Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity

• URL: http://arxiv.org/abs/2510.22941v1
• Date: Mon, 27 Oct 2025 02:55:09 GMT
• Title: Hazard-Responsive Digital Twin for Climate-Driven Urban Resilience and Equity
• Abstract summary: We present a Hazard-Responsive Digital Twin (H-RDT) that combines physics-informed neural network modeling, multimodal data fusion, preemptive and equity-aware risk analytics for urban-scale response.<n>In a synthetic district with diverse building archetypes and populations, a simulated wildfire-outage-heatwave cascade shows that H-RDT maintains stable indoor temperature predictions.<n>interventions, such as cooling-center activation and microgrid sharing, reduce population-weighted thermal risk by 11 to 13 percent, shrink the 95th-percentile (tail) risk by 7 to 17 percent, and cut overheating hours by up to
• Score: 19.386626403817246
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Compounding climate hazards, such as wildfire-induced outages and urban heatwaves, challenge the stability and equity of cities. We present a Hazard-Responsive Digital Twin (H-RDT) that combines physics-informed neural network modeling, multimodal data fusion, and equity-aware risk analytics for urban-scale response. In a synthetic district with diverse building archetypes and populations, a simulated wildfire-outage-heatwave cascade shows that H-RDT maintains stable indoor temperature predictions (approximately 31 to 33 C) under partial sensor loss, reproducing outage-driven surges and recovery. The reinforcement learning based fusion module adaptively reweights IoT, UAV, and satellite inputs to sustain spatiotemporal coverage, while the equity-adjusted mapping isolates high-vulnerability clusters (schools, clinics, low-income housing). Prospective interventions, such as preemptive cooling-center activation and microgrid sharing, reduce population-weighted thermal risk by 11 to 13 percent, shrink the 95th-percentile (tail) risk by 7 to 17 percent, and cut overheating hours by up to 9 percent. Beyond the synthetic demonstration, the framework establishes a transferable foundation for real-city implementation, linking physical hazard modeling with social equity and decision intelligence. The H-RDT advances digital urban resilience toward adaptive, learning-based, and equity-centered decision support for climate adaptation.

