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All Journal International Journal of Informatics Engineering and Computing
Khaled Abduljalil Saleh AL-SADI
Harbin University of Science and Technology, China
Computer Science & IT

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Identifying Devastated Flood Events using CNN Algorithm Khaled Abduljalil Saleh AL-SADI
International Journal of Informatics Engineering and Computing Vol. 3 No. 2 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ct24p639

Abstract

Tsunami prediction requires accurate analysis of seismic and oceanographic parameters to support timely disaster mitigation. This study applies a Convolutional Neural Network (CNN) to classify tsunami-related and non-tsunami events using historical data from the National Oceanic and Atmospheric Administration (NOAA) tsunami dataset. We utilized relevant features, including earthquake magnitude, depth, location, wave height, and other tsunami-related parameters, to train and evaluate the model. The CNN learned nonlinear patterns among these variables and achieved 92% accuracy on the test dataset. The evaluation also produced 90% precision, 94% recall, and 92% F1-score, demonstrating consistent classification performance. The high recall indicates that the model successfully identified most actual tsunami events, which is particularly important in disaster prediction applications where missed tsunami events may lead to serious consequences. We also applied a probability threshold of 0.5 to determine the predicted class, and the prediction results showed that most samples obtained probabilities clearly above or below the threshold. These findings indicate that the proposed CNN can effectively learn patterns from historical seismic and oceanographic data.
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