{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T16:52:19Z","timestamp":1784911939699,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T00:00:00Z","timestamp":1746316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u20132023, 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/computers14050175","type":"journal-article","created":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T20:09:14Z","timestamp":1746389354000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5494-8467","authenticated-orcid":false,"given":"Snehal","family":"Satish","sequence":"first","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3360-154X","authenticated-orcid":false,"given":"Hari","family":"Gonaygunta","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6377-6265","authenticated-orcid":false,"given":"Akhila Reddy","family":"Yadulla","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2137-0864","authenticated-orcid":false,"given":"Deepak","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3335-7516","authenticated-orcid":false,"given":"Mohan Harish","family":"Maturi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6056-7577","authenticated-orcid":false,"given":"Karthik","family":"Meduri","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5599-506X","authenticated-orcid":false,"given":"Elyson","family":"De La Cruz","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7126-5186","authenticated-orcid":false,"given":"Geeta Sandeep","family":"Nadella","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0327-2450","authenticated-orcid":false,"given":"Guna Sekhar","family":"Sajja","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,4]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Predicting earthquake hazards using neural networks and big data","volume":"126","author":"Anderson","year":"2021","journal-title":"J. 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