Flood Risk Data Analysis in India

Syeda Hafsa FatimaUG Scholar, Department of AI&DS, Methodist College of Engineering and Technology, Hyderabad, IndiaAdiba KhushbooUG Scholar, Department of AI&DS, Methodist College of Engineering and Technology, Hyderabad, IndiaHeena RathoreUG Scholar, Department of AI&DS, Methodist College of Engineering and Technology, Hyderabad, IndiaSana MateenAssistant Professor, Department of CSE, Methodist College of Engineering and Technology, Hyderabad, India

Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 448-455

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 25-05-2026

doi Logo doi.org/10.47001/IRJIET/2026.105062

Abstract

India is a disaster - prone country during the monsoon season. In this scenario, it leads to loss of life and damages homes and crops. To examine this phenomenon, we obtained many datasets from Kaggle that include rainfall, temperature, humidity, river flow data (river water level), elevation (land altitude), land use, land soil, rural and urban population density, the presence of roads and houses and whether or not there has been a past flood event. This study helps India deal with climate change and manage floods in a way by looking at water using geographic information systems and visualizing data.

Keywords

Flood Risk Analysis India, Monsoon Floods, Tableau Visualization, Disaster Management, Climate Change Adaptation.


Citation of this Article

Syeda Hafsa Fatima, Adiba Khushboo, Heena Rathore, & Sana Mateen. (2026). Flood Risk Data Analysis in India. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 448-455. Article DOI https://doi.org/10.47001/IRJIET/2026.105062

References
Mishra, K. and Sinha, R. (2020). Flood risk assessment in the Kosimegafan using multi-criteria decision analysis: A hydro-geomorphic approach. Geomorphology, 350, 106861. DOI: 10.1016/j.geomorph.2019.106861.

Tiwari, M.K. and Chatterjee, C. (2010). Development of an accurate and reliable hourly flood forecasting model using wavelet-bootstrap-ANN (WBANN) hybrid approach. Journal of Hydrology, 394(3-4), 458–470. DOI: 10.1016/j.jhydrol.2010.10.001.

Goswami, B.N., Venugopal, V., Sengupta, D., Madhusoodanan, M.S. and Xavier, P.K. (2006). Increasing Trend of Extreme Rain Events over India in a Warming Environment. Science, 314(5804), 1442–1445. DOI: 10.1126/science.1132027.

Chakraborty, S. and Mukhopadhyay, S. (2019). Assessing flood risk using AHP and GIS: application in Coochbehar district of West Bengal, India. Natural Hazards, 99, 247–274. DOI: 10.1007/s11069-019-03737-7.

Tehrany, M.S., Pradhan, B. and Jebur, M.N. (2014). Flood susceptibility mapping using ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. DOI: 10.1016/j.jhydrol.2014.03.008.

Mojaddadi, H. et al. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080–1102. DOI: 10.1080/19475705.2017.1294113.

Khosravi, K. et al. (2019). A comparative assessment of flood susceptibility modeling using multi-criteria decision-making and machine learning. Journal of Hydrology, 573, 311–323. DOI: 10.1016/j.jhydrol.2019.03.073.

Choubin, B. et al. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, CART, and SVM. Science of the Total Environment, 651, 2087–2096. DOI: 10.1016/j.scitotenv.2018.10.064.

Kundzewicz, Z.W. et al. (2014). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59(1), 1–28. DOI: 10.1080/02626667.2013.857411.

Vegad, U., Pokhrel, Y. and Mishra, V. (2024). Flood risk assessment for Indian sub-continental river basins. Hydrology and Earth System Sciences, 28, 1107–1126. DOI: 10.5194/hess-28-1107-2024.

National Disaster Management Authority (NDMA), India. (2020). National Guidelines on Flood Risk Assessment and Management. Government of India, New Delhi.

Saha, A.K. and Agrawal, S. (2020). Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnology for Environmental Engineering, 5, 11. DOI: 10.1007/s41204-020-00073-1.

Ghosh, A. and Kar, S.K. (2018). Application of AHP for flood risk assessment: a case study in Malda district of West Bengal, India. Natural Hazards, 94(1), 349–368. DOI: 10.1007/s11069-018-3392-y.

Vishnu, C.L. et al. (2019). Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomatics, Natural Hazards and Risk, 10(1), 758–767. DOI: 10.1080/19475705.2018.1543212.

Nanditha, J.S. and Mishra, V. (2021). On the need of ensemble flood forecast in India. Water Security, 12, 100086. DOI: 10.1016/j.wasec.2021.100086.

Guhathakurta, P., Sreejith, O.P. and Menon, P.A. (2011). Impact of climate change on extreme rainfall events and flood risk in India. Journal of Earth System Science, 120(3), 359–373. DOI: 10.1007/s12040-011-0082-5.

Hazarika, N. et al. (2018). Assessing and mapping flood hazard, vulnerability and risk in the Upper Brahmaputra River valley using stakeholders’ knowledge and MCE. Journal of Flood Risk Management, 11, S700–S716. DOI: 10.1111/jfr3.12237.

Mishra, A. et al. (2022). An Overview of Flood Concepts, Challenges, and Future Directions. Journal of Hydrologic Engineering, 27(6), 03122001. DOI: 10.1061/(ASCE)HE.1943-5584.0002164.

Sarkar, D., Saha, S. and Mondal, P. (2019). GIS-based frequency ratio and Shannon’s entropy for flood vulnerability assessment in Patna district, Bihar, India. International Journal of Environmental Science and Technology, 17, 3071–3086. DOI: 10.1007/s13762-019-02627-4.

Dilip K.N., Vegad, U. and Mishra, V. (2025). Drivers of flash floods in the Indian sub-continental river basins.npj Natural Hazards, 2, 62. DOI: 10.1038/s44304-025-00121-3.