Effective Method Selection for Flood Disaster Management: A Decision Support Approach Based on River Type

Year 2025, Volume: 14 Issue: 4, 2336 - 2356, 31.12.2025

Ufuk Yükseler

https://doi.org/10.17798/bitlisfen.1730824

https://izlik.org/JA28DM59RT

Abstract

In recent decades, the frequency and intensity of flood events have escalated, driven primarily by climate change and evolving land-use patterns. These increasingly severe hydrological hazards underscore the critical need for accurate delineation of flood-prone areas and the implementation of effective risk management strategies. This study presents a comparative analysis of flood risk mapping in two hydrologically distinct river basins: the seasonally flowing Çapakçur River and the Continuos Harşit River.
A multi-method approach was employed, utilizing Fuzzy Analytic Hierarchy Process (Fuzzy AHP), Random Forest, HEC-RAS 1D, and HEC-RAS 2D modeling techniques to generate flood hazard maps for each basin. Initially, historical flood reports provided by relevant authorities were converted into spatial disaster records to define the study areas. Subsequently, model-specific data inputs and workflows were implemented, incorporating spatial parameter maps, hydrological datasets, and physical modeling inputs to simulate flood-prone zones.
The performance of each method was evaluated through comparison with past flood occurrences using four validation metrics: accuracy, precision, recall, and F-score. Findings indicate that the Random Forest model consistently achieved the highest accuracy across both river types. While physically based HEC-RAS models demonstrated stable and reliable performance—particularly in continuous flow conditions—the Fuzzy AHP method showed limited predictive capability, primarily due to its reliance on subjective expert judgment.
Overall, the study emphasizes the importance of aligning flood modeling approaches with the hydrological characteristics of the watershed and the nature of available data. The results provide valuable insights into method selection for flood risk assessment and contribute to more informed decision-making in disaster risk reduction and land-use planning.

Keywords

Hecras , Hecgeoras , Geographic İnformation System , Flood , Flood Management , Machine Learning , Fuzzy AHP

Ethical Statement

There are no ethical issues after the publication of this manuscript.

Thanks

I would like to thank the Turkish Republic State Water Works, Emergency and Disaster Coordination Presidency for providing the data for this study.

