Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics

Satish, Snehal; Gonaygunta, Hari; Yadulla, Akhila Reddy; Kumar, Deepak; Maturi, Mohan Harish; Meduri, Karthik; De La Cruz, Elyson; Nadella, Geeta Sandeep; Sajja, Guna Sekhar

doi:10.3390/computers14050175

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Open AccessArticle

Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics

by

Snehal Satish

,

Hari Gonaygunta

,

Akhila Reddy Yadulla

,

Deepak Kumar

,

Mohan Harish Maturi

,

Karthik Meduri

^†

,

Elyson De La Cruz

,

Geeta Sandeep Nadella

^*,†

and

Guna Sekhar Sajja

Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Computers 2025, 14(5), 175; https://doi.org/10.3390/computers14050175

Submission received: 9 February 2025 / Revised: 28 April 2025 / Accepted: 30 April 2025 / Published: 4 May 2025

(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)

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Abstract

This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving the accuracy and lead times of tsunami occurrence predictions. The study employs machine learning methods, including Random Forest and Logistic Regression, for binary classification of tsunami events. Data collection is performed using a Kaggle dataset spanning 1995–2023, with preprocessing and exploratory analysis to identify critical patterns. The Random Forest model achieved superior performance with an accuracy of 0.90 and precision of 0.88 compared to Logistic Regression (accuracy: 0.89, precision: 0.87). These results underscore Random Forest’s effectiveness in handling imbalanced data. Challenges such as improving data quality and model interpretability are discussed, with recommendations for future improvements in real-time warning systems.

Keywords: predictive model; tsunami occurrence; earthquake data; data analytics; machine learning (ML); forecasting

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MDPI and ACS Style

Satish, S.; Gonaygunta, H.; Yadulla, A.R.; Kumar, D.; Maturi, M.H.; Meduri, K.; De La Cruz, E.; Nadella, G.S.; Sajja, G.S. Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics. Computers 2025, 14, 175. https://doi.org/10.3390/computers14050175

AMA Style

Satish S, Gonaygunta H, Yadulla AR, Kumar D, Maturi MH, Meduri K, De La Cruz E, Nadella GS, Sajja GS. Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics. Computers. 2025; 14(5):175. https://doi.org/10.3390/computers14050175

Chicago/Turabian Style

Satish, Snehal, Hari Gonaygunta, Akhila Reddy Yadulla, Deepak Kumar, Mohan Harish Maturi, Karthik Meduri, Elyson De La Cruz, Geeta Sandeep Nadella, and Guna Sekhar Sajja. 2025. "Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics" Computers 14, no. 5: 175. https://doi.org/10.3390/computers14050175

APA Style

Satish, S., Gonaygunta, H., Yadulla, A. R., Kumar, D., Maturi, M. H., Meduri, K., De La Cruz, E., Nadella, G. S., & Sajja, G. S. (2025). Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics. Computers, 14(5), 175. https://doi.org/10.3390/computers14050175

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Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics

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