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@article{156079,
        author = {Pooja K G and Manasa B  and Pooja P  and Vaibhavi V B and Sharan L Pais},
        title = {An EWS for Prediction of Earthquake Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {786-791},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156079},
        abstract = {Earthquake Early Warning (EEW) system may be a period of time earthquake harm mitigation system. It detects, analyzes and transmits information of the next upcoming event at the potential user sites. An endeavor has been created to develop a multi-parameter-based EEW formula for correct and reliable supplying of EEW. The planned formula depends on a convolutional neural network (CNN) Algorithm that has the flexibility to extract vital options from waveforms that enabled the classifier to succeed in a strong performance within the needed earthquake parameters. Victimization of K-Mean formula to analyzing unstable datasets in conjunction with mental image for deciphering the results. With the advancement In machine learning and deep learning, it's attainable to extract helpful information and train models on massive datasets. we are able to predict the earthquakes supported that location’s knowledge and therefore the knowledge of larger area’s. Magnitude determination of earthquakes may be a obligatory step before An earthquake early warning (EEW) system sends an Alarm and therefore the foremost step includes classification of the Hyperparameters: location, magnitude, depth, and origin time of earthquake .},
        keywords = {Deep learning, Earthquake Early Warn- ing(EEW) system, Classification of hyperparameters, earthquake magnitude , CNN Algorithm .},
        month = {},
        }