için alıcı işletim karakteristiği (ROC) eğrisi kullanılmıştır. Elde edilen sonuçlara göre iki değişkenli istatistik olan frekans oranı yöntemi %.0,976 ile daha yüksek sonuç vermiştir.

Keywords

Taşkın duyarlılık analizi Ezine Çayı Havzası Frekans Oranı Shannon Entropisi

References

  • Al-Abadi, A. M. (2018). Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study. Arabian Journal of Geosciences, 11(9), 218. https://doi.org/10.1007/s12517-018-3584-5
  • Al-Hinai, H., ve Abdalla, R. (2021). Mapping Coastal Flood Susceptible Areas Using Shannon’s Entropy Model: The Case of Muscat Governorate, Oman. ISPRS International Journal of Geo-Information, 10(4), 252. https://doi.org/10.3390/ijgi10040252
  • Alexander, K., Hettiarachchi, S., Ou, Y., ve Sharma, A. (2019). Can integrated green spaces and storage facilities absorb the increased risk of flooding due to climate change in developed urban environments? Journal of Hydrology, 579(October), 124201. https://doi.org/10.1016/j.jhydrol.2019.124201
  • Ali, R., Kuriqi, A., Abubaker, S., ve Kisi, O. (2019). Long-term trends and seasonality detection of the observed flow in Yangtze River using Mann-Kendall and Sen’s innovative trend method. Water (Switzerland), 11(9). https://doi.org/10.3390/w11091855
  • Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojteková, J., Costache, R., Linh, N. T. T., Nguyen, H. Q., Ahmad, A., ve 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(December 2019), 106620. https://doi.org/10.1016/j.ecolind.2020.106620
  • Askar, S., Zeraat Peyma, S., Yousef, M. M., Prodanova, N. A., Muda, I., Elsahabi, M., ve Hatamiafkoueieh, J. (2022). Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms. Water (Switzerland), 14(19), 1–24. https://doi.org/10.3390/w14193062
  • Beckers, A., Dewals, B., Erpicum, S., Dujardin, S., Detrembleur, S., Teller, J., Pirotton, M., ve Archambeau, P. (2013). Contribution of land use changes to future flood damage along the river Meuse in the Walloon region. Natural Hazards and Earth System Sciences, 13(9), 2301–2318. https://doi.org/10.5194/nhess-13-2301-2013
  • Benito, G., Rico, M., Sánchez-Moya, Y., Sopeña, A., Thorndycraft, V. R., ve Barriendos, M. (2010). The impact of late Holocene climatic variability and land use change on the flood hydrology of the Guadalentín River, southeast Spain. Global and Planetary Change, 70(1–4), 53–63. https://doi.org/10.1016/j.gloplacha.2009.11.007
  • Bubeck, P., ve Thieken, A. H. (2018). What helps people recover from floods? Insights from a survey among flood-affected residents in Germany. Regional Environmental Change, 18(1), 287–296. https://doi.org/10.1007/s10113-017-1200-y
  • Bui, D. T., Ngo, P.-T. T., Pham, T. D., Jaafari, A., Minh, N. Q., Hoa, P. V., ve 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/https://doi.org/10.1016/j.catena.2019.04.009
  • Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H., Zhang, S., Pradhan, B., ve Ahmad, B. Bin. (2020). Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Science of the Total Environment, 701, 134979. https://doi.org/10.1016/j.scitotenv.2019.134979
  • Coşkun, M., ve Akbaş, V. (2017). Karadeniz Kıyısından İç Kesi̇me: Kastamonu Çevresi̇ni̇n İkli̇m Parametreleri̇. The Journal of Social Sciences, 4(11), 46–86. https://doi.org/10.16990/sobider.3486
  • Coşkun, S. (2021). Küre Dağlarının Kastamonu iklimi üzerindeki etkileri. Türk Coğrafya Dergisi, 77, 37–52. https://doi.org/10.17211/tcd.833701
  • Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T. T., Abba, S. I., Vojtek, M., Vojteková, J., Nhi, P. T. T., ve Khoi, D. N. (2019). Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques. Remote Sensing, 12(1), 106. https://doi.org/10.3390/rs12010106
  • Das, S., ve 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
  • Deniz, A., Şahin, A. D., Tezer, A., ve Dabanlı, İ. (2021). Bozkurt Sel Afeti, Sebepler ve Tespitler. İTÜ Yayınevi, Ağustos.
  • Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A., Arnold, M., Agwe, J., Buys, P., Kjekstad, O., Lyon, B., ve Yetman, G. (2005). Natural disaster hotspots: a global risk analysis. Içinde World Bank Disaster Risk Management Series (Sayı 5). The World Bank.
  • Dodangeh, E., Choubin, B., Eigdir, A. N., Nabipour, N., Panahi, M., Shamshirband, S., ve Mosavi, A. (2020). Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Science of the Total Environment, 705. https://doi.org/10.1016/j.scitotenv.2019.135983
  • Dodangeh, E., Panahi, M., Rezaie, F., Lee, S., Tien Bui, D., Lee, C.-W., ve Pradhan, B. (2020). Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search. Journal of Hydrology, 590, 125423. https://doi.org/https://doi.org/10.1016/j.jhydrol.2020.125423
  • Doruk, Ö. (2022). BOZKURT: İklim Adaleti Üzerine Saha Notları (B. G. Baykan (ed.)). Yeşil Düşünce Derneği.
