Taşkın Duyarlılık Haritalarının Oluşturulmasında Kullanılan Yöntemler

Year 2022, Volume: 3 Issue: 2, 191 - 209, 18.09.2022

Çağla Melisa Kaya

https://doi.org/10.48123/rsgis.1129606

Cited By: 2

Abstract

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.

Keywords

Taşkın duyarlılık haritalaması, Esnek hesaplama, ÇKKV, İstatistiksel yöntemler, Makine öğrenimi, Metasezgisel optimizasyon algoritmaları

Methods Used in Flood Susceptibility Mapping

Abstract

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.

Keywords

Flood susceptibility mapping, Soft computing, MCDM, Statistical methods, Machine learning, Metaheuristic optimization algorithms

References

• 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.
• DFO. (2014). Dartmouth Flood Observatory. Retrieved from: http://www.dartmouth.edu/
• Dimitriadis, P., Tegos, A., Oikonomou, A., Pagana, V., Koukouvinos, A., Mamassis, N., ... & Efstratiadis, A. (2016). Comparative evaluation of 1D and quasi-2D hydraulic models based on benchmark and real-world applications for uncertainty assessment in flood mapping. Journal of Hydrology, 534, 478-492.
• Ibrahim, D. (2016). An overview of soft computing. Procedia Computer Science, 102, 34-38.
• EM-DAT. (2012, Mart 30). The international disaster database. Natural Disasters Trends. Retrieved from http://www.emdat.be/natural-disasters-trends
• Fenicia, F., Kavetski, D., Savenije, H. H., Clark, M. P., Schoups, G., Pfister, L., & Freer, J. (2014). Catchment properties, function, and conceptual model representation: is there a correspondence?. Hydrological Processes, 28(4), 2451-2467.
• Hamurcu, M., & Eren, T. (2015). Ankara Büyükşehir Belediyesi’nde çok ölçütlü karar verme yöntemi ile monoray güzergâh seçimi. Transist, 8, 410-419.
• Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A. X., & Chen, W. (2018). Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Science of the Total Environment, 625, 575-588.
• Jacinto, R., Grosso, N., Reis, E., Dias, L., Santos, F. D., & Garrett, P. (2015). Continental Portuguese territory flood susceptibility index–contribution to a vulnerability index. Natural Hazards and Earth System Sciences, 15(8), 1907-1919.
• Jeb, D. N., & Aggarwal, S. P. (2008). Flood inundation hazard modeling of the River Kaduna using remote sensing and geographic information systems. Journal of Applied Sciences Research, 4(12), 1822-1833.
• Jebur, M. N., Pradhan, B., & Tehrany, M. S. (2014). Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sensing of Environment, 152, 150-165.
• Jha, A. K., Bloch, R., & Lamond, J. (2012). Cities and flooding: a guide to integrated urban flood risk management for the 21st century. Washington D.C., USA: World Bank Publications.
• Kaya, Ç. M. (2017). Akım gözlem istasyonu bulunmayan taşkın havzalarındaki değişimlerin taşkın riskine etkisinin belirlenmesi: Rize, Güneysu örneği (Doktora Tezi). Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon, Türkiye.
• Kaya, Ç. M. (2018). Taşkın riskinin belirlenmesinde sosyo-demogrofik ve sosyo-ekonomik özelliklerin önemi. Afet ve Risk Dergisi, 1(1), 53-62.
• Kaya, Ç. M. (2021). 1B ve 2B taşkın modellemesinin karşılaştırılması: Fol deresi örneği. Afet ve Risk Dergisi, 5(1), 13-21.
• 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.
• Knebl, M. R., Yang, Z. L., Hutchison, K., & Maidment, D. R. (2005). Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event. Journal of Environmental Management, 75(4), 325-336.
• Kundzewicz, Z. W., Pińskwar, I., & Brakenridge, G. R. (2013). Large floods in Europe, 1985–2009. Hydrological Sciences Journal, 58(1), 1-7. doi: 10.1080/02626667.2012.745082.
• Lavoie, B., & Mahdi, T. F. (2017). Comparison of two-dimensional flood propagation models: SRH-2D and Hydro_AS-2D. Natural Hazards, 86(3), 1207-1222.
• Lee, S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477-1491.
• Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47(7), 982-990.
• Lee, S., & Pradhan, B. (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1), 33-41.
• Li, Q., Jiang, X., & Liu, D. (2013). Analysis and modelling of flood risk assessment using information diffusion and artificial neural network. Water SA, 39(5), 643-648.
• Li, X., Yan, D., Wang, K., Weng, B., Qin, T., & Liu, S. (2019). Flood risk assessment of global watersheds based on multiple machine learning models. Water, 11(8), 1654-1672.
