Authors : Gautam Kumar Sinha; Dr. Komal Saxena

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/3ez92wek

DOI : https://doi.org/10.38124/ijisrt/25may2128

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Floods rank among the most devastating natural calamities in the Indian plains, where monsoon regimes and large river systems result in recurrent flooding. Knowing the hydrometeorological precursors of floods is paramount to enhance predictive accuracy and decrease risks. This study investigates the most significant hydrometeorological parameters influencing flood events in the Indian plains and develops predictive models using Machine Learning (MI) and Deep Learning techniques. This research uses a 60-year historical rainfall record of five cities, Patna, Kanpur, Prayagraj, Haridwar, and Varanasi, collected from the India Meteorological Department (IMD). Robust statistical modelling and feature selection techniques determine the most significant flood predictors. The research adopts some of the machine and deep learning techniques such as Random Forest, Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Long Short-Term Memory (LSTM) networks, Fully Connected Networks (FCN), Deep FCN, Convolutional Neural Networks (CNNs) to evaluate their performance when making flood forecasts. The results show that the intensity of rainfall plays a vital role in determining floods. LSTM networks handle the time sequential data and generate the future rainfall data, providing an FCN-trained model for better prediction accuracy. The proposed Deep Learning - based models demonstrate the effectiveness of early flood warning systems that allow authorities to initiate preventive measures promptly. The results also demonstrate the significance of region-specific flood prevention measures in response to climate variability and land- use changes in the Indian plains. By improving the accuracy of flood forecasts, the study is helpful to disaster management agencies, policymakers, and researchers. The present research will inspire the integration of onboard data sources from other locations and the model's generalizability to other flood-risk areas. Roll-out of Machine Learning and Deep Learning - driven approaches in flood forecasting will significantly minimize the socio-economic impact of floods, leading to enhanced preparedness and resilience for high-risk communities.

Keywords : Flood, Rainfall, Rainfall-Threshold, Machine Learning, Deep Learning (LSTM, FCN), Flood Forecasting.

References :

1. Ashley, S. T., & Ashley, W. S. (2007). The storm morphology of deadly flooding events in the United States. International Journal of Climatology, 28(4), 493–503. Portico. https://doi.org/10.1002/joc.1554
2. British Pathé. (1975). Floods In Bihar – Patna Under Water
3. Central Water Commission. (2011). Flood Affected Area Atlas of India – Technical Document. Ministry of Water Resources
4. Cunha, L. (2022). Deep learning with Python (2ª ed) - François Chollet - Manning, outubro 2021, 504 pp. Interações: Sociedade e as Novas Modernidades, 42, 113–115. https://doi.org/10.31211/interacoes.n42.2022.r1
5. Dhar, O. N., & Nandargi, S. (2000). A study of floods in the Brahmaputra basin in India. International Journal of Climatology, 20(7), 771–781. https://doi.org/10.1002/1097-0088(20000615)20:7<771::aid-joc518>3.0.co;2-z
6. District Disaster Management Authority (DDMA), Haridwar. (2022). District Disaster Management Plan – Haridwar
7. Fabian, P. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research 12, 2825.
8. Flood Management Improvement Support Centre (FMISC), Water Resources Department, Bihar. (2021). Flood Reports and Statistics.
9. Gadgil, S. (2018). The monsoon system: Land–sea breeze or the ITCZ? Journal of Earth System Science, 127(1). https://doi.org/10.1007/s12040-017-0916-x
10. Heaton, J. (2017). Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning. Genetic Programming and Evolvable Machines, 19(1–2), 305–307. https://doi.org/10.1007/s10710-017-9314-z
11. Hindustan Hindi News. (2019, September 29).
12. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
13. IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/9781009157896
14. Kabir, S., Patidar, S., Xia, X., Liang, Q., Neal, J., & Pender, G. (2020). A deep convolutional neural network model for rapid prediction of fluvial flood inundation. Journal of Hydrology, 590, 125481. https://doi.org/10.1016/j.jhydrol.2020.125481
15. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
16. McKinney, Wes. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media, Inc.
17. Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1–2), 202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012
18. Mishra, M., Singh, A., & Tripathi, R. (2022). Assessment of Flood Hazard and Vulnerability in Varanasi District Using Geospatial Techniques.
19. Mosavi, A., Ozturk, P., & Chau, K. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536
20. Pradhan, B., Kumari, S., & Mondal, A. (2021). Application of Machine Learning Algorithms for Flood Susceptibility Mapping in India. Remote Sensing
21. Roy, D., & Sharma, N. (2022). Deep Learning-Based Flood Forecasting Using Convolutional Neural Networks (CNN). Water Resources Management, 36(5), 1823–1837
22. Singh, A., & Ramesh, R. (2020). Flood Prediction Using LSTM Neural Networks, 7(3), 45.
23. Singh, D., Tsiang, M., Rajaratnam, B., & Diffenbaugh, N. S. (2018). Monsoon variability and its impact on Indian floods. Nature Climate Change
24. Situ, Z., Wang, Q., Teng, S., Feng, W., Chen, G., Zhou, Q., & Fu, G. (2024). Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion. Journal of Hydrology, 630, 130743. https://doi.org/10.1016/j.jhydrol.2024.130743
25. Times of India. (2020, August 21). Prayagraj: Ganga, Yamuna water levels rising, flood alert issued
26. UN Office (2015). UN Office for Disaster Risk Reduction, 2015. Global Assessment Report on Disaster Risk Reduction 2015, 183–198. https://doi.org/10.18356/35eb6f07-en
27. Ward, P. J., Jongman, B., Weiland, F. S., Bouwman, A., van Beek, R., Bierkens, M. F. P., Ligtvoet, W., & Winsemius, H. C. (2015). Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 60(1), 37–58
28. Zeng, C., & Bertsimas, D. (2023). Global flood prediction: a multimodal machine learning approach. arXiv preprint arXiv:2301.12548.