  • Gudiyangada Nachappa, T., Tavakkoli Piralilou, S., Gholamnia, K., Ghorbanzadeh, O., Rahmati, O., ve Blaschke, T. (2020). Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology, 590, 125275. https://doi.org/10.1016/j.jhydrol.2020.125275
  • Gürer, I., ve Uçar, I. (2022). The Inventory of Flood Disasters in Turkey. Içinde H. Gökçekuş ve Y. Kassem (Ed.), Climate Change, Natural Resources and Sustainable Environmental Management (ss. 313–322). Springer. https://doi.org/10.1007/978-3-031-04375-8_35
  • Harita Genel Müdürlüğü, 1/25,000 ölçekli topoğrafya haritaları ve 5m çözünürkte Sayısal Yüzey Modeli verisi.
  • Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A. X., Chen, W., Kougias, I., ve Kazakis, N. (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment, 621, 1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114
  • Huang, K., Li, X., Liu, X., ve Seto, K. C. (2019). Projecting global urban land expansion and heat island intensification through 2050. Environmental Research Letters, 14(11). https://doi.org/10.1088/1748-9326/ab4b71
  • Islam, A., ve Sarkar, B. (2021). Analysing flood history and simulating the nature of future floods using Gumbel method and Log-Pearson Type III: The case of the Mayurakshi River Basin, India. Içinde Bulletin of Geography, Physical Geography Series (C. 19, Sayı 1, ss. 43–69). https://doi.org/10.2478/bgeo-2020-0009
  • Jasiewicz, J., ve Stepinski, T. F. (2013). Geomorphons-a pattern recognition approach to classification and mapping of landforms. Geomorphology, 182, 147–156. https://doi.org/10.1016/j.geomorph.2012.11.005
  • Jenks, G. (1967). The Data Model Concept in Statistical Mapping. Içinde International Yearbook of Cartography (ss. 7:186-190).
  • Khan, I., Lei, H., Shah, A. A., Khan, I., ve Muhammad, I. (2021). Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future. Environmental Science and Pollution Research, 28(23), 29720–29731. https://doi.org/10.1007/s11356-021-12801-4
  • Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., ve Tien Bui, D. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266
  • Knighton, A. D. (1999). Downstream variation in stream power. Geomorphology, 29(3–4), 293–306. https://doi.org/10.1016/S0169-555X(99)00015-X
  • Koç, G., Petrow, T., ve Thieken, A. H. (2020). Analysis of the most severe flood events in Turkey (1960-2014): Which triggering mechanisms and aggravating pathways can be identified? Water (Switzerland), 12(6), 1–32. https://doi.org/10.3390/W12061562
  • Koçman, A. (1993). Türkiye iklimi. İzmir: Ege Üniversitesi Edebiyat Fakültesi Yayınları.
  • Li, Y., Badu, F., Hu, T., ve Stein, A. (2023). Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustainable Cities and Society, 88(July 2022), 104307. https://doi.org/10.1016/j.scs.2022.104307
  • 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
  • Liuzzo, L., Sammartano, V., ve Freni, G. (2019). Comparison between Different Distributed Methods for Flood Susceptibility Mapping. Water Resources Management, 33(9), 3155–3173. https://doi.org/10.1007/s11269-019-02293-w
  • Moore, I. D., Grayson, R. B. B., & Ladson, A. R. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30.
  • Onuşluel Gül, G. (2013). Estimating flood exposure potentials in Turkish catchments through index-based flood mapping. Nat Hazards 69, 403–423.
  • Öztürk, M. Z., Çetinkaya, G., & Aydın, S. (2017). Köppen-Geiger İklim Sınıflandırmasına Göre Türkiye’nin İklim Tipleri. Coğrafya Dergisi, 35, 17-27 (In Turkish). https://dergipark.org.tr/iucografya/issue/32204/330955
  • Park, K., & Lee, M. H. (2019). The development and application of the urban flood risk assessment model for reflecting upon urban planning elements. Water (Switzerland), 11(5), 1–17. https://doi.org/10.3390/w11050920
  • Park, K., Lee, M.H. (2019). The Development and Application of the Urban Flood Risk Assessment Model for Reflecting upon Urban Planning Elements. Water.11(5):920. https://doi.org/10.3390/w11050920
  • Pham, B. T., Luu, C., Phong, T. Van, Nguyen, H. D., Le, H. Van, Tran, T. Q., Ta, H. T., & 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(September 2020), 125815. https://doi.org/10.1016/j.jhydrol.2020.125815
  • Pham, B. T., Pradhan, B., Tien Bui, D., Prakash, I., & Dholakia, M. B. (2016). A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling and Software, 84, 240–250. https://doi.org/10.1016/j.envsoft.2016.07.005
  • Pradhan, B., ve Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling ve Software, 25(6), 747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
  • Rahmati, O., Pourghasemi, H. R., ve 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. https://doi.org/10.1080/10106049.2015.1041559
  • Razavi Termeh, S. V., Kornejady, A., Pourghasemi, H. R., ve Keesstra, S. (2018). Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of The Total Environment, 615, 438–451. https://doi.org/https://doi.org/10.1016/j.scitotenv.2017.09.262
  • Samanta, S., Pal, D. K., ve Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, 8(2), 1–14. https://doi.org/10.1007/s13201-018-0710-1
