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@article{186138,
        author = {Sasane kartik Pravin and DHAMALE NIKHIL SHAM and YADAV OM DATTATRAY and PROF.BRAMHANE.P.S},
        title = {DISASTER MANAGEMENT SYSTEM AND PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {789-792},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186138},
        abstract = {Abstract— Natural disasters similar as cataracts, earthquakes, cyclones, backfires, and landslides beget significant loss of life, property, and coffers every time. A Disaster Management System and Prediction aim to minimize these losses by using ultramodern technologies for early discovery, vaticination, and effective response collaboration. This system integrates data from multiple sources similar as rainfall satellites, seismic detectors, IoT bias, and literal datasets to dissect and prognosticate implicit disaster circumstances using machine literacy and artificial intelligence algorithms. The proposed system consists of three major factors data accession, disaster vaticination, and response operation. The vaticination module utilizes AI- grounded models to read disaster chances and issue timely cautions to authorities and the public. The operation module supports decision- making during extremities by offering real- time situational mindfulness, automated announcements, resource allocation, and communication between deliverance brigades and original authorities. By employing prophetic analytics, geospatial visualization, and pall- grounded communication, the system enhances preparedness and reduces response time during critical events. The integration of AI and IoT enables nonstop monitoring and intelligent soothsaying, leading to visionary disaster operation. Overall, this system provides a comprehensive, data- driven approach to perfecting adaptability, minimizing mortal and profitable losses, and icing effective disaster response},
        keywords = {},
        month = {November},
        }