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@article{174523,
        author = {KARAKA YATHEESH PAVAN KUMAR and PANUGANTI RAVI and PADALA HANISH and KAPULA DIVYA PRASANNA SANJUSHA and TUMMI CHAITANYA},
        title = {LEVERAGING DEEP LEARNING FOR EFFECTIVE FLOOD PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4235-4240},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174523},
        abstract = {Floods remain one of the most catastrophic natural disasters, causing massive loss of life, infrastructure damage, and economic downturns. Traditional flood prediction models rely on machine learning techniques such as K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), and Decision Trees, which often fail to capture the nonlinear relationships in flood occurrences. To address these limitations, we propose a deep learning-based flood prediction system utilizing Artificial Neural Networks (ANN). Our approach integrates crucial environmental factors such as monsoon intensity, river management, climate change, and urbanization. The ANN model is trained on an extensive dataset, achieving an accuracy of 99.981%, significantly surpassing conventional prediction techniques. The system also features a user-friendly web interface that provides real-time flood probability assessments, empowering disaster management authorities to take proactive measures. By leveraging deep learning, this study enhances flood prediction reliability, reduces response time, and mitigates disaster impacts.},
        keywords = {Flood prediction, Deep learning, Artificial Neural Networks (ANN), Disaster management, Climate change, Environmental monitoring, Real-time forecasting.},
        month = {March},
        }