  • Sarkar, D., ve Mondal, P. (2020). Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region. Applied Water Science, 10(1), 1–13. https://doi.org/10.1007/s13201-019-1102-x
  • Sarkar, D., Saha, S., ve 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
  • Serengil, Y., Aydın, Mi̇., Çeler, E., Aytekin, M., ve Özkan, U. (2021). İklim Değişikliğine Adaptasyonda Havza Yaklaşımı: Kent Orman Etkileşiminde Örnek Çalışmalar. Içinde B. Pakdemirli, Ö. Küçük, Z. Bayraktar, ve S. Takmaz (Ed.), EKOLOJİ VE EKONOMİ EKSENİNDE TÜRKİYE’DE ORMAN VE ORMANCILIK (1. baskı, ss. 547–578). Uzun Dijital Matbaa.
  • Shirzadi, A., Chapi, K., Shahabi, H., Solaimani, K., Kavian, A., ve Ahmad, B. Bin. (2017). Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio. Environmental Earth Sciences, 76(4), 1–17. https://doi.org/10.1007/s12665-017-6471-6
  • Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., ve 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
  • Sørensen, R., Zinko, U., ve Seibert, J. (2006). On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrology and Earth System Sciences, 10(1), 101–112. https://doi.org/10.5194/hess-10-101-2006
  • Tariq, A., Yan, J., Ghaffar, B., Qin, S., Mousa, B. G., Sharifi, A., Huq, M. E., ve Aslam, M. (2022). Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data. Water, 14(19), 3069. https://doi.org/10.3390/w14193069
  • Tehrany, M. S., ve 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. Environmental Earth Sciences, 77(13), 1–24. https://doi.org/10.1007/s12665-018-7667-0
  • Tehrany, M. S., Pradhan, B., ve 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., ve 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
  • Tehrany, M. S., Pradhan, B., ve 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(4), 1149–1165. https://doi.org/10.1007/s00477-015-1021-9
  • UNIDDR. (2020). Human cost of disasters: An overview of the last 20 years 2000–2019.
  • Utlu, M., Şimşek, M. & Öztürk, M. Z. (2020). 1D Taşkın Modellemeleri Açısından Topo Dem ve Alos Dsm Verilerinin Karşılaştırılması: Alara Çayı Örneği. Ege Coğrafya Dergisi, 29 (2), 161-177. Retrieved from https://dergipark.org.tr/en/pub/ecd/issue/58525/775152
  • Vafakhah, M., Mohammad Hasani Loor, S., Pourghasemi, H., ve Katebikord, A. (2020). Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping. Arabian Journal of Geosciences, 13(11). https://doi.org/10.1007/s12517-020-05363-1
  • Wahlstrom, M., ve Guha-Sapir, D. (2015). The human cost of weather-related disasters 1995–2015.
  • Wang, Y., Fang, Z., Hong, H., Costache, R., ve Tang, X. (2021). Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. Journal of Environmental Management, 289, 112449. https://doi.org/10.1016/j.jenvman.2021.112449
  • Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., Shirzadi, A., Shahabi, H., Panahi, S., ve 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/https://doi.org/10.1016/j.jenvman.2019.06.102
  • Wang, Y., Li, Z., Tang, Z., ve Zeng, G. (2011). A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China. Water Resources Management, 25(13), 3465–3484. https://doi.org/10.1007/s11269-011-9866-2
  • Waqas, H., Lu, L., Tariq, A., Li, Q., Baqa, M. F., Xing, J., ve Sajjad, A. (2021). Flash flood susceptibility assessment and zonation using an integrating analytic hierarchy process and frequency ratio model for the chitral district, khyber pakhtunkhwa, pakistan. Water (Switzerland), 13(12). https://doi.org/10.3390/w13121650
  • Yilmaz, I., ve Keskin, I. (2009). GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bulletin of Engineering Geology and the Environment, 68(4), 459–471. https://doi.org/10.1007/s10064-009-0188-z
  • Youssef, A. M., Pourghasemi, H. R., ve El-Haddad, B. A. (2022). Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-20213-1
  • Yulianto, F., Fitriana, H. L., ve Sukowati, K. A. D. (2020). Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia. Modeling Earth Systems and Environment, 6(1), 383–395. https://doi.org/10.1007/s40808-019-00686-9
  • Url-1: https://onenationuk.org/libyafloods?gad=1
  • Url-2: https://reliefweb.int/report/libya/unicef-libya-humanitarian-situation-report-no-1-storm-daniel-and-floods-14-september-2023
  • Url-3: https://www.ntv.com.tr/galeri/turkiye/sel-felaketi-yasayan-bozkurtun- okaklari-yeniden-sular- ltinda,EHD39e_cKk2ZZHgd4wmVvA/3jlCJswaDkqsu5BBPgERig
  • Url-4: https://www.aa.com.tr/tr/gundem/sel-felaketinin-yasandigi-bazi-bolgelerde-48-saatteki-yagis-miktari-1-yillik-toplam-yagisin-3te-2sini-buldu/2335014 Url-5: https://www.dw.com/tr/bozkurttaki-felaketin-7-nedeni/a-58881523
  • Url-6:https://www.yenisafak.com/foto-galeri/gundem/goruntuler-korkunc-bozkurttakisel-felaketinin-boyutu-gun-agarinca-ortaya-cikti-2052138?page=18
  • Url-7: https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/curvature-function.htm