References

• Fuller, W. E. (1914). Flood flows. Transactions of the American Society of Civil Engineers, 77(1), 564-617.
• Eagleson, P. S. (1972). Dynamics of flood frequency. Water Resources Research, 8(4), 878-898.
• Tingsanchali, T. (2012). Urban flood disaster management. Procedia engineering, 32, 25-37.
• Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2015). Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic environmental research and risk assessment, 29, 1149-1165.
• Zhao, G., Pang, B., Xu, Z., Peng, D., & Xu, L. (2019). Assessment of urban flood susceptibility using semi-supervised machine learning model. Science of the Total Environment, 659, 940-949.
• Munawar, H. S., Hammad, A. W., & Waller, S. T. (2021). A review on flood management technologies related to image processing and machine learning. Automation in Construction, 132, 103916.
• Yukseler, U., Toprak, A., Gul, E., & Dursun, O. F. (2023). Flood hazard mapping using M5 tree algorithms and logistic regression: a case study in East Black Sea Region. Earth Science Informatics, 16(3), 2033-2047.
• Guo, Q., Jiao, S., Yang, Y., Yu, Y., & Pan, Y. (2025). Assessment of urban flood disaster responses and causal analysis at different temporal scales based on social media data and machine learning algorithms. International Journal of Disaster Risk Reduction, 117, 105170.
• Brunner, G. W. (1997). HEC-RAS river analysis system. Hydraulic reference manual. Version 1.0.
• FilipoVa, V. (2012). Urban flooding in Gothenburg-A MIKE 21 study. TVVR12/5010.
• Safitri, D., Putra, R. A., & Dewantoro, D. F. (2022). Analisis Pola Aliran Banjir Pada Sungai Cimadur, Provinsi Banten Dengan Menggunakan Hec-Ras.
• Mondal, K., Ghosh, M., & Karmakar, S. (2025). Global sensitivity analysis in a complex 1D-2D coupled hydrodynamic model: flood hazard and resilience perspectives over an urban catchment. Sustainable Cities and Society, 106279.
• Dasallas, L., Kim, Y., & An, H. (2019). Case study of HEC-RAS 1D–2D coupling simulation: 2002 Baeksan flood event in Korea. Water, 11(10), 2048.
• Farooq, M., Shafique, M., & Khattak, M. S. (2019). Flood hazard assessment and mapping of River Swat using HEC-RAS 2D model and high-resolution 12-m TanDEM-X DEM (WorldDEM). Natural Hazards, 97, 477-492.
• Ghimire, E., & Sharma, S. (2021). Flood damage assessment in HAZUS using various resolution of data and one-dimensional and two-dimensional HEC-RAS depth grids. Natural Hazards Review, 22(1), 04020054.
• Zotou, I., Karamvasis, K., Karathanassi, V., & Tsihrintzis, V. A. (2022). Potential of two sar-based flood mapping approaches in supporting an integrated 1d/2d hec-ras model. Water, 14(24), 4020.
• Ghimire, E., Sharma, S., & Lamichhane, N. (2022). Evaluation of one-dimensional and two-dimensional HEC-RAS models to predict flood travel time and inundation area for flood warning system. ISH Journal of Hydraulic Engineering, 28(1), 110-126.
• Quirogaa, V. M., Kurea, S., Udoa, K., & Manoa, A. (2016). Application of 2D numerical simulation for the analysis of the February 2014 Bolivian Amazonia flood: Application of the new HEC-RAS version 5. Ribagua, 3(1), 25-33.
• Rahimzadeh, O., Bahremand, A., Noura, N., & Mukolwe, M. (2019). Evaluating flood extent mapping of two hydraulic models, 1D HEC‐RAS and 2D LISFLOOD‐FP in comparison with aerial imagery observations in Gorgan flood plain, Iran. Natural resource modeling, 32(4), e12214.
• Yükseler, U., & Dursun, Ö. F. (2024). Taşkın Tahmininde Farklı Havzaların Kullanılması; Artvin Taşkınlarının İncelenmesi Örneği. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 36(2), 827-835.
• Horritt, M. S., & Bates, P. D. (2002). Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of hydrology, 268(1-4), 87-99.
• Arash, A. M., Yasi, M., & Azizian, A. (2020). Accuracy assessment of RS-based DEMs in flood inundation mapping of different morphological types of rivers. Journal of Hydraulics, 15(3), 15-31.
• Patel, D. P., Ramirez, J. A., Srivastava, P. K., Bray, M., & Han, D. (2017). Assessment of flood inundation mapping of Surat city by coupled 1D/2D hydrodynamic modeling: a case application of the new HEC-RAS 5. Natural Hazards, 89, 93-130.
• Arash, A. M., & Yasi, M. (2023). The assessment for selection and correction of RS‐based DEMs and 1D and 2D HEC‐RAS models for flood mapping in different river types. Journal of Flood Risk Management, 16(1), e12871.
• Kalra, A., Joshi, N., Baral, S., Nhuchhen Pradhan, S., Mambepa, M., Paudel, S., ... & Gupta, R. (2021). Coupled 1D and 2D HEC-RAS floodplain modeling of Pecos River in New Mexico. In World Environmental and Water Resources Congress 2021 (pp. 165-178).
• Al-Sabhan, W., Mulligan, M., & Blackburn, G. A. (2003). A real-time hydrological model for flood prediction using GIS and the WWW. Computers, Environment and Urban Systems, 27(1), 9-32.
• Chen, J., Hill, A. A., & Urbano, L. D. (2009). A GIS-based model for urban flood inundation. Journal of Hydrology, 373(1-2), 184-192.
• Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental earth sciences, 67, 251-264.
• Samanta, S., Pal, D. K., & Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, 8(2), 66.
