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

• URL: http://arxiv.org/abs/2309.14610v3
• Date: Mon, 13 Nov 2023 19:24:26 GMT
• Title: Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas
• Authors: Kai Yin, Ali Mostafavi
• Abstract summary: This study presents an integrated urban flood-risk rating model based on a novel unsupervised graph deep learning model (FloodRisk-Net) Flood risk is found to be spatially distributed in a hierarchical structure within each metropolitan statistical area (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.
• Score: 4.295013129588405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Capturing Unseen Spatial Extremes Through Knowledge-Informed Generative Modeling [9.672435929179073]
  We develop DeepX-GAN to better capture the spatial structure of rare extremes.<n>We define two types of unseen extremes: "checkmate" extremes that directly hit targets, and "stalemate" extremes that narrowly miss.<n>We find that these unseen extremes disproportionately affect regions with high vulnerability and low socioeconomic readiness.
  arXiv  Detail & Related papers  (2025-07-12T09:06:45Z)
• Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection [9.264147242217364]
  Street scene datasets offer a promising means of detecting urban objects and incidents like street flooding. A major challenge in using these datasets is their lack of reliable labels. Here, we propose BayFlood, a two-stage approach which circumvents this difficulty.
  arXiv  Detail & Related papers  (2025-03-18T21:53:37Z)
• StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting [85.67616000086232]
  StreetSurfGS is first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction. StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability. To address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information.
  arXiv  Detail & Related papers  (2024-10-06T04:21:59Z)
• Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity [18.566139471849844]
  Traffic accidents pose a significant risk to human health and property safety. To prevent traffic accidents, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents.
  arXiv  Detail & Related papers  (2024-07-29T03:10:15Z)
• UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping [24.857739769719778]
  UrbanSARFloods is a dataset featuring pre-processed Sentinel-1 intensity data and interferometric coherence imagery acquired before and during flood events. It contains 8,879 $512times 512$ chips covering 807,500 $km2$ across 20 land cover classes and 5, spanning 18 flood events. We used UrbanSARFloods to benchmark existing state-of-the-art convolutional neural networks (CNNs) for segmenting open and urban flood areas.
  arXiv  Detail & Related papers  (2024-06-06T14:28:43Z)
• Rethinking Urban Flood Risk Assessment By Adapting Health Domain Perspective [4.096028590445942]
  Inspired by ideas from health risk assessment, this paper presents a new perspective for flood risk assessment. The proposed perspective focuses on three pillars for examining flood risk: (1) inherent susceptibility, (2) mitigation strategies, and (3) external stressors.
  arXiv  Detail & Related papers  (2024-03-06T19:12:41Z)
• SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data [55.609946936979036]
  We propose a novel spatial event ranking approach named SpatialRank. We show that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents.
  arXiv  Detail & Related papers  (2023-09-30T06:20:21Z)
• Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks [142.67349734180445]
  Existing algorithms that provide risk-awareness to deep neural networks are complex and ad-hoc. Here we present capsa, a framework for extending models with risk-awareness.
  arXiv  Detail & Related papers  (2023-08-01T02:07:47Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Pi theorem formulation of flood mapping [0.0]
  We propose a framework to improve machine learning (ML) model generalization based on dimensionless, multi-scale features. Dimensionless features outperformed dimensional features, with some of the largest gains occurring when the model was trained in one region and tested in another.
  arXiv  Detail & Related papers  (2022-11-01T17:57:38Z)
• Spatial-Temporal Sequential Hypergraph Network for Crime Prediction [56.41899180029119]
  We propose Spatial-Temporal Sequential Hypergraph Network (ST-SHN) to collectively encode complex crime spatial-temporal patterns. In particular, to handle spatial-temporal dynamics under the long-range and global context, we design a graph-structured message passing architecture. We conduct extensive experiments on two real-world datasets, showing that our proposed ST-SHN framework can significantly improve the prediction performance.
  arXiv  Detail & Related papers  (2022-01-07T12:46:50Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• A Network Percolation-based Contagion Model of Flood Propagation and Recession in Urban Road Networks [2.5876546798940616]
  A network of urban roads resilient to flooding events is essential for provision of public services and for emergency response. This study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of flood waters in urban road networks. The application of the proposed model was verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017.
  arXiv  Detail & Related papers  (2020-04-07T17:25:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.