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

TL;DR

This paper introduces a lightweight deep learning model for accurate, scalable coastal flood prediction under climate change scenarios, outperforming existing methods and generalizing across different regions.

Contribution

The study develops a novel CNN-based flood prediction model leveraging a low-resource vision framework, capable of handling diverse geographical data and variable sea-level rise scenarios.

Findings

Model reduces flood depth prediction MAE by nearly 20%.

Demonstrates effective generalization across Abu Dhabi and San Francisco.

Outperforms state-of-the-art flood prediction methods.

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/