• Yükseler, U., & Dursun, Ö. F. (2024). Shannon Entropi (SE) ve AHP Metoduyla Artvin (Arhavi) Kapisre Taşkınının İncelenmesi. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 29(2), 611-631.
• Yang, X. L., Ding, J. H., & Hou, H. (2013). Application of a triangular fuzzy AHP approach for flood risk evaluation and response measures analysis. Natural hazards, 68, 657-674.
• Hammami, S., Zouhri, L., Souissi, D., Souei, A., Zghibi, A., Marzougui, A., & Dlala, M. (2019). Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arabian Journal of Geosciences, 12, 1-16.
• Yang, W., Xu, K., Lian, J., Ma, C., & Bin, L. (2018). Integrated flood vulnerability assessment approach based on TOPSIS and Shannon entropy methods. Ecological Indicators, 89, 269-280.
• Hossain, M. N., & Mumu, U. H. (2024). Flood susceptibility modelling of the Teesta River Basin through the AHP-MCDA process using GIS and remote sensing. Natural Hazards, 120(13), 12137-12161.
• Sun, X., & Wang, D. (2025). Conflict analysis of disputes in livelihood vulnerability assessment of flood using fuzzy TOPSIS method and GMCR with triangular fuzzy numbers. Scientific Reports, 15(1), 8609.
• Xu, W., Guo, X., Proverbs, D. G., & Han, P. (2025). A GIS-based multi-criteria decision analysis of urban flood risk. International Journal of Building Pathology and Adaptation.
• Tayfur, G., Singh, V. P., Moramarco, T., & Barbetta, S. (2018). Flood hydrograph prediction using machine learning methods. Water, 10(8), 968.
• Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., ... & Prakash, I. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. Journal of Hydrology, 573, 311-323.
• Chen, J., Huang, G., & Chen, W. (2021). Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models. Journal of environmental management, 293, 112810.
• Tanim, A. H., McRae, C. B., Tavakol-Davani, H., & Goharian, E. (2022). Flood detection in urban areas using satellite imagery and machine learning. Water, 14(7), 1140.
• Gul, E. (2025). Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye. Applied Water Science, 15(4), 78.
• Goswami, G., & Prasad, R. K. Hydrodynamic Flood Modeling with HEC-RAS 6.3: A Case Study of the Pakke River in Seijosa, Arunachal Pradesh, India. In World Environmental and Water Resources Congress 2023 (pp. 259-270).
• Al-Sheriadeh, M. S., & Daqdouq, M. A. (2024). Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan. Geomatics, Natural Hazards and Risk, 15(1), 2378991.
• Khodaei, H., Nasiri Saleh, F., Nobakht Dalir, A., & Zarei, E. (2025). Future flood susceptibility mapping under climate and land use change. Scientific Reports, 15(1), 12394.
• Varra, G., İnan, Ç. A., Della Morte, R., Tartaglia, M., Fiduccia, A., Zammuto, A., ... & Cozzolino, L. (2025). Assessment of direct rainfall and flood-induced damage to land transport infrastructure using two-dimensional HEC-RAS 6.6 rain-on-grid simulations. Natural Hazards, 1-31.
• Wahba, M., Essam, R., El-Rawy, M., Al-Arifi, N., Abdalla, F., & Elsadek, W. M. (2024). Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems. Heliyon, 10(13).
• Friedman, J. M., & Lee, V. J. (2002). Extreme floods, channel change, and riparian forests along ephemeral streams. Ecological monographs, 72(3), 409-425.
• Avci, V. (2017). Bingöl İli’nde Nüfus Ve Yerleşmelerin Yükselti Basamaklarina Göre Dağilişi. Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi (Busbed), 7(13), 202-223.
• Avcı V., Esen F., Kıranşan K., (2018), Bingöl İli’nin fiziki coğrafya özellikleri, Bingöl Araştırmalari Dergisi, 4(2), 9-40
• Tarhan N., (1997), 1/100.000 Ölçekli Türkiye Jeoloji Haritaları, Erzurum G 31 ve G 32 Paftaları, Maden Tetkik Arama Enstitüsü Genel Müdürlüğü, Jeoloji Etütleri Dairesi, Ankara
• Çeliker, M., Yükseler, U., & Dursun, Ö. F. (2021). Trend analyses for discharge-recharge of Tacin karstic spring (Kayseri, Turkey). Journal of African Earth Sciences, 184, 104344.
• Kaya, C. M., & Derin, L. (2023). Parameters and methods used in flood susceptibility mapping: a review. Journal of Water and Climate Change, 14(6), 1935-1960.
• Hicks, F. E., & Peacock, T. (2005). Suitability of HEC-RAS for flood forecasting. Canadian water resources journal, 30(2), 159-174.
• Kute, S., Kakad, S., Bhoye, V., & Walunj, A. (2014). Flood modeling of river Godavari using HEC-RAS. Int J Res Eng Technol, 3(09), 81-87.
• Ogras, S., & Onen, F. (2020). Flood Analysis with HEC‐RAS: A Case Study of Tigris River. Advances in Civil Engineering, 2020(1), 6131982.
• Tamiru, H., & Dinka, M. O. (2021). Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia. Journal of Hydrology: Regional Studies, 36, 100855.
• Yükseler, U., & Dursun, Ö. F. (2024). Taşkın Afetlerinin Önceden Tahmin Edilebilirliği; Gümüşhane İlinde Yaşanan Afetlerinin Farklı Yöntemlerle Tahmin Örneklemi. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 11(23), 248-264.
• Hapuarachchi, H. A. P., Wang, Q. J., & Pagano, T. C. (2011). A review of advances in flash flood forecasting. Hydrological processes, 25(18), 2771-2784.
• Han, S., & Coulibaly, P. (2017). Bayesian flood forecasting methods: A review. Journal of Hydrology, 551, 340-351.