Flood Susceptibility Analysis of the Ezine River Basin (Kastamonu-Bozkurt) Using Frequency Ratio and Shannon Entropy Method

Year 2023, Issue: 11, 160 - 178, 15.10.2023

Mustafa Utlu

https://doi.org/10.46453/jader.1358845

Abstract

Taşkın olayları, Türkiye’de özellikle Karadeniz Bölgesi’nde yoğun bir şekilde meydana gelen doğal afetlerin başında gelmektedir. Ekstrem yağışlar, Karadeniz Bölgesi akarsu havzalarında, suların ani bir şekilde yüzeysel akışa geçmesi neticesinde taşkın afetinin yaşanmasında etkili olur. Kastamonu Bozkurt sınırları içerisinde yer alan Ezine Çayı havzası da bu taşkın afetinin gerçekleştiği sahalardan biridir. Dar ve derin vadilerde kısıtlı yerleşim alanlarının varlığı ve taşkın yatağı sınırları içerisinde olması nedeniyle, taşkına duyarlı alanların tespit edilmesi kritik önem taşımaktadır. Coğrafi bilgi sistemleri (CBS) bu amaçla taşkına duyarlı sahaların tespit edilmesinde büyük rol oynamaktadır. Bu çalışmada da taşkın duyarlılığının tespit edilmesi amacıyla CBS temelli iki farklı istatistik yöntem kullanılmıştır. Frekans oranı (FR) ve Shannon Entropisi (SE) yöntemi taşkın duyarlılıkların üretilmesinde tercih edilen yöntemlerdir. Taşkın duyarlılık analizlerinin gerçekleştirilmesinde, Sayısal Yükselti Modeli (SYM), Eğim, Bakı, normalize edilmiş bitki örtüsü indeksi (NDVI), Arazi kullanımı, Topografik nemlilik indeksi (TWI), Akarsu aşındırma gücü (SPI), Jeomorfoloji, Normalize edilmiş yerleşim alan indeksi (NDBI), plan eğrisellik, akarsuya mesafe, drenaj yoğunluğu kullanılan parametrelerdir. 2021 yılı ağustos ayı taşkın yayılış alanı verileri dikkate alınarak oluşturulan envanter verisi, çalışmada yapılan analizlerin doğruluğu için tercih edilmiş, bu analiz için alıcı işletim karakteristiği (ROC) eğrisi kullanılmıştır. Elde edilen sonuçlara göre iki değişkenli istatistik olan frekans oranı yöntemi %.0,976 ile daha yüksek sonuç vermiştir.

