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。

基于深度视觉的沿海洪水预测框架，应对气候变化及岸线适应

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

Satellite altimeter observations retrieved since 1993 show that the global
mean sea level is rising at an unprecedented rate (3.4mm/year). With almost
three decades of observations, we can now investigate the contributions of
anthropogenic climate-change signals such as greenhouse gases, aerosols, and
biomass burning in this rising sea level. We use machine learning (ML) to
investigate future patterns of sea level change. To understand the extent of
contributions from the climate-change signals, and to help in forecasting sea
level change in the future, we turn to climate model simulations. This work
presents a machine learning framework that exploits both satellite observations
and climate model simulations to generate sea level rise projections at a
2-degree resolution spatial grid, 30 years into the future. We train fully
connected neural networks (FCNNs) to predict altimeter values through a
non-linear fusion of the climate model hindcasts (for 1993-2019). The learned
FCNNs are then applied to future climate model projections to predict future
sea level patterns. We propose segmenting our spatial dataset into meaningful
clusters and show that clustering helps to improve predictions of our ML model.

自 1993 年以来，卫星测高仪观测表明全球平均海平面正以空前的速度上升（每年 3.4 毫米）。我们利用机器学习（ML）技术研究了海平面变化的未来模式，以了解气候变化信号如温室气体、气溶胶和生物质燃烧在海平面上升中的贡献，并用于未来海平面变化的预测。我们提出了一种利用卫星观测和气候模型模拟相结合的机器学习框架，以 30 年为期限进行 2 度分辨率的海平面上升预测。我们通过训练全连接神经网络（FCNNs）进行非线性融合，预测测高仪数值（1993-2019 年）。然后将学习到的 FCNNs 应用于未来的气候模型预测，以预测未来的海平面模式。我们提出将空间数据集分段成有意义的聚类，并显示聚类有助于改进我们的机器学习模型的预测能力。

利用海平面高度和气候模型集合进行机器学习的海平面预测

Sea level Projections with Machine Learning using Altimetry and Climate  Model ensembles

Contemporary sea level rise (SLR) research seldom considers enabling
effective geovisualisation for the communities. This lack of knowledge transfer
impedes raising awareness on climate change and its impacts. The goal of this
study is to produce an online SLR map accessible to the public that allows them
to interact with evolving high-resolution geospatial data and techniques. The
study area was the North Shore of Vancouver, British Columbia, Canada. While
typically coarser resolution (10m+/pixel) Digital Elevation Models have been
used by previous studies, we explored an open access airborne 1 metre LiDAR
which has a higher resolution and vertical accuracy and can penetrate tree
cover at a higher degree than most satellite imagery. A bathtub method model
with hydrologic connectivity was used to delineate the inundation zones for
various SLR scenarios which allows for a not overly complex model and process
using standard tools such as ArcGIS and QGIS with similar levels of accuracy as
more complex models, especially with the high-resolution data. Deep Learning
and 3D visualizations were used to create past, present, and modelled future
Land Use/Land Cover and 3D flyovers. Analysis of the possible impacts of 1m,
2m, 3m, and 4m SLR over the unique coastline, terrain and land use was
detailed. The generated interactive online map helps local communities
visualise and understand the future of their coastlines. We have provided a
detailed methodology and the methods and results are easily reproducible for
other regions. Such initiatives can help popularise community-focused
geovisualisation to raise awareness about SLR.

该研究使用 1 米 LiDAR 探测数据构建了一个在线的海平面上升地图，采用一种浴缸模型法来推断不同海平面上升情景下的淹没区域，并利用数据挖掘和 3D 可视化技术展示了不同情景下的土地利用、地形和海岸线的变化，有助于提高人们对海平面上升的认识。

构建温哥华北岸海平面上升互动在线地图：为沿海社区实现地理可视化提供方法和洞见

Generating an interactive online map of future sea level rise along the  North Shore of Vancouver: methods and insights on enabling geovisualisation  for coastal communities