Integrating Machine Learning With GIS for Real-Time Natural Disaster Prediction

  • Authors

    • Chen Ming Software Architect, Huawei, China. Author
    • Wang Li Data Science Manager, Alibaba Group, China. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2021PI1Q9D

    Published 04-04-2021

  • Machine Learning, Geographic Information Systems, Natural Disaster Prediction, Real-Time Forecasting, Disaster Management, AI-Driven Models, Data Quality, Model Transparency

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    C. Ming and W. Li, “Integrating Machine Learning With GIS for Real-Time Natural Disaster Prediction”, IJETMR, vol. 4, no. 1, pp. 01–08, Apr. 2021, doi: 10.67228/30715636/IJETMR-2021PI1Q9D.

  • Abstract

    The integration of Machine Learning (ML) with Geographic Information Systems (GIS) has revolutionized the prediction and management of natural disasters. This paper explores the synergistic application of ML algorithms within GIS platforms to enhance real-time disaster forecasting, monitoring, and response strategies. We examine various case studies where ML models have successfully analyzed spatial and temporal data to predict events such as hurricanes, floods, and wildfires. The research highlights the role of AI-driven forecasting models, data collection improvements, and real-time response mechanisms in urban environments, emphasizing the importance of accurate and timely disaster predictions. Challenges such as data quality, model transparency, and regional disparities in data availability are also discussed, along with potential solutions to mitigate these issues. The paper concludes with recommendations for future research and the development of best practices to effectively integrate ML and GIS in disaster management.​

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