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@article{176434,
        author = {Yogiraj Sunil Jadhav and Abhishek Ashish Nimbalkar and Siddharth Janardhan Darwade and Shubham Dattatray Khurange and Rajesh Keshav Nale},
        title = {LANDSLIDE PREDICTION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7362-7364},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176434},
        abstract = {This project focuses on the development of a machine / deep learning-based landslide prediction system designed to enhance disaster management efforts. By analyzing geological and environmental data such aselevation, slope gradient, and soil composition, the system aims to detect past landslide occurrences accurately. The methodology incorporates various machine / deep learning algorithms, including Logistic Regression, Random Forest, and Convolution Neural Networks (CNNs), to process the input data and generate reliable detection results. The project contributes valuable insights for improving community preparedness and response strategies against landslides},
        keywords = {Landslide Detection, Risk Assessment, Disaster, Disaster Prediction},
        month = {April},
        }