Urban flood risk emerges from complex and nonlinear interactions among
multiple features related to flood hazard, flood exposure, and social and
physical vulnerabilities, along with the complex spatial flood dependence
relationships. Existing approaches for characterizing urban flood risk,
however, are primarily based on flood plain maps, focusing on a limited number
of features, primarily hazard and exposure features, without consideration of
feature interactions or the dependence relationships among spatial areas. To
address this gap, this study presents an integrated urban flood-risk rating
model based on a novel unsupervised graph deep learning model (called
FloodRisk-Net). FloodRisk-Net is capable of capturing spatial dependence among
areas and complex and nonlinear interactions among flood hazards and urban
features for specifying emergent flood risk. Using data from multiple
metropolitan statistical areas (MSAs) in the United States, the model
characterizes their flood risk into six distinct city-specific levels. The
model is interpretable and enables feature analysis of areas within each
flood-risk level, allowing for the identification of the three archetypes
shaping the highest flood risk within each MSA. Flood risk is found to be
spatially distributed in a hierarchical structure within each MSA, where the
core city disproportionately bears the highest flood risk. Multiple cities are
found to have high overall flood-risk levels and low spatial inequality,
indicating limited options for balancing urban development and flood-risk
reduction. Relevant flood-risk reduction strategies are discussed considering
ways that the highest flood risk and uneven spatial distribution of flood risk
are formed.

基于一种新型无监督图深度学习模型（称为 FloodRisk-Net），本研究提出了一个集成的城市洪水风险评级模型，能够捕捉区域间的空间依赖关系和洪水危险和城市特征之间的复杂非线性相互作用，以确定突发洪水风险。利用美国多个都市统计区（MSAs）的数据，该模型将风险评级分为六个不同的城市特定级别，通过对每个 MSA 中的区域进行特征分析，可识别出塑造最高洪水风险的三种典型形态。洪水风险在每个 MSA 内以分层结构的空间分布，其中核心城市承担了最高的洪水风险。发现多个城市存在高整体洪水风险水平和低空间不平等，说明在城市发展和洪水风险减少之间的平衡选择有限。讨论了相关的洪水风险减少策略，考虑到最高洪水风险和洪水风险的空间分布不均等的形成方式。

无监督图深度学习揭示城市区域出现的洪水风险特征

Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of  Urban Areas

Origin-destination (OD) flow, which contains valuable population mobility
information including direction and volume, is critical in many urban
applications, such as urban planning, transportation management, etc. However,
OD data is not always easy to access due to high costs or privacy concerns.
Therefore, we must consider generating OD through mathematical models. Existing
works utilize physics laws or machine learning (ML) models to build the
association between urban structures and OD flows while these two kinds of
methods suffer from the limitation of over-simplicity and poor generalization
ability, respectively. In this paper, we propose to adopt physics-informed ML
paradigm, which couple the physics scientific knowledge and data-driven ML
methods, to construct a model named Origin-Destination Generation Networks
(ODGN) for better population mobility modeling by leveraging the complementary
strengths of combining physics and ML methods. Specifically, we first build a
Multi-view Graph Attention Networks (MGAT) to capture the urban features of
every region and then use a gravity-guided predictor to obtain OD flow between
every two regions. Furthermore, we use a conditional GAN training strategy and
design a sequence-based discriminator to consider the overall topological
features of OD as a network. Extensive experiments on real-world datasets have
been done to demonstrate the superiority of our proposed method compared with
baselines.

本文提出了一种名为 “ODGN” 的新型数学建模方法，它采用物理学与机器学习的结合，包括 Multi-view 图形注意网络和 gravity-guided 预测器等技术，用于更好地建模人口流动分布，并通过真实数据的实验验证了其与基准算法相比的优越性。