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@article{195274,
        author = {Amena Jabeen and Alisha Batwar and A. Siddeshwari},
        title = {FLOOD AND LANSLIDE PREDICTION TECHNOLOGY: HUMAN-CENTRIC RESILINCE AND RESPONSE PLANNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7665-7674},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195274},
        abstract = {Natural hazards such as landslides and food insecurity create serious problems, especially in areas with difficult terrain and populations that are highly vulnerable. This research proposes a machine learning based framework to predict landslide events and potential food shortages in high-risk regions. Multiple datasets are combined in the model, including historical weather data, soil characteristics, land-use information, terrain features, and socio-economic factors, to generate early warnings and useful insights. For landslide prediction, algorithms such as Random Forest, Gradient Boosting, and Neural Networks are used to evaluate the probability of landslide occurrence. In addition, the food prediction component applies time-series analysis and regression methods to estimate crop production trends and identify possible shortages. The models are tested and validated using real-world datasets collected from vulnerable areas. Experimental results show that the proposed approach achieves higher prediction accuracy compared with traditional prediction techniques. This system can support policymakers and disaster-management authorities in making proactive decisions and preparing effective strategies to reduce the impact of such disasters on communities.},
        keywords = {Machine Learning, Floods and Landslides.},
        month = {March},
        }