Keywords

Flood Susceptibility Analysis Ezine River Basin Frequency Ratio Shannon’s Entropy

References

  • Al-Abadi, A. M. (2018). Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study. Arabian Journal of Geosciences, 11(9), 218. https://doi.org/10.1007/s12517-018-3584-5
  • Al-Hinai, H., ve Abdalla, R. (2021). Mapping Coastal Flood Susceptible Areas Using Shannon’s Entropy Model: The Case of Muscat Governorate, Oman. ISPRS International Journal of Geo-Information, 10(4), 252. https://doi.org/10.3390/ijgi10040252
  • Alexander, K., Hettiarachchi, S., Ou, Y., ve Sharma, A. (2019). Can integrated green spaces and storage facilities absorb the increased risk of flooding due to climate change in developed urban environments? Journal of Hydrology, 579(October), 124201. https://doi.org/10.1016/j.jhydrol.2019.124201
  • Ali, R., Kuriqi, A., Abubaker, S., ve Kisi, O. (2019). Long-term trends and seasonality detection of the observed flow in Yangtze River using Mann-Kendall and Sen’s innovative trend method. Water (Switzerland), 11(9). https://doi.org/10.3390/w11091855
  • Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojteková, J., Costache, R., Linh, N. T. T., Nguyen, H. Q., Ahmad, A., ve 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(December 2019), 106620. https://doi.org/10.1016/j.ecolind.2020.106620
  • Askar, S., Zeraat Peyma, S., Yousef, M. M., Prodanova, N. A., Muda, I., Elsahabi, M., ve Hatamiafkoueieh, J. (2022). Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms. Water (Switzerland), 14(19), 1–24. https://doi.org/10.3390/w14193062
  • Beckers, A., Dewals, B., Erpicum, S., Dujardin, S., Detrembleur, S., Teller, J., Pirotton, M., ve Archambeau, P. (2013). Contribution of land use changes to future flood damage along the river Meuse in the Walloon region. Natural Hazards and Earth System Sciences, 13(9), 2301–2318. https://doi.org/10.5194/nhess-13-2301-2013
  • Benito, G., Rico, M., Sánchez-Moya, Y., Sopeña, A., Thorndycraft, V. R., ve Barriendos, M. (2010). The impact of late Holocene climatic variability and land use change on the flood hydrology of the Guadalentín River, southeast Spain. Global and Planetary Change, 70(1–4), 53–63. https://doi.org/10.1016/j.gloplacha.2009.11.007
  • Bubeck, P., ve Thieken, A. H. (2018). What helps people recover from floods? Insights from a survey among flood-affected residents in Germany. Regional Environmental Change, 18(1), 287–296. https://doi.org/10.1007/s10113-017-1200-y
  • Bui, D. T., Ngo, P.-T. T., Pham, T. D., Jaafari, A., Minh, N. Q., Hoa, P. V., ve 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/https://doi.org/10.1016/j.catena.2019.04.009
  • Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H., Zhang, S., Pradhan, B., ve Ahmad, B. Bin. (2020). Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Science of the Total Environment, 701, 134979. https://doi.org/10.1016/j.scitotenv.2019.134979
  • Coşkun, M., ve Akbaş, V. (2017). Karadeniz Kıyısından İç Kesi̇me: Kastamonu Çevresi̇ni̇n İkli̇m Parametreleri̇. The Journal of Social Sciences, 4(11), 46–86. https://doi.org/10.16990/sobider.3486
  • Coşkun, S. (2021). Küre Dağlarının Kastamonu iklimi üzerindeki etkileri. Türk Coğrafya Dergisi, 77, 37–52. https://doi.org/10.17211/tcd.833701
  • Costache, R., Pham, Q. B., Sharifi, E., Linh, N. T. T., Abba, S. I., Vojtek, M., Vojteková, J., Nhi, P. T. T., ve Khoi, D. N. (2019). Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques. Remote Sensing, 12(1), 106. https://doi.org/10.3390/rs12010106
  • Das, S., ve 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
  • Deniz, A., Şahin, A. D., Tezer, A., ve Dabanlı, İ. (2021). Bozkurt Sel Afeti, Sebepler ve Tespitler. İTÜ Yayınevi, Ağustos.
  • Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A., Arnold, M., Agwe, J., Buys, P., Kjekstad, O., Lyon, B., ve Yetman, G. (2005). Natural disaster hotspots: a global risk analysis. Içinde World Bank Disaster Risk Management Series (Sayı 5). The World Bank.
