Deep Learning Approaches for Flood Forecasting and Early Warning Systems in River Basins

Authors

  • Dr. Firoj Ahamad Assistant Professor, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India Author
  • Dr Vineet Kumar Singh Assistant Professor, Department of IT, IET Dr RMLAU, Ayodhya, Uttar Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT25113354

Keywords:

Deep Learning, Flood Forecasting, Early Warning Systems, River Basins, Hydrology

Abstract

Floods are among the most devastating natural disasters globally, causing significant loss of life, widespread property damage, and severe economic disruption. River basins, with their intricate hydrological dynamics, are particularly vulnerable to these events, necessitating robust and timely flood forecasting and early warning systems (FEWS). Traditional hydrological models often struggle with the complexity and non-linearity of hydrological processes, especially under changing climatic conditions. The advent of deep learning (DL) has revolutionized various fields, and its application in flood forecasting has shown remarkable promise. This review paper comprehensively examines the state-of-the-art deep learning approaches for flood forecasting and early warning in river basins. It delves into the diverse architectures, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs), and their hybrid combinations, that have been successfully employed. The paper also highlights the advantages of DL, such as its ability to learn complex patterns from large datasets, handle multivariate inputs, and provide accurate predictions with varying lead times. Furthermore, it discusses the challenges associated with implementing DL-based FEWS, including data scarcity, model interpretability, and computational demands. The aim is to provide a holistic overview of the current landscape, identify existing research gaps, and propose future directions for advancing flood preparedness and mitigation strategies through intelligent deep learning solutions.

Downloads

Download data is not yet available.

References

L. S. Singh, K. N. Singh, N. Hoque, and K. R. Singh, “An Integrated Machine Learning and Deep Learning Framework for River-Based Flood Early Warning System,” IDRiM Journal, vol. 15, no. 1, 2025.

Y. Zhao et al., “A deep learning-based probabilistic approach to flash flood warnings in mountainous catchments,” Journal of Hydrology, vol. 652, p. 132677, 2025.

L.-C. Chang, M.-T. Yang, and F.-J. Chang, “Flood resilience through hybrid deep learning: Advanced forecasting for Taipei’s urban drainage system,” Journal of Environmental Management, vol. 379, p. 124835, 2025.

S. Nezhadbasaidu et al., “A flood expert system using machine learning and IoT: warning, detection, and prediction,” International Journal of System Assurance Engineering and Management, pp. 1–17, 2025.

W. Almikaeel, A. Šoltész, L. Čubanová, and D. Baroková, “Hydro-informer: A deep learning model for accurate water level and flood predictions,” Natural Hazards, vol. 121, no. 4, pp. 3959–3979, 2025.

F. Kordi-Karimabadi, E. Fadaei-Kermani, M. Ghaeini-Hessaroeyeh, and H. Farhadi, “Integrating numerical models with deep learning techniques for flood risk assessment,” Scientific Reports, vol. 15, no. 1, p. 8913, 2025.

M. Asif, M. M. Kuglitsch, I. Pelivan, and R. Albano, “Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting,” Water Resources Management, pp. 1–21, 2025.

H. Eom, Y. Kim, and J. Paik, “Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application,” Mathematics, vol. 13, no. 11, p. 1696, 2025.

X. Zhou, X. Huang, X. Jiang, and J. Jiang, “Real-time error correction of multiple-hour-ahead flash flood forecasting based on the sliding runoff-rain data and deep learning models,” Journal of Hydrology, vol. 655, p. 132918, 2025.

W. Duangkhwan, C. Ekkawatpanit, D. Kositgittiwong, W. Kompor, and C. Petpongpan, “DEEP LEARNING-BASED FLOOD INUNDATION PREDICTION IN THE PATTANI RIVER BASIN,” GEOMATE Journal, vol. 28, no. 125, pp. 133–140, 2025.

K. V. N. S. Perera, D. D. A. Arthanayake, and M. W. P. Maduranga, “Machine Learning Approach to Modeling Rainfall and River Overflow Trends in Nilwala River Basin, Sri Lanka,” in 2025 5th International Conference on Advanced Research in Computing (ICARC), 2025, pp. 1–6.

J. Karthiyayini, A. Jain, K. S. Prasad, T. A. A. U. Abedi, Y. Prasanna, and K. Murugesan, “Flood Prediction and Adaptive Farming Solutions Using IoT, Machine Learning, and Remote Sensing for Climate-Resilient Agriculture,” in 2025 International Conference on Intelligent Control, Computing and Communications (IC3), 2025, pp. 777–782.

Z. Huang, S. Liu, C. Tu, and H. Zhou, “Research on Flood Forecasting in the Pa River Basin Based on the Xin’anjiang Model,” Water, vol. 17, no. 8, p. 1154, 2025.

H. Solanki, U. Vegad, A. Kushwaha, and V. Mishra, “Improving streamflow prediction using multiple hydrological models and machine learning methods,” Water Resources Research, vol. 61, no. 1, p. e2024WR038192, 2025.

I. F. Ridwan, “Flood Early Warning Monitoring System Using KNN Methods In Bangkalan District,” Journal of Computation Physics and Earth Science (JoCPES), vol. 5, no. 1, 2025.

Y. Zhao et al., “A deep learning-based probabilistic approach to flash flood warnings in mountainous catchments,” Journal of Hydrology, vol. 652, p. 132677, 2025.

S. Nezhadbasaidu et al., “A flood expert system using machine learning and IoT: warning, detection, and prediction,” International Journal of System Assurance Engineering and Management, pp. 1–17, 2025.

Y. Hahn, P. Kienitz, M. Wönkhaus, R. Meyes, and T. Meisen, “Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data,” Water, vol. 16, no. 23, p. 3368, 2024.

N. Byaruhanga, D. Kibirige, S. Gokool, and G. Mkhonta, “Evolution of flood prediction and forecasting models for flood early warning systems: A scoping review,” Water, vol. 16, no. 13, p. 1763, 2024.

K.-H. Chang, Y.-T. Chiu, W.-R. Su, Y.-C. Yu, and C.-H. Chang, “A spatial–temporal deep learning-based warning system against flooding hazards with an empirical study in Taiwan,” International Journal of Disaster Risk Reduction, vol. 102, p. 104263, 2024.

Downloads

Published

03-06-2025

Issue

Section

Research Articles