Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India

Ruba Maarouf; Vazeer Mahammood; Jagadeeswara Rao P

doi:10.31127/tuje.1819040

Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India

Abstract

Flooding remains one of the most destructive natural hazards in the eastern Indian river basins, but the controlling factors and spatial extent have not been thoroughly measured. The research investigates the geospatial assessment of flood prone areas in the Nagavali Basin using the Frequency Ratio (FR) model. The main aim of the study is to delineate monsoon-driven flood susceptibility zones. To do this, geospatial variables, including land use/land cover, distance from river, elevation, slope, landforms, lithology, surface runoff, soil drainage, soil type, topographic wetness index and rainfall were considered. Using Remote Sensing and GIS techniques, the flood susceptible areas of the research area were systematically analyzed and classified into the following five zones: Very Low Susceptibility Zone (36.93%); Low Susceptibility Zone (12.92%); Moderate Susceptibility Zone (18.27%); High Susceptibility Zone (21.19%); and Very High Susceptibility Zone (10.69%). The performance of the FR model was evaluated using 80% of the data for training and 20% for testing. The model had an accuracy of 97% for Very Low Susceptibility Zones for the testing datasets tested. Overall, the model had 100% accuracy in all of the susceptibility zones. The analysis utilized in this study demonstrates the operational efficiency of using the Frequency Ratio model in delineating flood prone areas and showcases the effectiveness of geospatial technologies in risk mapping and disaster management. This study fills the research gap by defining flood susceptibility zones using the Frequency Ratio (FR) model and sophisticated geospatial techniques. Actionable recommendations for local authorities and policy makers to improve their flood preparedness, develop localised mitigation measures and design more sustainable interventions are provided by the results, which can reduce the negative impacts of floods in vulnerable areas of the Nagavali Basin.

Keywords

References

Amutha, R., & Porchelvan, P. (2009). Estimation of surface runoff in Malattar sub-watershed using SCS-CN method. Journal of the Indian Society of Remote Sensing, 37(2), 291–304.
Bisht, D. S., Chatterjee, C., Kalakoti, S., Upadhyay, P., Sahoo, M., & Panda, A. (2016). Modeling urban floods and drainage using SWMM and MIKE URBAN: A case study. Natural Hazards, 84(2), 749–776.
Bhavsar, C., Gadhavi, M., & Shaikh, M. (2025). Impact Assessment of Land Use Change Detection on the Environment of Ahmedabad District, Gujarat, India using Supervised Classification In GIS. Turkish Journal of Engineering, 9(4), 823-830.
Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: Modelling with GIS. Pergamon.
Bezcioğlu, M. (2024). Farklı açık kaynak kodlu tek-frekanslı hassas nokta konum belirleme (SF-PPP) yazılımlarının statik moddaki konum belirleme yeteneklerinin değerlendirilmesi. Geomatik, 9(3), 313-322.
Chen, Y. R., Yeh, C. H., & Yu, B. (2011). Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Natural Hazards, 59(3), 1261–1276.
Choi, J., Oh, H. J., Won, J. S., & Lee, S. (2010). Validation of an artificial neural network model for landslide susceptibility mapping. Environmental Earth Sciences, 60(3), 473–483.
Damaševičius, R. (2010). Optimization of SVM parameters for recognition of regulatory DNA sequences. Top, 18(2), 339–353.