  • Dodangeh, E., Choubin, B., Eigdir, A. N., Nabipour, N., Panahi, M., Shamshirband, S., ve Mosavi, A. (2020). Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Science of the Total Environment, 705. https://doi.org/10.1016/j.scitotenv.2019.135983
  • Dodangeh, E., Panahi, M., Rezaie, F., Lee, S., Tien Bui, D., Lee, C.-W., ve Pradhan, B. (2020). Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search. Journal of Hydrology, 590, 125423. https://doi.org/https://doi.org/10.1016/j.jhydrol.2020.125423
  • Doruk, Ö. (2022). BOZKURT: İklim Adaleti Üzerine Saha Notları (B. G. Baykan (ed.)). Yeşil Düşünce Derneği.
  • Gudiyangada Nachappa, T., Tavakkoli Piralilou, S., Gholamnia, K., Ghorbanzadeh, O., Rahmati, O., ve Blaschke, T. (2020). Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology, 590, 125275. https://doi.org/10.1016/j.jhydrol.2020.125275
  • Gürer, I., ve Uçar, I. (2022). The Inventory of Flood Disasters in Turkey. Içinde H. Gökçekuş ve Y. Kassem (Ed.), Climate Change, Natural Resources and Sustainable Environmental Management (ss. 313–322). Springer. https://doi.org/10.1007/978-3-031-04375-8_35
  • Harita Genel Müdürlüğü, 1/25,000 ölçekli topoğrafya haritaları ve 5m çözünürkte Sayısal Yüzey Modeli verisi.
  • Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A. X., Chen, W., Kougias, I., ve Kazakis, N. (2018). Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of the Total Environment, 621, 1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114
  • Huang, K., Li, X., Liu, X., ve Seto, K. C. (2019). Projecting global urban land expansion and heat island intensification through 2050. Environmental Research Letters, 14(11). https://doi.org/10.1088/1748-9326/ab4b71
  • Islam, A., ve Sarkar, B. (2021). Analysing flood history and simulating the nature of future floods using Gumbel method and Log-Pearson Type III: The case of the Mayurakshi River Basin, India. Içinde Bulletin of Geography, Physical Geography Series (C. 19, Sayı 1, ss. 43–69). https://doi.org/10.2478/bgeo-2020-0009
  • Jasiewicz, J., ve Stepinski, T. F. (2013). Geomorphons-a pattern recognition approach to classification and mapping of landforms. Geomorphology, 182, 147–156. https://doi.org/10.1016/j.geomorph.2012.11.005
  • Jenks, G. (1967). The Data Model Concept in Statistical Mapping. Içinde International Yearbook of Cartography (ss. 7:186-190).
  • Khan, I., Lei, H., Shah, A. A., Khan, I., ve Muhammad, I. (2021). Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future. Environmental Science and Pollution Research, 28(23), 29720–29731. https://doi.org/10.1007/s11356-021-12801-4
  • Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I., ve Tien Bui, D. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment, 627, 744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266
  • Knighton, A. D. (1999). Downstream variation in stream power. Geomorphology, 29(3–4), 293–306. https://doi.org/10.1016/S0169-555X(99)00015-X
  • Koç, G., Petrow, T., ve Thieken, A. H. (2020). Analysis of the most severe flood events in Turkey (1960-2014): Which triggering mechanisms and aggravating pathways can be identified? Water (Switzerland), 12(6), 1–32. https://doi.org/10.3390/W12061562
  • Koçman, A. (1993). Türkiye iklimi. İzmir: Ege Üniversitesi Edebiyat Fakültesi Yayınları.
  • Li, Y., Badu, F., Hu, T., ve Stein, A. (2023). Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustainable Cities and Society, 88(July 2022), 104307. https://doi.org/10.1016/j.scs.2022.104307
  • 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
  • Liuzzo, L., Sammartano, V., ve Freni, G. (2019). Comparison between Different Distributed Methods for Flood Susceptibility Mapping. Water Resources Management, 33(9), 3155–3173. https://doi.org/10.1007/s11269-019-02293-w
  • Moore, I. D., Grayson, R. B. B., & Ladson, A. R. R. (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5(1), 3–30.
  • Onuşluel Gül, G. (2013). Estimating flood exposure potentials in Turkish catchments through index-based flood mapping. Nat Hazards 69, 403–423.
  • Öztürk, M. Z., Çetinkaya, G., & Aydın, S. (2017). Köppen-Geiger İklim Sınıflandırmasına Göre Türkiye’nin İklim Tipleri. Coğrafya Dergisi, 35, 17-27 (In Turkish). https://dergipark.org.tr/iucografya/issue/32204/330955
  • Park, K., & Lee, M. H. (2019). The development and application of the urban flood risk assessment model for reflecting upon urban planning elements. Water (Switzerland), 11(5), 1–17. https://doi.org/10.3390/w11050920
  • Park, K., Lee, M.H. (2019). The Development and Application of the Urban Flood Risk Assessment Model for Reflecting upon Urban Planning Elements. Water.11(5):920. https://doi.org/10.3390/w11050920
  • Pham, B. T., Luu, C., Phong, T. Van, Nguyen, H. D., Le, H. Van, Tran, T. Q., Ta, H. T., & 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(September 2020), 125815. https://doi.org/10.1016/j.jhydrol.2020.125815
  • Pham, B. T., Pradhan, B., Tien Bui, D., Prakash, I., & Dholakia, M. B. (2016). A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environmental Modelling and Software, 84, 240–250. https://doi.org/10.1016/j.envsoft.2016.07.005
  • Pradhan, B., ve Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling ve Software, 25(6), 747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
  • Rahmati, O., Pourghasemi, H. R., ve 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. https://doi.org/10.1080/10106049.2015.1041559
  • Razavi Termeh, S. V., Kornejady, A., Pourghasemi, H. R., ve Keesstra, S. (2018). Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Science of The Total Environment, 615, 438–451. https://doi.org/https://doi.org/10.1016/j.scitotenv.2017.09.262
  • Samanta, S., Pal, D. K., ve Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, 8(2), 1–14. https://doi.org/10.1007/s13201-018-0710-1
  • Sarkar, D., ve Mondal, P. (2020). Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region. Applied Water Science, 10(1), 1–13. https://doi.org/10.1007/s13201-019-1102-x
  • Sarkar, D., Saha, S., ve 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
  • Serengil, Y., Aydın, Mi̇., Çeler, E., Aytekin, M., ve Özkan, U. (2021). İklim Değişikliğine Adaptasyonda Havza Yaklaşımı: Kent Orman Etkileşiminde Örnek Çalışmalar. Içinde B. Pakdemirli, Ö. Küçük, Z. Bayraktar, ve S. Takmaz (Ed.), EKOLOJİ VE EKONOMİ EKSENİNDE TÜRKİYE’DE ORMAN VE ORMANCILIK (1. baskı, ss. 547–578). Uzun Dijital Matbaa.
  • Shirzadi, A., Chapi, K., Shahabi, H., Solaimani, K., Kavian, A., ve Ahmad, B. Bin. (2017). Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio. Environmental Earth Sciences, 76(4), 1–17. https://doi.org/10.1007/s12665-017-6471-6
  • Siahkamari, S., Haghizadeh, A., Zeinivand, H., Tahmasebipour, N., ve 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
  • Sørensen, R., Zinko, U., ve Seibert, J. (2006). On the calculation of the topographic wetness index: Evaluation of different methods based on field observations. Hydrology and Earth System Sciences, 10(1), 101–112. https://doi.org/10.5194/hess-10-101-2006
  • Tariq, A., Yan, J., Ghaffar, B., Qin, S., Mousa, B. G., Sharifi, A., Huq, M. E., ve Aslam, M. (2022). Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data. Water, 14(19), 3069. https://doi.org/10.3390/w14193069
  • Tehrany, M. S., ve 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. Environmental Earth Sciences, 77(13), 1–24. https://doi.org/10.1007/s12665-018-7667-0
  • Tehrany, M. S., Pradhan, B., ve 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., ve 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
