Coastal Flood Prediction

Coastal flood prediction aims to accurately forecast the extent and severity of coastal inundation, crucial for mitigating the increasing risks posed by climate change and sea-level rise. Current research heavily emphasizes data-driven approaches, employing deep learning architectures like convolutional neural networks and transformers, often coupled with techniques like adversarial domain adaptation to address data scarcity in specific locations. These models are being compared against traditional physics-based methods and machine learning techniques, with a focus on improving prediction accuracy and efficiency, particularly in resource-constrained settings. Improved prediction capabilities are vital for informing effective coastal management strategies and disaster preparedness.

Papers

June 6, 2024

Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
Areg Karapetyan, Aaron Chung Hin Chow, Samer Madanat
Vision Based Flood Inundation Mapping Climate Change Impact Coastal Flood Prediction

May 30, 2024

The Merit of River Network Topology for Neural Flood Forecasting
Nikolas Kirschstein, Yixuan Sun
High Performance River Network Coastal Flood Prediction

June 16, 2022

A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting
Delong Chen, Ruizhi Zhou, Yanling Pan, Fan Liu
Domain Adversarial Flood Prediction Domain Encoders Coastal Flood Prediction Flood Adaptation

Coastal Flood Prediction

Papers

Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations

The Merit of River Network Topology for Neural Flood Forecasting

A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting