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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Disaster Prediction and Resource Allocation: An XGBoost Approach Using LLM-Generated Historical Data |
| Authors Name: | Vinit Shah , Vedant Khade , Prof Sindhu Nair |
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IJRTI_210518
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| Published Paper Id: | IJRTI2603125 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | Effective disaster management is an essential challenge be- cause of the uncertainty of events, the unstructured character of arriv- ing information, and the necessity for timely, resource-efficient reactions. Historical systems are inadequate with real-time flexibility and usually do not have intelligent decision-support tools for prediction and distri- bution. This work presents DISPATCH-NDMA (Disaster Prediction and Allocation Through Computational Heuristic for National Disaster Man- agement), an algorithmic multi-modal, real-time disaster prediction and adaptive emergency resource allocation system. Fundamentally, the sys- tem uses a boosted XGBoost architecture with added temporal atten- tion mechanisms and spatial graph embeddings, which yields state-of- the-art disaster localization with 0.49° mean absolute error. An LLM- powered feature extraction pipeline (on GPT-4) translates unstructured disaster reports into structured representations along geographic, demo- graphic, and logistical aspects. Retrieval-augmented generation (RAG) on a FAISS-indexed database facilitates contextual grounding through semantically close historical analogs. Field tests in the 2023 Maharash- tra floods and 2024 Gujarat industrial accidents registered a 30% im- provement in resource positioning efficiency and dramatically shortened response times. A user-focused Streamlit interface and Twilio-powered alerting infrastructure also enhance real-time interaction and emergency coordination, providing a scalable, confidence-aware infrastructure for future disaster management in time-critical, data-constrained environ-ments. |
| Keywords: | Disaster Forecasting, Resource Management, XGBoost, Large Language Models, Retrieval-Augmented Generation, Emergency Management, Real-Time Optimization |
| Cite Article: | "Disaster Prediction and Resource Allocation: An XGBoost Approach Using LLM-Generated Historical Data", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b201-b211, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603125.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2603125
Registration ID:210518
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: b201-b211 Country: mumbai, maharastra, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603125 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603125 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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