Related papers

• Climate-resilient electric vehicle charging infrastructure for sustainable cities: An interpretable causal-ensemble framework for preventive maintenance and low-carbon mobility [4.19518803950435]
  We develop FGDSE, a feature-governed dynamic stacking ensemble that forms a decision-support system for climate-resilient charging-asset management.<n>It partitions heterogeneous signals into four feature families, assigns each to a domain expert whose inductive bias matches the data, and adds two deep temporal experts for short-term pulses and long-term degradation.<n>On 25 months of data from 13 stations, FGDSE surpasses twelve baselines beyond the ten-day horizon, sustains about 85% macro-recall at 30 days with an AUC decay of only 3.2 points, and reveals a shift of dominance from
  arXiv  Detail & Related papers  (2026-07-23T15:51:08Z)
• A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids [0.0]
  Hybrid CNN-LSTM IDS exploits CNN-based spatial feature extraction and LSTM-based temporal sequence modeling.<n>Model achieves a real-time inference throughput of 27,800 flows/s on GPU and 0.082 ms/sample CPU latency in FP32, with INT8 quantization providing an additional 3.1 x speedup at 0.3% accuracy loss.
  arXiv  Detail & Related papers  (2026-06-23T21:44:25Z)
• DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning [62.347535250646196]
  This paper proposes DeFRiS, a Decentralized Federated Reinforcement Learning framework for robust and scalable Silo-cooperative IoT application scheduling.<n>DeFRiS integrates three innovations: (i) an action-space-agnostic policy utilizing candidate resource scoring to enable seamless knowledge transfer across heterogeneous silos; (ii) a silo-optimized local learning mechanism combining Generalized Advantage Estimation with clipped policy updates to resolve sparse delayed reward challenges; and (iii) a Dual-Track Non-IID robust decentralized aggregation protocol leveraging gradient fingerprints for similarity-aware knowledge transfer and anomaly detection.
  arXiv  Detail & Related papers  (2026-03-16T02:02:38Z)
• When World Models Dream Wrong: Physical-Conditioned Adversarial Attacks against World Models [54.08784776767683]
  We present Physical-Conditioned World Model Attack (PhysCond-WMA), the first white-box world model attack that perturbs physical-condition channels.<n>PhysCond-WMA induces semantic, logic, or decision-level distortion while preserving perceptual fidelity.
  arXiv  Detail & Related papers  (2026-02-21T07:22:37Z)
• CASCADE: Cross-scale Advective Super-resolution with Climate Assimilation and Downscaling Evolution [51.56484100374058]
  CASCADE reconstructs fine structure by iteratively advecting coarse information along learned, flow-conditioned velocity warping fields.<n> evaluated on SEVIR radar data for 4x super-resolution of severe convective storms.<n> produces temporally coherent, physically consistent, and mass-conserving reconstructions well suited to advection-dominated extremes in atmospheric and oceanic applications.
  arXiv  Detail & Related papers  (2026-02-18T14:41:54Z)
• Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room [1.4323566945483497]
  wildfire frequency and intensity are projected to increase by approximately 14% by 2030 and 30% by 2050 due to global warming.<n> Conventional disaster management frameworks rely on static simulations and passive data acquisition.<n>We introduce the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin (DT) platform augmented by autonomous AI agents.
  arXiv  Detail & Related papers  (2026-02-09T17:44:52Z)
• Adaptive Learning in Spatial Agent-Based Models for Climate Risk Assessment: A Geospatial Framework with Evolutionary Economic Agents [0.0]
  We present a novel model that integrates climate hazard data with evolutionary learning for economic agents.<n>We demonstrate the framework using riverine flood projections under RCP8.5 until 2100.<n>Our results reveal systemic risks where even agents that are not directly exposed to floods face impacts through supply chain disruptions.
  arXiv  Detail & Related papers  (2025-09-23T04:33:58Z)
• Generative artificial intelligence improves projections of climate extremes [13.768093814009374]
  GCMs are essential for projecting future climate, yet their coarse resolution and high computational costs constrain their ability to represent extremes.<n>Here, we introduce FuXi-CMIPAlign, a generative deep learning framework for downscaling CMIP outputs.
  arXiv  Detail & Related papers  (2025-08-22T13:56:02Z)
• Enhancing Spatio-Temporal Forecasting with Spatial Neighbourhood Fusion:A Case Study on COVID-19 Mobility in Peru [2.6983054732688045]
  We leverage a large-scale dataset collected from Peru's national Digital Contact (DCT) application to forecast mobility flows across urban regions.<n>A key challenge lies in the spatial sparsity of hourly mobility counts across hexagonal grid cells.<n>We propose a lightweight and model-agnostic Neighbourhood Fusion (SPN) technique that augments each cell's features with aggregated signals from its immediate H3 neighbors.
  arXiv  Detail & Related papers  (2025-06-17T23:51:36Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [69.38041171537573]
  Water quality is foundational to environmental sustainability, ecosystem resilience, and public health.<n>Deep learning offers transformative potential for large-scale water quality prediction and scientific insights generation.<n>Their widespread adoption in high-stakes operational decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• Integrating Spatiotemporal Vision Transformer into Digital Twins for High-Resolution Heat Stress Forecasting in Campus Environments [11.14786537532112]
  Extreme heat events pose significant challenges to urban resilience and planning.<n>This study introduces a climate-responsive digital twin framework to enhance heat stress forecasting and decision-making.
  arXiv  Detail & Related papers  (2025-02-12T05:27:16Z)
• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  This paper introduces a novel framework for detecting instability in smart grids using only stable data.<n>It achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks.<n>Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• Regional climate risk assessment from climate models using probabilistic machine learning [12.737495484442443]
  GenFocal is a general-purpose, end-to-end generative framework for complex climate processes interacting at finetemporal scales.<n>It more accurately assesses extreme risk in the current climate than leading approaches.<n>GenFocal shows compelling results consistent with the literature on projecting climate impact on decadal timescales.
  arXiv  Detail & Related papers  (2024-12-11T03:52:17Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

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.