[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121098-en":3,"doc-seo-121098-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121098,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","FLOODDAMAGECAST: BUILDING FLOOD DAMAGE NOWCASTING WITH MACHINE LEARNING AND DATA AUGMENTATION","FloodDamageCast delivers near-real-time estimates of residential flood damage to support emergency responders during disaster response and recovery. The machine learning framework combines heterogeneous hydrological, topographic, and built-environment features to predict damage at 500 m by 500 m resolution for Harris County, Texas, using data from Hurricane Harvey 2017. To address severe class imbalance, the approach applies a GAN-based data augmentation strategy with an efficient learning model. Results highlight high-damage areas that baseline methods can miss, improving prioritization of repairs and inspection planning.","arXiv:2405.14232v2[cs.LG]24 May 2024  \n# FLOODDAMAGECAST:BUILDING FLOOD DAMAGE NOWCASTING WITHMACHINE LEARNING AND DATA AUGMENTATION\n\nA PREPRINT  \nChia-Fu LiuLipai Huang  \nDepartment of Civil and Environmental Engineering Department of Civil and Environmental EngineeringTexas A&M UniversityTexas A&M UniversityCollege Station,TX 77843-3136College Station,TX77843-3136joeyliu0324@tamu.edulipai.huang@tamu.edu  \nKai Yin  \nSam Brody  \nDepartment of Civil and Environmental EngineeringDepartment of Marine and Coastal ScienceTexas A&M UniversityTexas A&M University at GalvestonCollege Station,TX 77843-3136Galveston,TX 77554-1675kai_yin@tamu.edubrodys@tamu.edu  \nAli Mostafavi  \nDepartment of Civil and Environmental EngineeringTexas A&M UniversityCollege Station,TX 77843-3136amostafavi@civil.tamu.edu  \n## Abstract\n\nNear-real time estimation of damage to buildings and infrastructure,referred to as damage nowcasting inthis study,is crucial for empowering emergency responders to make informed decisions regardingevacuation orders and infrastructure repair priorities during disaster response and recovery.Here,weintroduce FloodDamageCast,a machine learning(ML)framework tailored for property flood damagenowcasting.The framework leverages heterogeneous data to predict residential flood damage at a resolutionof500 meters by 500 meters within Harris County,Texas,during the 2017 Hurricane Harvey.To deal withdata imbalance,FloodDamageCast incorporates a generative adversarial networks-based data augmentationcoupled with an efficient machine learning model.The results demonstrate the framework's ability toidentify high-damage spatial areas that would be overlooked by baseline models.Insights gleaned fromflood damage nowcasting can assist emergency responders to more efficiently identify repair needs,allocateresources,and streamline on-the-ground inspections,thereby saving both time and effort.  \nKeywords:Flood damage nowcasting·Data augmentation·Generative adversarial network·Lightgradient-boosting machine·Imbalance learning  \n## 1 Introduction\n\nFlood hazards wreak havoc on urban areas,resulting in both physical destruction and loss of life indensely populated regions.In the United States alone,annual insurance claims have hovered around $1billion per year over the past four decades [1].This financial burden is expected to persist and potentiallyworsen due to the escalating frequency and intensity of flood events resulting from climate change [2,3].Rapid damage assessment of flooded areas is essential for swift response and recovery of affectedcommunities.Emergency responders and public officials rely primarily on visual inspection to evaluateflood damage,incurring significantly delaying the recovery process.This limitation is due mainly to(1)  \n1  \ninadequately trained personnel conducting field inspections in the aftermath of flood events,and(2)limited flood data analytics capability available to emergency managers and public officials.  \nExpediting the flood damage assessment process is instrumental to accelerating post-disaster recoveryefforts and bolstering community resilience against flood hazards,Currently,the main approach forestimating flood damage is based on specifying inundation depths then utilizing historical flood depthdamage curves [4,5].The applicability of this approach for flood damage nowcasting,however,would belimited due to significant computation effort needed to model inundation depths using hydrologicalmodels based on the principles of hydrodynamics [6,7,8,9].Recent advancements in computing powerand algorithms have rendered machine learning(ML)techniques increasingly prevalent and valuableacross various flood risk analysis applications [10,11,12].Given their versatility and adaptability,MLalgorithms excel in integrating diverse datasets and discerning intricate relationships between input dataand outputs.Consequently,ML approaches hold promise in mitigating the structure biases,data-specificrequirements,and model calibratio","cbCaipR26waxRw4t","https://ap.wps.com/l/cbCaipR26waxRw4t","pdf",1596577,1,20,"English","en",105,"# Abstract\n# Introduction\n## Motivation and need for rapid damage assessment\n## Limitations of depth–damage curve approaches\n## Technical challenges in fine-resolution damage nowcasting\n## Data limitations from NFIP\n## Proposed FloodDamageCast framework","[{\"question\":\"What problem does FloodDamageCast address?\",\"answer\":\"FloodDamageCast targets near-real-time building flood damage estimation, called damage nowcasting, to help emergency responders decide on evacuations and repair priorities.\"},{\"question\":\"What data and resolution does the framework aim to produce?\",\"answer\":\"It predicts residential flood damage at 500 m by 500 m resolution for Harris County, Texas, during Hurricane Harvey 2017.\"},{\"question\":\"How does FloodDamageCast handle the class imbalance in flood damage data?\",\"answer\":\"It incorporates generative adversarial networks-based data augmentation coupled with an efficient machine learning model to mitigate imbalance between damaged and non-damaged buildings.\"}]","FLOODDAMAGECAST: BUILDING FLOOD DAMAGE NOWCASTING WITH MACHINE LEARNING AND DATA AUGMENTATION | 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