Climate Adaptation-Aware Flood Prediction for Coastal Cities Using Deep Learning

2025-10-29

AI Summary

To address the high computational cost of physics-based models and the data scarcity and poor generalizability of deep learning approaches in coastal flood prediction under climate change, this paper proposes a lightweight, vision-driven, low-resource deep learning framework. The framework leverages multisource remote sensing and topographic data from multiple regions (Abu Dhabi and San Francisco) and employs a convolutional neural network to achieve high-accuracy flood depth mapping under few-shot learning conditions. It further integrates shoreline adaptation scenario modeling to enable rapid simulation across multiple sea-level rise (SLR) scenarios. Compared to state-of-the-art methods, the framework reduces mean absolute error by nearly 20%, significantly enhancing cross-regional generalizability and supporting urban-scale climate adaptation decision-making. It balances high predictive accuracy, strong scalability, and practical deployability for real-world applications.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Climate change and sea-level rise (SLR) pose escalating threats to coastal cities, intensifying the need for efficient and accurate methods to predict potential flood hazards. Traditional physics-based hydrodynamic simulators, although precise, are computationally expensive and impractical for city-scale coastal planning applications. Deep Learning (DL) techniques offer promising alternatives, however, they are often constrained by challenges such as data scarcity and high-dimensional output requirements. Leveraging a recently proposed vision-based, low-resource DL framework, we develop a novel, lightweight Convolutional Neural Network (CNN)-based model designed to predict coastal flooding under variable SLR projections and shoreline adaptation scenarios. Furthermore, we demonstrate the ability of the model to generalize across diverse geographical contexts by utilizing datasets from two distinct regions: Abu Dhabi and San Francisco. Our findings demonstrate that the proposed model significantly outperforms state-of-the-art methods, reducing the mean absolute error (MAE) in predicted flood depth maps on average by nearly 20%. These results highlight the potential of our approach to serve as a scalable and practical tool for coastal flood management, empowering decision-makers to develop effective mitigation strategies in response to the growing impacts of climate change. Project Page: https://caspiannet.github.io/

Problem

• Predicting coastal flooding under climate change scenarios
• Overcoming computational limitations of traditional flood simulators
• Generalizing deep learning models across diverse geographical regions

Innovation

• Lightweight CNN model predicts coastal flooding
• Generalizes across diverse geographical contexts
• Reduces flood depth prediction error by 20%