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@article{183290,
        author = {Prof Annapurna K. Salunke and Prof. Geetanjali R. Kulkarni and Prof. Prajakta V. Kale},
        title = {A Review Paper On Tidal Analysis For Cyclone Prediction Using CNN And MLP},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {787-794},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183290},
        abstract = {Cyclone prediction is crucial for effective disaster preparedness and risk mitigation. Traditional forecasting methods, while advanced, can be significantly enhanced by integrating modern machine learning techniques. This review paper investigates the use of sea tide level data in conjunction with Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) to improve cyclone prediction accuracy. By analyzing the relationship between anomalous sea level fluctuations and cyclonic activity, this study explores how CNNs and MLPs can process and interpret complex patterns in tide gauge data. The integration of these machine learning models with traditional meteorological data aims to provide more accurate and timely cyclone warnings. This paper reviews current methodologies, discusses the efficacy of CNNs and MLPs in tide level analysis, and evaluates their potential to enhance existing cyclone prediction models. Challenges and future research directions are also discussed, emphasizing the need for a multidisciplinary approach to harness the potential of these advanced techniques fully.},
        keywords = {Cyclone prediction, sea tide level, Convolutional Neural Networks (CNNs), Multilayer Perceptrons (MLPs), tide gauge data, machine learning, meteorological models, forecasting accuracy, disaster mitigation.},
        month = {August},
        }