Araştırma Makalesi
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Taşkın Duyarlılık Haritalarının Oluşturulmasında Kullanılan Yöntemler

Yıl 2022, Cilt: 3 Sayı: 2, 191 - 209, 18.09.2022
https://doi.org/10.48123/rsgis.1129606

Öz

Son yüzyılda taşkın afetinin ön görülebilen muhtemel zararları ve etkilerini minimize etmek için yapılan bütüncül taşkın yönetimi yaklaşımları arasında taşkın duyarlılık haritalarının oluşturulması önemli bir yere sahiptir. Bu bağlamda, bölgesel ölçekte taşkın duyarlılık analizleri pek çok araştırmacı tarafından araştırma konusu olmuştur. Bu çalışmada taşkın duyarlılık haritalarının üretilmesinde kullanılan hesaplama yöntemleri irdelenmiştir. Bu kapsamda taşkın duyarlılığı ile ilgili 2014-2022 yılları arasında yayımlanmış 155 çalışma değerlendirilmiştir. İncelenen çalışmalarda taşkın duyarlılık değerlendirmelerinde 125’den fazla yöntem kullanıldığı belirlenmiştir. Bu yöntemler arasında çok kriterli karar verme (ÇKKV) yöntemleri, fiziksel tabanlı hidrolojik modeller, istatistiksel yöntemler ve çeşitli esnek hesaplama yöntemleri ön plana çıkmaktadır. Geleneksel istatistiksel yöntemlerin ve çok kriterli karar verme yöntemlerinin kullanım oranının araştırmacılar arasında halihazırda yüksek olduğu, ancak yıllar içinde geleneksel yaklaşımlardaki uzman görüşlerinin temel alındığı yöntemlerden, büyük verilere dayalı istatistiksel ve makine öğrenimi yöntemlerine doğru evirilmiş olduğu görülmüştür. Bununla birlikte son yıllarda makine öğrenimi, bulanık mantık, metasezgisel optimizasyon algoritmaları ve sezgisel arama algoritmalarının duyarlılık haritalarının oluşturulmasında ön plana çıktığı belirlenmiştir.

Kaynakça

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Methods Used in Flood Susceptibility Mapping

Yıl 2022, Cilt: 3 Sayı: 2, 191 - 209, 18.09.2022
https://doi.org/10.48123/rsgis.1129606

Öz

In recent years, flood susceptibility mapping has an important place among the studies carried out to take precautions against floods and mitigate the damages and possible negative effects caused by floods. In this context, flood susceptibility analysis, especially on a regional scale, has been the subject of research by many researchers. In this study, the methods used in flood susceptibility mapping were investigated. 155 studies on flood susceptibility published between 2014 and 2022 were evaluated. In general, the methods used in the determination and evaluation of flood susceptibility are multi-criteria decision making (MCDM) methods, physically based hydrological models, statistical methods and various soft computing methods. Although the use rate of traditional statistical methods and multi-criteria decision making methods is already high among researchers, the methods used in flood susceptibility analysis have evolved over the years from traditional human judgments to statistical methods based on big data and machine learning methods. In the reviewed studies, it has been observed that machine learning, fuzzy logic, metaheuristic optimization algorithms and heuristic search algorithms, which are soft computing methods, have been widely used in the flood susceptibility mapping in recent years.