• Jain, S. K., Mani, P., Jain, S. K., Prakash, P., Singh, V. P., Tullos, D., ... & Dimri, A. P. (2018). A Brief review of flood forecasting techniques and their applications. International journal of river basin management, 16(3), 329-344.
• Chau, K. W., Wu, C. L., & Li, Y. S. (2005). Comparison of several flood forecasting models in Yangtze River. Journal of Hydrologic Engineering, 10(6), 485-491.
• Nanditha, J. S., & Mishra, V. (2021). On the need of ensemble flood forecast in India. Water Security, 12, 100086.
• Brunner, G. W. (2016). HEC-RAS River Analysis System Hydraulic Reference Manual. USACE.
• USACE. (2023). HEC-RAS User’s Manual (Version 6.x).
• Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, applications and uncertainties. Journal of Hydrology, 551, 224–243.
• Nevo, S., Morin, E., Gerzi Rosenthal, A., Metzger, A., Barshai, C., Weitzner, D., ... & Matias, Y. (2022). Flood forecasting with machine learning models in an operational framework. Hydrology and Earth System Sciences, 26(15), 4013-4032.
• Baalousha, H. M., Younes, A., Yassin, M. A., & Fahs, M. (2023). Comparison of the fuzzy analytic hierarchy process (F-AHP) and fuzzy logic for flood exposure risk assessment in arid regions. Hydrology, 10(7), 136.
• Chang, D-Y.(1996). Applications of The Extent Analysis Method On Fuzzy AHP. European Journal of Operational Research, 95, 649-655.
• Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.
• Forman, E. H., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal of Operational Research, 108(1), 165–169.
• Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert Systems with Applications, 38(11), 14336–14345.
• Zhou, Q., Huang, G. H., & Zhang, H. (2006). A fuzzy AHP approach for environmental decision making. Journal of Environmental Management, 80(2), 131–142.
• Alinezhad, A., & Khalili, J. (2019). New methods and applications in multiple attribute decision making (MADM). Springer.
• Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
• Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140.
• Breiman L (2000a) Some infinity theory for predictor ensembles. Technical Report 577, University of California, Berkeley
• Breiman L (2000b) Randomizing outputs to increase prediction accuracy. Mach Learn 40:229–242
• Breiman L (2001) Random forests. Mach Learn 45:5–32
• Breiman L (2004) Consistency for a simple model of random forests. Technical Report 670, University of California, Berkeley
• 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.
• Feng, Q., Liu, J., & Gong, J. (2015). Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—A case of Yuyao, China. Water, 7(4), 1437-1455.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer.
• Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer.
• Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
• Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
• Belgiu, M., & Drăguț, L. (2016). Random forest in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.
• Zhu, Z., & Zhang, Y. (2022). Flood disaster risk assessment based on random forest algorithm. Neural Computing and Applications, 1-13
• Brunner, G. W. (2021). HEC-RAS River Analysis System Hydraulic Reference Manual (Version 6.0). U.S. Army Corps of Engineers, Hydrologic Engineering Center.
• Lyu, H. M., Shen, S. L., Zhou, A. N., & Zhou, W. H. (2022). Comparison of physically-based and machine learning models for flood hazard assessment. Journal of Hydrology, 608, 127–576.
• Lin, Y., Chen, L., Zhang, Q., & Li, Z. (2021). Assessing hydrodynamic model performance under data-limited conditions for seasonal streams. Natural Hazards, 108(1), 561–584
• Chapi, K., Khosravi, K., Shahabi, H., Pham, B. T., Shirzadi, A., Ahmad, B. B., & Tien Bui, D. (2022). Flood susceptibility mapping using hybrid machine learning models in data-scarce regions. Journal of Hydrology, 606, 127–189.
• Khosravi, K., Pham, B. T., Chapi, K., Shahabi, H., Shirzadi, A., Pradhan, B., Dou, J., & Tien Bui, D. (2020). A comparative assessment of decision tree-based machine learning models and multi-criteria decision-making methods for flood susceptibility mapping. Science of the Total Environment, 723, 138–202.
• Pham, B. T., Le, T. T., Ho, L. S., Prakash, I., & Bui, D. T. (2023). Flood susceptibility mapping using ensemble machine learning approaches: A case study in Central Vietnam. Natural Hazards, 119(3), 2345–2368.
• Kalra, A., & Ahmad, S. (2019). Evaluating the effects of boundary condition and topographic data on HEC-RAS flood simulations. Environmental Modelling & Software, 119, 25–39.
• Toprak, A., Yükseler, U., & Yildizhan, E. (2024). Success of machine learning and statistical methods in predicting landslide hazard: the case of Elazig (Maden). Arabian Journal of Geosciences, 17(10), 275.
• Yükseler, U., Dursun, Ö. F., & Alashan, S. (2021). Yağışların Mevsimsel Değişimlerinin Eğilim Analiz Yöntemleri İle Araştırılması: Bingöl İli Örneği. El-Cezeri, 8(1), 45-59.
• Yükseler, U. (2019). Küresel iklim değişikliğinin akarsuların akış potansiyellerine etkisi; Bingöl Göynük Çayı örneği (Master's thesis, Inonu University (Turkey)).