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@article{175219,
        author = {Aman Sande},
        title = {Natural Disaster Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2529-2532},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175219},
        abstract = {The increasing frequency and severity of natural disasters worldwide necessitate sophisticated predictive mechanisms to minimize their devastating impact. This paper examines the application of various artificial intelligence and machine learning paradigms for forecasting natural calamities including seismic events, hydrological disasters, and extreme meteorological phenomena. Our analysis focuses primarily on advanced algorithmic approaches such as ensemble methods, recurrent neural networks, and visual pattern recognition systems. The study evaluates predictive performance using comprehensive datasets from international monitoring agencies. Experimental findings demonstrate that neural network architectures with temporal awareness consistently outperform conventional statistical models in prediction accuracy. The research highlights the transformative potential of AI-driven systems in processing multidimensional environmental data and proposes architectural improvements to enhance real-time predictive capabilities for disaster management frameworks.},
        keywords = {Natural disaster prediction, machine learning, deep learning, disaster forecasting, risk assessment, early warning systems, AI in disaster management, and geospatial data analysis.},
        month = {April},
        }