• Liu, J., Wang, J., Xiong, J., Cheng, W., Li, Y., Cao, Y., He, Y., Duan, Y., He, W., & Yang, G. (2022). Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region. Geocarto International, doi: 10.1080/10106049.2022.2025918.
• Mahmoud, S. H., & Gan, T. Y. (2018). Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. Journal of Cleaner Production, 196, 216-229.
• Neumüller, C., Wagner, S., Kronberger, G., & Affenzeller, M. (2011, February). Parameter meta-optimization of metaheuristic optimization algorithms. In International Conference on Computer Aided Systems Theory. Proceedings. (pp. 367-374). Springer, Berlin, Heidelberg.
• Pham, B. T., Shirzadi, A., Bui, D. T., Prakash, I., & Dholakia, M. B. (2018). A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: A case study in the Himalayan area, India. International Journal of Sediment Research, 33(2), 157-170.
• 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.
• 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., 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.
• Shuncai, S., & Katayama, T. (1994). The flood disaster and mitigation projects in Yangtze Delta area. In INCEDE Report 1994-01 (Vol. 2, pp. 143-8). International Center for Disaster-Mitigation Engineering.
• Saleh, A., Yuzir, A., Sabtu, N., Abujayyab, S. K., Bunmi, M. R., & Pham, Q. B. (2022). Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method. Geocarto International, doi: 10.1080/10106049.2022.2032394.
• 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.
• Sole, A., Giosa, L., Albano, R., & Cantisani, A. (2013). The laser scan data as a key element in the hydraulic flood modelling in urban areas. Proceedings of the international archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archive, XL-4/W1, 65–70.
• Stavropoulos, S., Zaimes, G. N., Filippidis, E., Diaconu, D. C., & Emmanouloudis, D. (2020). Mitigating flash floods with the use of new technologies: A Μulti-criteria decision analysis to map flood susceptibility for Zakynthos Island, Greece. Journal of Urban and Regional Analysis, 12(2), 233-248.
• Tella, A., & Balogun, A. L. (2020). Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria. Natural Hazards, 104(3), 2277-2306.
• 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., & Jebur, M. N. (2014a). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332-343.
• Tehrany, M. S., Lee, M. J., Pradhan, B., Jebur, M. N., & Lee, S. (2014b). Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental Earth Sciences, 72(10), 4001-4015.
• 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., Jones, S., & Shabani, F. (2019). Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena, 175, 174-192.
• Termeh, S. V. R., Kornejady, A., Pourghasemi, H. R., & 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.
• 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(3), 701-714.
• UNDP. (2004). World Energy Assessment—Energy and the Challenge of Sustainability. Overview: 2004 Update. United Nations Development Programme, New York.
• USGS. (2022, Mart 30). Floods and Flood Plains. Retrieved from https://pubs.usgs.gov/of/1993/ofr93-641/pdf/ofr93-641.pdf
• URL-1. (2022, Mart 30). Esnek Hesaplamaya Giriş. Retrieved from https://ekblc.files.wordpress.com/2013/09/esnek-hesaplamaya-giric59f.pdf
• URL-2. (2022, Mart 30). Yapay zeka: esnek hesaplama ve örnek bazı uygulamalar. Retrieved from https://cdn.bartin.edu.tr/istatistikhesaplama/04e2a9cf26efbde9d694b803c2b5074a/yzesnekhesaplama_4guIZTz.pdf
• Malczewski, J. (1999). GIS and Multicriteria Decision Analysis. New York, NY: John Wiley & Sons.
• Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S., & Bai, X. (2015). Flood hazard risk assessment model based on random forest. Journal of Hydrology, 527, 1130-1141.
• Wang, Y., Hong, H., Chen, W., Li, S., Pamučar, D., Gigović, L., Dropnjak, S., Bui D. T., & Duan, H. (2018). A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sensing, 11(1), 62. doi: 10.3390/rs11010062.
• 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.
• Vojtek, M., & Vojteková, J. (2019). Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water, 11(2), 364-381.
• Zadeh, L. A. (1994, April). Fuzzy logic: issues, contentions and perspectives. In ICASSP'94. IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings. (Vol. 6, pp. VI/183). IEEE.