Sustainable Marine Structures

Machine Learning Models for Early Warning of Coastal Flooding and Storm Surges

Dr. Anant Kurhade

DYPIOT

Ranjana Gore

Department of Computer Science Engineering, MIT Art Design & Technology University, Loni Kalbhor Pune, Maharashtra 412201, India

Dipa Dattatray Dharmadhikari

Emerging Science and Technology Department, Maharashtra Institute of Technology, Chhatrapati Sambhajinagar Aurangabad, Maharashtra 431010, India

Jyoti Deone

Department of Information Technology, D.Y. Patil Deemed to be University RAIT, Navi Mumbai, Maharashtra 400706, India

Shwetal Kishor Patil

Department of Computer Engineering, Marathwada Mitramandal’s Institute of Technology, Lohgaon Pune, Maharashtra 411047, India

Swapnil S. Chaudhari

Department of Computer Engineering, Marathwada Mitramandal’s Institute of Technology, Lohgaon Pune, Maharashtra 411047, India

Aarti Puri

Department of First Year Engineering (Engineering Chemistry), Dr. D. Y. Patil Institute of Technology, Pimpri Pune, Maharashtra 411018, India

Shital Yashwant Waware

Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri Pune, Maharashtra 411018, India; School of Technology and Research, Dr. D. Y. Patil Dnyan Prasad University, Pimpri Pune, Maharashtra 411018, India

DOI: https://doi.org/10.36956/sms.v7i3.2438

Received: 9 July 2025; Published: 7 August 2025

Abstract

Floods and storm surges pose significant threats to coastal regions worldwide, demanding timely and accurate early warning systems (EWS) for disaster preparedness. Traditional numerical and statistical methods often fall short in capturing complex, nonlinear, and real-time environmental dynamics. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as promising alternatives for enhancing the accuracy, speed, and scalability of EWS. This review critically evaluates the evolution of ML models—such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—in coastal flood prediction, highlighting their architectures, data requirements, performance metrics, and implementation challenges. A unique contribution of this work is the synthesis of real-time deployment challenges including latency, edge-cloud tradeoffs, and policy-level integration, areas often overlooked in prior literature. Furthermore, the review presents a comparative framework of model performance across different geographic and hydrologic settings, offering actionable insights for researchers and practitioners. Limitations of current AI-driven models, such as interpretability, data scarcity, and generalization across regions, are discussed in detail. Finally, the paper outlines future research directions including hybrid modelling, transfer learning, explainable AI, and policy-aware alert systems. By bridging technical performance and operational feasibility, this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.

Keywords: Coastal Flood Forecasting, Deep Learning Algorithms, Early Warning Systems (EWS), Machine Learning Models, Real-Time Flood Monitoring, Storm Surge Prediction

References