Authors

Y Mohan Das

Department of Computer Science Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Gavvala Srujana

Department of Computer Science Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Shaik Sabiha Sulthana

Department of Computer Science Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Gollapalli Ganesh

Department of Computer Science Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Banka Gowri Shankar

Department of Computer Science Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Dasagari Vamsi

Department of Computer Science Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

Effective disaster risk management relies on science-based solutions to close the gap between prevention and preparedness measures. The consultation on the United Nations post-2015 framework for disaster risk reduction highlights the need for cross-border early warning systems to strengthen the preparedness phases of disaster risk management, in order to save lives and property and reduce the overall impact of severe events. Continental and global scale flood forecasting systems provide vital early flood warning information to national and international civil protection authorities, who can use this information to make decisions on how to prepare for upcoming floods. Here the potential monetary benefits of early flood warnings are estimated based on the forecasts of the continental-scale European Flood Awareness System (EFAS) using existing flood damage cost information and calculations of potential avoided flood damages. The benefits are of the order of 400 Euro for every 1 Euro invested. A sensitivity analysis is performed in order to test the uncertainty in the method and develop an envelope of potential monetary benefits of EFAS warnings. The results provide clear evidence that there is likely a substantial monetary benefit in this cross-border continental-scale flood early warning system. This supports the wider drive to implement early warning systems at the continental or global scale to improve our resilience to natural hazards.

Keywords

Random Forest Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Logistic Regression MLP (Multiplier) KNN (k-nearest neighbour)

Citation of this Article

Y Mohan Das, Gavvala Srujana, Shaik Sabiha Sulthana, Gollapalli Ganesh, Banka Gowri Shankar, & Dasagari Vamsi. (2025). Flood Prediction Using Machine Learning. Current Journal of Engineering and Science Research. 2(4), 12-17. Article DOI: https://doi.org/10.47001/CJESR/2025.204003

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. Pappenberger, F.; Cloke, H.L.; Parker, D.J.; Wetterhall, F.; Richardson, D.S.; Thielen, J. The monetary benefit of early flood warnings in Europe. Environ. Sci. Policy 2015, 51, 278–291.
  2. Krzysztofowicz, R. Bayesian system for probabilistic river stage forecasting. J. Hydrol. 2002, 268, 16–40.
  3. Todini, E. Role and treatment of uncertainty in real-time flood forecasting. Hydrol. Process. 2004, 18, 2743–2746.
  4. Clark, M.P.; Slater, A.G. Probabilistic quantitative precipitation estimation in complex terrain. J. Hydrometeorol. 2006, 7, 3–22.
  5. [5] Vrugt, J.A.; Robinson, B.A. Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging. Water Resources. 2007, 43.
  6. Ebtehaj, M.; Moradkhani, H.; Gupta, H.V. Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap re-sampling. Water Resour. Res. 2010, 46.
  7. He, X.; Refsgaard, J.C.; Sonnenborg, T.O.; Vejen, F.; Jensen, K.H. Statistical analysis of the impact of radar rainfall uncertainties on water resources modeling. Water Resources. 2011, 47.
  8. Legleiter, C.J.; Kyriakidis, P.C.; McDonald, R.R.; Nelson, J.M. Effects of uncertain topographic input data on two dimensional flow modelling in a gravel-bed river. Water Resour. Res. 2011, 47.
  9. Sikorska, A.E.; Scheidegger, A.; Banasik, K.; Rieckermann, J. Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models. Hydrol. Earth Syst. Sci. 2012, 16, 1221–1236.
  10. Montanari, A.; Koutsoyiannis, D. A blueprint for process-based modeling of uncertain hydrological systems. Water Resour. Res. 2012, 48.
  11. Prof. Parthasarathi Choudhary and A. Sankarasubramanian (2009), ―River Flood Forecasting Using Complementary Muskingumrating Equations ‖ ,Journal of Hydrologic Engineering, Vol. 14, No. 7, July 1, 2009.
  12. Korada Hari Venkata Durga Rao, Vala Venkateshwar Rao, Vinay Kumar Dadhwal, Gandarbha Behera, and Jaswant Raj Sharma, ―A Distributed Model for Real-Time Flood Forecasting In The Godavari Basin Using Space Inputs‖, International Journal Disaster Risk Sci. 2011, 2 (3): 31– 40.
  13. Sulafa Hag Elsafi (2014) ―Artificial Neural Networks (Anns) For Flood Forecasting at Dongola Station in the River Nile, Sudan‖, Alexandria Engineering Journal (2014) 53, 655–662.