In light of growing threats posed by climate change in general and sea level
rise (SLR) in particular, the necessity for computationally efficient means to
estimate and analyze potential coastal flood hazards has become increasingly
pressing. Data-driven supervised learning methods serve as promising candidates
that can dramatically expedite the process, thereby eliminating the
computational bottleneck associated with traditional physics-based hydrodynamic
simulators. Yet, the development of accurate and reliable coastal flood
prediction models, especially those based on Deep Learning (DL) techniques, has
been plagued with two major issues: (1) the scarcity of training data and (2)
the high-dimensional output required for detailed inundation mapping. To remove
this barrier, we present a systematic framework for training high-fidelity Deep
Vision-based coastal flood prediction models in low-data settings. We test the
proposed workflow on different existing vision models, including a fully
transformer-based architecture and a Convolutional Neural Network (CNN) with
additive attention gates. Additionally, we introduce a deep CNN architecture
tailored specifically to the coastal flood prediction problem at hand. The
model was designed with a particular focus on its compactness so as to cater to
resource-constrained scenarios and accessibility aspects. The performance of
the developed DL models is validated against commonly adopted geostatistical
regression methods and traditional Machine Learning (ML) approaches,
demonstrating substantial improvement in prediction quality. Lastly, we round
up the contributions by providing a meticulously curated dataset of synthetic
flood inundation maps of Abu Dhabi's coast produced with a physics-based
hydrodynamic simulator, which can serve as a benchmark for evaluating future
coastal flood prediction models.

在追求计算效率的过程中，提出了训练高保真、基于深度学习的沿海洪水预测模型的系统框架，通过与地质统计回归方法和传统机器学习方法进行性能验证，表明其对预测质量的显著提升，并提供了阿布扎比海岸的综合数据集，用于评估未来的沿海洪水预测模型。