  • Tehrany, M. S., Pradhan, B., ve 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(4), 1149–1165. https://doi.org/10.1007/s00477-015-1021-9
  • UNIDDR. (2020). Human cost of disasters: An overview of the last 20 years 2000–2019.
  • Utlu, M., Şimşek, M. & Öztürk, M. Z. (2020). 1D Taşkın Modellemeleri Açısından Topo Dem ve Alos Dsm Verilerinin Karşılaştırılması: Alara Çayı Örneği. Ege Coğrafya Dergisi, 29 (2), 161-177. Retrieved from https://dergipark.org.tr/en/pub/ecd/issue/58525/775152
  • Vafakhah, M., Mohammad Hasani Loor, S., Pourghasemi, H., ve Katebikord, A. (2020). Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping. Arabian Journal of Geosciences, 13(11). https://doi.org/10.1007/s12517-020-05363-1
  • Wahlstrom, M., ve Guha-Sapir, D. (2015). The human cost of weather-related disasters 1995–2015.
  • Wang, Y., Fang, Z., Hong, H., Costache, R., ve Tang, X. (2021). Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. Journal of Environmental Management, 289, 112449. https://doi.org/10.1016/j.jenvman.2021.112449
  • Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., Shirzadi, A., Shahabi, H., Panahi, S., ve 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/https://doi.org/10.1016/j.jenvman.2019.06.102
  • Wang, Y., Li, Z., Tang, Z., ve Zeng, G. (2011). A GIS-Based Spatial Multi-Criteria Approach for Flood Risk Assessment in the Dongting Lake Region, Hunan, Central China. Water Resources Management, 25(13), 3465–3484. https://doi.org/10.1007/s11269-011-9866-2
  • Waqas, H., Lu, L., Tariq, A., Li, Q., Baqa, M. F., Xing, J., ve Sajjad, A. (2021). Flash flood susceptibility assessment and zonation using an integrating analytic hierarchy process and frequency ratio model for the chitral district, khyber pakhtunkhwa, pakistan. Water (Switzerland), 13(12). https://doi.org/10.3390/w13121650
  • Yilmaz, I., ve Keskin, I. (2009). GIS based statistical and physical approaches to landslide susceptibility mapping (Sebinkarahisar, Turkey). Bulletin of Engineering Geology and the Environment, 68(4), 459–471. https://doi.org/10.1007/s10064-009-0188-z
  • Youssef, A. M., Pourghasemi, H. R., ve El-Haddad, B. A. (2022). Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-20213-1
  • Yulianto, F., Fitriana, H. L., ve Sukowati, K. A. D. (2020). Integration of remote sensing, GIS, and Shannon’s entropy approach to conduct trend analysis of the dynamics change in urban/built-up areas in the Upper Citarum River Basin, West Java, Indonesia. Modeling Earth Systems and Environment, 6(1), 383–395. https://doi.org/10.1007/s40808-019-00686-9
  • Url-1: https://onenationuk.org/libyafloods?gad=1
  • Url-2: https://reliefweb.int/report/libya/unicef-libya-humanitarian-situation-report-no-1-storm-daniel-and-floods-14-september-2023
  • Url-3: https://www.ntv.com.tr/galeri/turkiye/sel-felaketi-yasayan-bozkurtun- okaklari-yeniden-sular- ltinda,EHD39e_cKk2ZZHgd4wmVvA/3jlCJswaDkqsu5BBPgERig
  • Url-4: https://www.aa.com.tr/tr/gundem/sel-felaketinin-yasandigi-bazi-bolgelerde-48-saatteki-yagis-miktari-1-yillik-toplam-yagisin-3te-2sini-buldu/2335014 Url-5: https://www.dw.com/tr/bozkurttaki-felaketin-7-nedeni/a-58881523
  • Url-6:https://www.yenisafak.com/foto-galeri/gundem/goruntuler-korkunc-bozkurttakisel-felaketinin-boyutu-gun-agarinca-ortaya-cikti-2052138?page=18
  • Url-7: https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/curvature-function.htm
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Details

Primary Language Turkish
Subjects Natural Hazards
Journal Section Articles
Authors

Mustafa Utlu 0000-0002-7508-4478

Early Pub Date October 9, 2023
Publication Date October 15, 2023
Submission Date September 12, 2023
Acceptance Date October 9, 2023
Published in Issue Year 2023 Issue: 11

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APA 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
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