Du, J., Fang, J., Xu, W., & Shi, P. (2013). Analysis of dry/wet conditions using the standardized precipitation index and its potential usefulness for drought/flood monitoring in Hunan Province, China. Stochastic Environmental Research and Risk Assessment, 27(2), 377–387.
Ercan, O. (2020). Essentials of a sustainable land use planning approach for rural areas and a model proposal to be applied under Turkish Conditions. Turkish Journal of Engineering, 4(3), 154-163.
Eren, M., Kadir, S., & Akgöz, M. (2017). Mineralogical, geochemical and micromorphological characteristics of calcite precipitated from a thin cover of recent water taken from the stalagmites in Küpeli Cave, Esenpinar (Erdemli, Mersin), southern Turkey. Turkish Journal of Engineering, 1(2), 44-51.
Fathelrahman, S., & Mohamed, H. (2025). Remote Sensing and GIS methods for detecting changes in Land Cover and Urbanization in Khartoum, Sudan (1975-2022). Turkish Journal of Engineering, 9(4), 678-685.
Gokceoglu, C., & Sezer, E. (2009). A statistical assessment on international landslide literature (1945–2008). Landslides, 6(4), 345–351.
Göksel, C., & Balçık, F. B. (2019). Land use and land cover changes using spot 5 Pansharpen images; A case study in Akdeniz district, Mersin-Turkey. Turkish Journal of Engineering, 3(1), 32-38.
Huang, X., Tan, H., Zhou, J., Yang, T., Benjamin, A., Wen, S. W., Li, S., Liu, A., Li, X., & Fen, S. (2008). Flood hazard in Hunan Province of China: An economic loss analysis. Natural Hazards, 47, 65–73.
Hudson, P., Botzen, W. J. W., Kreibich, H., Bubeck, P., & Aerts, J. C. J. H. (2014). Evaluating the effectiveness of flood damage mitigation measures by the application of propensity score matching. Natural Hazards and Earth System Sciences, 14, 1731–1747.
Lavanya, A. K. (2012). Urban flood management – A case study of Chennai city. Architecture Research, 2(6), 115–121.
Lee, M. J., Kang, J. E., & Jeon, S. (2012). Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 895–898.
Liao, X., & Carin, L. (2009). Migratory logistic regression for learning concept drift between two datasets with application to UXO sensing. IEEE Transactions on Geoscience and Remote Sensing, 47, 1454–1466.
Lohani, A., Kumar, R., & Singh, R. (2012). Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network, and autoregressive techniques. Journal of Hydrology, 442, 23–35.
Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.
Manandhar, B. (2010). Flood plain analysis and risk assessment of Lothar Khola [Master's thesis, Tribhuvan University].
Markantonis, V., Meyer, V., & Lienhoop, N. (2013). Evaluation of the environmental impacts of extreme floods in the Evros River Basin using the contingent valuation method. Natural Hazards, 69, 1535–1549.
Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Assessment of economic flood damage. Natural Hazards and Earth System Sciences, 10, 1697–1724.
Moel, H. D., Vliet, M. V., & Aerts, J. C. J. H. (2014). Evaluating the effect of flood damage-reducing measures: A case study of the unembanked area of Rotterdam, the Netherlands. Regional Environmental Change, 14, 895–908.
Özdemir, E. G., Zengin, T. U., & Güleç, H. A. (2024). Orman ekosistemindeki ağaç boylarının, optik, radar, lazer altimetre uydu verileri ve yardımcı kaynaklar kullanılarak Google Earth Engine platformunda modellenmesi. Geomatik, 9(2), 259-268.
Pal, B., Samanta, S., & Pal, D. K. (2012). Morphometric and hydrological analysis and mapping for Watut watershed using remote sensing and GIS techniques. International Journal of Advances in Engineering and Technology, 2(1), 357–368.
Patel, D. P., & Srivastava, P. K. (2013). Flood hazards mitigation analysis using remote sensing and GIS: Correspondence with town planning schemes. Water Resources Management, 27, 2353–2368.
Poussin, J. K., Botzen, W. J. W., & Aerts, J. C. J. H. (2014). Factors of influence on flood damage mitigation behavior by households. Environmental Science & Policy, 40, 69–77.
Qin, C.-Z., Zhu, A.-X., Pei, T., Li, B.-L., Scholten, T., Behrens, T., & Zhou, C.-H. (2011). An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agriculture, 12, 32–43.
Samanta, S., & Koloa, C. (2014). Modeling coastal flood hazard using ArcGIS spatial analysis tools and satellite image. International Journal of Science and Research, 3(10), 961–967.
Samanta, S., Pal, D. K., & Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing GIS and frequency ratio model. Applied Water Science, 8(66).
Samanta, S., Pal, D. K., Lohar, D., & Pal, B. (2012). Interpolation of climate variables and temperature modeling. Theoretical and Applied Climatology, 107(1), 35–45.
Scheuer, S., Haase, D., & Meyer, V. (2011). Exploring multicriteria flood vulnerability by integrating economic, social, and ecological dimensions of flood risk and coping capacity. Natural Hazards, 58, 731–751.
Solin, L. (2012). Spatial variability in the flood vulnerability of urban areas in the headwater basins of Slovakia. Journal of Flood Risk Management, 5(4), 303–320.
Stieglitz, M., Rind, D., Famiglietti, J., & Rosenzweig, C. (1997). An efficient approach to modeling the topographic control of surface hydrology for regional and global climate modeling. Journal of Climate, 10(1), 118–137.
Tehrany, M. S., Lee, M. J., Pradhan, B., Jebur, M. N., & Lee, S. (2014). Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental Earth Sciences, 72(10), 4001–4015.
Yaman, Ş., & Görmüş, E. T. (2025). Sentinel-2 ve Landsat-8 ile bulut tabanlı orman yangın analizi. Geomatik, 10(3), 316-330.

Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing

Journal Section

Research Article

Authors

Ruba Maarouf ^*
0009-0005-3852-6459
India

Vazeer Mahammood
0009-0009-5945-4561
India

Jagadeeswara Rao P
0009-0009-9586-7742
India

Publication Date

May 1, 2026

Submission Date

November 6, 2025

Acceptance Date

December 13, 2025

Published in Issue

Year 2026 Volume: 10 Number: 2

DOI

https://doi.org/10.31127/tuje.1819040

IZ

https://izlik.org/JA77JA38WZ

Cite

RIS / Bibtex

APA

Maarouf, R., Mahammood, V., & Rao P, J. (2026). Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India. Turkish Journal of Engineering, 10(2), 493-504. https://doi.org/10.31127/tuje.1819040

AMA

1.Maarouf R, Mahammood V, Rao P J. Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India. TUJE. 2026;10(2):493-504. doi:10.31127/tuje.1819040

Chicago

Maarouf, Ruba, Vazeer Mahammood, and Jagadeeswara Rao P. 2026. “Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India”. Turkish Journal of Engineering 10 (2): 493-504. https://doi.org/10.31127/tuje.1819040.

EndNote

Maarouf R, Mahammood V, Rao P J (May 1, 2026) Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India. Turkish Journal of Engineering 10 2 493–504.

IEEE

[1]R. Maarouf, V. Mahammood, and J. Rao P, “Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India”, TUJE, vol. 10, no. 2, pp. 493–504, May 2026, doi: 10.31127/tuje.1819040.

ISNAD

Maarouf, Ruba - Mahammood, Vazeer - Rao P, Jagadeeswara. “Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India”. Turkish Journal of Engineering 10/2 (May 1, 2026): 493-504. https://doi.org/10.31127/tuje.1819040.

JAMA

1.Maarouf R, Mahammood V, Rao P J. Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India. TUJE. 2026;10:493–504.

MLA

Maarouf, Ruba, et al. “Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India”. Turkish Journal of Engineering, vol. 10, no. 2, May 2026, pp. 493-04, doi:10.31127/tuje.1819040.

Vancouver

1.Ruba Maarouf, Vazeer Mahammood, Jagadeeswara Rao P. Advanced Geospatial Analysis and Frequency Ratio Modelling for Flood Hazard Zonation in the Nagavali Basin, India. TUJE. 2026 May 1;10(2):493-504. doi:10.31127/tuje.1819040