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

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.