Flood Risk Assessment and Management

Últimos artículos

• C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories

  Yanjie Tong, Phillip Si, Yuan Qiu, Peng Chen · 1 de octubre de 2026

  Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time measurements come from only a handful of stream gauges, and high-resolution hydrodynamic models are too costly to rerun e…

• Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale

  Hong Zhang, John K. Hutchison, Rao Kotamarthi, Jeremy Feinstein, Haiwen Guan, Romit Maulik, Ross M. Alexander, Vijay P. Ramalingam, Jason Stock, Thomas Wall · 30 de septiembre de 2026

  Accurate flood forecasts several days in advance are essential for flood control, water-resource management, and emergency response. A central challenge is to produce high-resolution forecasts across continental domains where hydrological behavior varies widely from place to place. We developed Hapi…

• Cross-Architecture Foundation-Model Distillation for Edge Flood Segmentation

  Fabian Schmalstieg, Karsten Mueller, Wojciech Samek · 18 de septiembre de 2026

  Geospatial foundation models can provide strong flood-segmentation performance, but their size limits deployment on memory-constrained edge hardware. We distill a 300-million-parameter Prithvi-EO-2.0 teacher, fine-tuned on the 252 manually labeled Sen1Floods11 training scenes, into a 0.7-million-par…

• A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph

  Ruben Cartuyvels, Karim Douch, Gabriele Bertoli, Mounia El Baz, Artemis Vrettou, S\'ebastien Lef\`evre, Diego Fernandez Prieto · 11 de septiembre de 2026

  Continuous monitoring of water surface elevation across river networks is critical for flood forecasting, water resource management, and understanding the global water cycle. Yet, the scarcity of in situ gauges across much of the globe constrains the development of reliable modeling frameworks. Sate…

• Riverbank Erosion Analysis in Bangladesh Using Spatiotemporal Segmentation

  M. Saifuzzaman Rafat, Akif Islam, Mohd Ruhul Ameen, Momen Khandoker Ope, Abu Saleh Musa Miah, Jungpil Shin · 31 de agosto de 2026

  Riverbank erosion is a serious environmental problem in Bangladesh, causing land loss, damage to infrastructure, and displacement of local communities. Manual analysis of satellite images is often slow and difficult to apply consistently across large river networks. This study uses a parameter-effic…

• Characterizing Agentic Flooding of Government Services

  Chris Schmitz, Lewis Hammond, Alan Chan · 18 de agosto de 2026

  AI agents are making it easier for the public to interact with government, such as by helping them apply for benefits, understand complex policies, and make their opinions heard. Although improving service accessibility is beneficial, any resulting surges in demand could strain unprepared government…

• Mechanistic Interpretability-Guided Selective Fine-Tuning of Vision-Language Models for Centimeter-Level Flood Depth Estimation

  Nafis Fuad, Xiaodong Qian, Dongxiao Zhu · 11 de agosto de 2026

  Urban flooding poses an escalating threat to transportation infrastructure, yet no operational system provides real-time, street-level flood-depth estimates at centimeter resolution. This paper presents three vision-language models fine-tuned for continuous flood-depth estimation from street-level i…

• FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis

  Zhang Weihui, Wang Ruizhi, Xu Hongye, Wang Huiqiong, Sun Li, Song Mingli · 5 de agosto de 2026

  Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation. Although generative models provide a promising means of data augmentation, existing methods often yield implausible spatial layouts of flooded re…

• Adaptive Sampling for Automated Post-Disaster Rapid Damage Assessment via Level-Set Cost-Aware Bayesian Optimization

  Boyang Xu, Mostafa Reisi Gahrooei, Mohammad Ilbeigi, Hao Yan · 5 de agosto de 2026

  Natural disasters frequently inflict severe damage to the built environment, which demands a rapid, reliable, and cost-effective damage assessment for emergency response. However, traditional methods for post-disaster damage assessment often rely on static, labor-intensive data collection strategies…

• GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation

  Gaetano Chiriaco, Luca Barco, Andrea Bragagnolo, Claudio Rossi, Edoardo Arnaudo · 4 de agosto de 2026

  Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal benchmarks that measure how well such models extract value from data. Concerning flood mapping, existing datasets rarely…

• Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems

  Liangjun You, Min Wu, Orlando Woods, Dongsheng Luo · 4 de agosto de 2026

  Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations. Current forecasting models can capture temporal dependencies with low average errors, but global error metrics may conceal poor reproducti…

• Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis

  Ilya Novikov, Svetlana Illarionova, Ruslan Dzharkinov, Maria Smirnova, Ayrat Abdullin, Anna Korotkova, Mariia Ulianova, Dmitrii Shadrin, Evgeny Burnaev · 31 de julio de 2026

  Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing across extensive and environmentally diverse regions remains challenging, as most segmentation algorithms lack the generalisation capacity required for …

• Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibility

  Luc DCosta, Yidi Wang, Jonathan L. Goodall, Rohan Chandra · 29 de julio de 2026

  Deep learning surrogate models trained with mean-squared-error loss produce statistically accurate but physically unconstrained flood predictions: water may flow uphill, appear spontaneously, or smooth over street-level corridors. We develop a physics-informed training framework for CNN-LSTM models …

• HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows

  Qingyi Yang, Siqian Qiu, Bing Li, Xu Shan, Jia Feng, Shunan Zhou, Xudong Zhou, Tiantian Xing, Jiale Guo, Xiaoyi Dong, Gaoyu Liu, Xiaohuan Liu, Haiqing Pu, Qingwen Deng, Xun Zhang, Zhongrun Xiang, Haiyang Qian, Ying Yan, Yongkang Xu, Nuo Lei, Tianlong Jia, Baoying Shan, Carlo De Michele · 28 de julio de 2026

  Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer. Although artificial intelligence methods have advanced flood prediction and model-error correction, most existing studies have not explicitly represented the tacit expert rules, r…

• Context-Aware Concept Distillation for Trustworthy Flood Prediction

  Eli Levinkopf, Efrat Morin, Claudia V. Goldman · 28 de julio de 2026

  Effective flood risk management relies on accurate forecasting, yet the "black box" nature of stateof-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to …

• Physics-aware Masked Diffusion-based Flood Simulation for Urban Fisheye Disaster Detection

  Sodtavilan Odonchimed, Tsogt Enkhbayar, Oyunzul Munkhtamga, Munkhjargal Gochoo · 20 de julio de 2026

  Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and the development of anomaly detection models. However, the severe shortage of flood data in real-world environments, combined with the inherent distortio…

• DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings

  Yuya Kawakami, Daniel Cayan, Dongyu Liu, Kwan-Liu Ma, Tom Corringham · 20 de julio de 2026

  Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive proces…

• On the Disagreement in Perturbation-based xAI -- Benchmarking Perturbation Choices for Flood Detection from SAR Images

  Anastasia Schlegel, Ronny Hänsch · 17 de julio de 2026

  Perturbation-based xAI methods are widely used to analyze the behavior and predictions of deep learning models. By altering input regions and measuring the resulting changes in class probabilities with respect to the original image, they assign relevance scores and generate heatmaps that reflect eac…

• HaorFloodAlert: A 72-Hour Machine Learning Early Warning System for Flash Floods in Bangladesh's Haor Wetlands

  Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely, Md. Samiul Alim, Md. Zakir Hossen · 7 de julio de 2026

  Every spring, flash floods strike the haor wetlands of northeast Bangladesh just before the boro rice harvest, and one flood can erase a family's entire crop in days. Warning people in time is hard here for a structural reason: the Sunamganj Haor is a flat, bowl-shaped basin that fills at once from …

• HaorFloodAlert: A 72-Hour Machine Learning Early Warning System for Flash Floods in Bangladesh's Haor Wetlands

  Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely, Md. Samiul Alim, Md. Zakir Hossen · 7 de julio de 2026

  Every spring, flash floods strike the haor wetlands of northeast Bangladesh just before the boro rice harvest, and one flood can erase a family's entire crop in days. Warning people in time is hard here for a structural reason: the Sunamganj Haor is a flat, bowl-shaped basin that fills at once from …

• Spatial Support Matters: Geometry-Aware Graph Fusion for Rainfall Field Reconstruction

  Low Jun Yu, Niramay Kachhadiya, Herath Mudiyanselage Viraj Vidura Herath, Sanka Rasnayaka, Lucy Amanda Marshall · 3 de julio de 2026

  Fine-scale rainfall reconstruction is critical for urban flood modeling, but real rainfall sensing systems observe the field through incompatible spatial supports: gauges measure points, microwave links measure paths, and radar/satellite products measure gridded areas. These differences in measureme…

• High-Resolution Flood Mapping With Sentinel-1 and Sentinel-2 via Misalignment-Robust Cross-Sensor Learning and Generative Despeckling

  David Ma, Jeremy Feinstein, Shreya Pandit, Arkaprabha Ganguli, Eugene Yan · 30 de junio de 2026

  Reliable high-resolution flood extent mapping from satellite imagery remains constrained by limited data fidelity and sensor-specific artifacts. Multispectral optical imagery is degraded by clouds, shadows, and urban confounders, while synthetic aperture radar (SAR) imagery is affected by speckle no…

• Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones

  Sanjay Thasma, Yu-Hsuan Ho, Ali Mostafavi · 30 de junio de 2026

  Rapid flood mapping is critical for emergency response, yet optical imagery is often unusable during major flooding and single-temporal SAR is ambiguous, since new inundation, permanent water, and other smooth surfaces produce similar backscatter. This study evaluates whether stable land-context pri…

• Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery

  Sophia Li, Max Zhao, Raghu G. Raj, Tianyu Chen · 26 de junio de 2026

  Floods frequently impact regions around the world. Rapid and accurate flood detection is crucial for emergency response and timely mitigation of human and economic loss. The expanding availability of satellite data and advances in artificial intelligence have enhanced monitoring of environmental haz…

• Flood Mapping from RGB imagery using a Vision Foundation Model

  Vladyslav Polushko, Tilman Bucher, Ronald Rösch, Thomas März, Markus Rauhut, Andreas Weinmann · 24 de junio de 2026

  Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost. To produce flood maps, deep learning models for water segmentation are often used…