Physically Plausible Scenario Generation via \ Geo-Enhanced LLMs for Disaster Training

Physically Plausible Scenario Generation via \ Geo-Enhanced LLMs for Disaster Training | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physically Plausible Scenario Generation via \ Geo-Enhanced LLMs for Disaster Training Koki Asami, Kei Hiroi, Michinori Hatayama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7715103/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Scenario-based training is central to disaster preparedness, yet creating realistic scenarios is labor-intensive and limited by the weak spatial reasoning of large language models (LLMs). This study introduces a Geo-enhanced prompting framework that integrates elevation, slope, hazard maps, and breach distance into GPT-4.1 prompts to generate physically consistent, time-stepped disaster scenarios. The method was applied to flood response in Hirakata City, Japan, using publicly available geospatial datasets. A total of 740 scenarios were generated across ten locations and 37 timesteps per site, corresponding to realistic training intervals. Evaluation by a municipal crisis-management professional showed that all Geo-enhanced scenarios were physically plausible, while a geography-agnostic baseline produced numerous implausible outputs, including inconsistent inundation sequences. By embedding geospatial constraints directly into prompts, the approach improved temporal coherence and spatial ordering and removed the need for corrections due to physical contradictions. Because the framework relies only on open data and lightweight preprocessing, it is adaptable to other hazards and regions without the cost of model retraining. These findings demonstrate that geospatially grounded LLM prompting can mitigate structural weaknesses in spatial reasoning, offering a scalable pathway to more realistic and operationally useful training materials for disaster preparedness. GeoAI large language models scenario generation disaster training Full Text Additional Declarations No competing interests reported. Supplementary Files 202505164A.xlsx 202505164B.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 31 Oct, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 25 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7715103","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522759191,"identity":"903a46fa-6e7b-42a6-b81d-2037b49784b1","order_by":0,"name":"Koki 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Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"GeoAI, large language models, scenario generation, disaster training","lastPublishedDoi":"10.21203/rs.3.rs-7715103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7715103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eScenario-based training is central to disaster preparedness, yet creating realistic scenarios is labor-intensive and limited by the weak spatial reasoning of large language models (LLMs). This study introduces a Geo-enhanced prompting framework that integrates elevation, slope, hazard maps, and breach distance into GPT-4.1 prompts to generate physically consistent, time-stepped disaster scenarios. The method was applied to flood response in Hirakata City, Japan, using publicly available geospatial datasets. A total of 740 scenarios were generated across ten locations and 37 timesteps per site, corresponding to realistic training intervals. Evaluation by a municipal crisis-management professional showed that all Geo-enhanced scenarios were physically plausible, while a geography-agnostic baseline produced numerous implausible outputs, including inconsistent inundation sequences. By embedding geospatial constraints directly into prompts, the approach improved temporal coherence and spatial ordering and removed the need for corrections due to physical contradictions. Because the framework relies only on open data and lightweight preprocessing, it is adaptable to other hazards and regions without the cost of model retraining. These findings demonstrate that geospatially grounded LLM prompting can mitigate structural weaknesses in spatial reasoning, offering a scalable pathway to more realistic and operationally useful training materials for disaster preparedness. \u003c/p\u003e","manuscriptTitle":"Physically Plausible Scenario Generation via \\ Geo-Enhanced LLMs for Disaster Training","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 03:11:39","doi":"10.21203/rs.3.rs-7715103/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-30T14:40:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36764977085140182431502393349096839221","date":"2025-11-14T17:54:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T06:18:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70012653214824186152265891729509544127","date":"2025-11-10T16:50:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298918769528799905753240369450768716307","date":"2025-11-06T01:16:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T01:12:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T06:41:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T09:12:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Disaster Science and Management","date":"2025-09-25T16:44:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-disaster-science-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44367","submissionUrl":"https://submission.springernature.com/new-submission/44367/3","title":"Journal of Disaster Science and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b94902c-7b08-4668-847e-567ab4baf19e","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T03:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 03:11:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7715103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7715103","identity":"rs-7715103","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}