Kaynakça

  • Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281.
  • 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), 1-19.
  • Albano, R., & Sole, A. (2018). Geospatial methods and tools for natural risk management and communications. ISPRS International Journal of Geo-Information, 7(12), 470-479.
  • Ali, S. A., Parvin, F., Pham, Q. B., Vojtek, M., Vojteková, J., Costache, R., Linh, N. T., Nguyen, H. O., Ahmad, A., & 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. doi: 10.1016/j.ecolind.2020.106620.
  • Arora, A., Arabameri, A., Pandey, M., Siddiqui, M. A., Shukla, U. K., Bui, D. T., Mishra, V. N., & Bhardwaj, A. (2021). Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Science of the Total Environment, 750, 141565. doi: 10.1016/j.scitotenv.2020.141565.
  • Arslankaya, D., & Göraltay, K. (2019). Çok Kriterli Karar Verme Yöntemlerinde Güncel Yaklaşımlar. Ankara: Iksad Publications.
  • Avrupa Komisyonu, (2007). Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks, Official Journal of the European Communities, 288, 27-34.
  • Balica, S. F., Wright, N. G., & Van der Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64(1), 73-105.
  • Balogun, A., Quan, S., Pradhan, B., Dano, U., & Yekeen, S. (2020). An improved flood susceptibility model for assessing the correlation of flood hazard and property prices using geospatial technology and fuzzy-ANP. Journal of Environmental Informatics, 37(2), 107-122.
  • Bera, S., Das, A., & Mazumder, T. (2022). Evaluation of machine learning, information theory and multi-criteria decision analysis methods for flood susceptibility mapping under varying spatial scale of analyses. Remote Sensing Applications: Society and Environment, 25, 100686. doi: 10.1016/j.rsase.2021.100686.
  • Buckley, J. J., & Hayashi, Y. (1994). Fuzzy neural networks: A survey. Fuzzy Sets and Systems, 66(1), 1-13.
  • Bui, Q. T., Nguyen, Q. H., Nguyen, X. L., Pham, V. D., Nguyen, H. D., & Pham, V. M. (2020). Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. Journal of Hydrology, 581, 124379, doi: 10.1016/j.jhydrol.2019.124379.
  • Chen, Z., & Wang, J. (2007). Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada. Natural Hazards, 42(1), 75-89.
  • Chowdary, V. M., Chakraborthy, D., Jeyaram, A., Murthy, Y. V. N., Sharma, J. R., & Dadhwal, V. K. (2013). Multi-criteria decision making approach for watershed prioritization using analytic hierarchy process technique and GIS. Water Resources Management, 27(10), 3555-3571.
  • 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., & Bui, D. T. (2019). Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania. Science of The Total Environment, 691, 1098-1118.
  • Custer, R. (2015). Hierarchical modelling of flood risk for engineering decision analysis. Technical University of Denmark, Department of Civil Engineering. Retrieved from http://orbit. dtu. dk/files/124322422/Rocco_Custer_Til_Orbit. pdf.
  • Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3-4), 213-228.
  • Dano, U. L., Balogun, A. L., Matori, A. N., Wan Yusouf, K., Abubakar, I. R., Said Mohamed, M. A., ... & Pradhan, B. (2019). Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia. Water, 11(3), 615. doi: 10.3390/w11030615.
  • Derin Cengiz, L. (2020). Farklı analitik hiyerarşi süreci yöntemlerinin heyelan duyarlılığı haritalamalarındaki etkinliğinin araştırılması (Doktora Tezi), Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
  • Derin Cengiz, L., & Ercanoglu, M. (2022). A novel data-driven approach to pairwise comparisons in AHP using fuzzy relations and matrices for landslide susceptibility assessments. Environmental Earth Sciences, 81(7), 1-23.
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  • Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51(2), 350-365.
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  • Rahmati, O., Darabi, H., Panahi, M., Kalantari, Z., Naghibi, S. A., Ferreira, C. S. S., Kornejady, A., Karimidastenaei, Z., Mohammadi, F., Stefanidis, S., Bu, D. T., & Haghighi, A. T. (2020). Development of novel hybridized models for urban flood susceptibility mapping. Scientific Reports, 10(1), 12937. doi: 10.1038/s41598-020-69703-7.
  • Rehman, S., Hasan, M. S. U., Rai, A. K., Rahaman, M. H., Avtar, R., & Sajjad, H. (2022). Integrated approach for spatial flood susceptibility assessment in Bhagirathi sub‐basin, India using entropy information theory and geospatial technology. Risk Analysis, doi: 10.1111/risa.13887.
  • Rentschler, J., & Salhab, M. (2020). People in harm's way: Flood exposure and poverty in 189 countries. The World Bank. Retrieved from https://openknowledge.worldbank.org/handle/10986/34655
  • Roy, P., Pal, S. C., Arabameri, A., Rezaie, F., Chakrabortty, R., Chowdhuri, I., ... & Das, B. (2021). Climate and land use change induced future flood susceptibility assessment in a sub-tropical region of India. Soft Computing, 25(8), 5925-5949.
  • Ruidas, D., Chakrabortty, R., Islam, A. R. M., Saha, A., & Pal, S. C. (2022). A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India. Environmental Earth Sciences, 81(5), 145. doi: 10.1007/s12665-022-10269-0.
  • Saaty, T. L. (1985). Decision making for leaders. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(3), 450-452.
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  • Shahabi, H., Shirzadi, A., Ronoud, S., Asadi, S., Pham, B. T., Mansouripour, F., ... & Bui, D. T. (2021). Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geoscience Frontiers, 12(3), 101100. doi: 10.1016/j.gsf.2020.10.007.
  • Siam, Z. S., Hasan, R. T., Anik, S. S., Noor, F., Adnan, M. S. G., & Rahman, R. M. (2021, July). Study of Hybridized Support Vector Regression Based Flood Susceptibility Mapping for Bangladesh. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 59-71). Springer, Cham.
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  • Wubalem, A., Tesfaw, G., Dawit, Z., Getahun, B., Mekuria, T., & Jothimani, M. (2021). Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia. Open Geosciences, 13(1), 1668-1688.
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Toplam 80 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Çağla Melisa Kaya 0000-0002-2664-7510

Yayımlanma Tarihi 18 Eylül 2022
Gönderilme Tarihi 12 Haziran 2022
Kabul Tarihi 21 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 3 Sayı: 2

Kaynak Göster

APA Kaya, Ç. M. (2022). Taşkın Duyarlılık Haritalarının Oluşturulmasında Kullanılan Yöntemler. Türk Uzaktan Algılama Ve CBS Dergisi, 3(2), 191-209. https://doi.org/10.48123/rsgis.1129606

Creative Commons License
Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.