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@article{168792,
        author = {Dr. J.B.Shajilin Loret and Kasirajesh S},
        title = {predictive modelling for natural disaster with Machine learning algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {2208-2211},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168792},
        abstract = {Natural disasters, such as floods, earthquakes, and wildfires, have profound consequences on human life and infrastructure, prompting the need for improved prediction models and more efficient rescue operations. Traditional machine learning models, though effective, often struggle with the complexity and variety of disaster-related data, which includes categorical variables such as disaster types, weather conditions, and geographical locations. In this study, we present an approach using CatBoost, a high-performance gradient boosting algorithm, designed to handle such categorical data efficiently. We demonstrate the application of CatBoost to develop accurate and robust predictive models for disaster forecasting and introduce an optimization framework that integrates predictions with Geographic Information Systems (GIS) for resource allocation in rescue operations. The proposed system improves early-warning mechanisms and enhances decision-making processes during disaster management.},
        keywords = {},
        month = {October},
        }