Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics
Abstract
:1. Introduction
1.1. Research Problem
1.2. Research Questions
- Which machine learning architectures are most effective for binary classification of tsunami-generating earthquakes?
- What feature selection strategies optimize model performance while maintaining computational efficiency?
- How can temporal patterns in seismic data improve prediction lead times?
1.3. Research Objective
1.4. Structure of Paper
2. Literature Review
2.1. Introduction to Tsunami Occurrence Prediction
2.2. Traditional Tsunami Occurrence Prediction
- Seismic Activity MonitoringSeismic activity monitoring is a cornerstone of tsunami prediction, involving the continuous observation of seismic waves generated by underwater earthquakes [14,15]. This method leverages networks of seismometers and ocean-bottom sensors to detect potential tsunami triggers.
- Seismicity Patterns: Monitoring seismicity patterns involves analyzing the frequency and distribution of earthquakes, particularly foreshocks, which may precede a tsunami-generating event. Seismic gaps—regions along faults with low recent activity—are critical indicators of potential large earthquakes [16]. For example, the 2011 Tohoku earthquake was preceded by a seismic gap along the Japan Trench, highlighting the predictive value of this approach [17].
- Earthquake Swarms: Earthquake swarms, characterized by clusters of seismic events in a short period, can signal stress changes in the Earth’s crust. While not all swarms lead to tsunamis, they are monitored closely in subduction zones like the Pacific Ring of Fire [16]. Advanced algorithms now enhance swarm detection by filtering noise from seismic data [18].
- Precursors and Anomalies: Seismic precursors, such as changes in P-wave velocity or P-wave shadowing, indicate potential tsunami-generating earthquakes. P-wave shadowing occurs when primary seismic waves are absorbed or deflected, suggesting imminent fault rupture [19]. Recent studies have improved precursor detection using machine learning to analyze seismic waveforms [20].
- Fault Line AnalysisFault line analysis focuses on submarine faults, where tectonic plate movements can displace ocean water, generating tsunamis [21]. This method combines geological mapping, historical data, and geophysical measurements to assess tsunami risk.
- Stress Accumulation: GPS and InSAR (Interferometric Synthetic Aperture Radar) measurements monitor stress accumulation along faults. High stress rates indicate elevated tsunami risk, as seen during the 2018 Palu tsunami [24].
- Paleo-Seismology and Paleo-Tsunami Studies: Paleo-seismology examines sediment layers to reconstruct past earthquakes and tsunamis, offering a long-term perspective on tsunami frequency [25]. For example, paleo-tsunami deposits in Chile have revealed 500-year recurrence intervals for major events [18].
- Slip Rates and Fault Segmentation: Faults are segmented, with each segment exhibiting unique slip rates. Analyzing these rates helps predict which segments are prone to tsunami-generating earthquakes [26]. Recent advancements in fault segmentation models have improved risk assessments for regions like Indonesia [27].
2.3. The Finite Element Method (FEM) in Tsunami Prediction
- Fault Mechanics: Simulating stress accumulation and rupture along submarine faults to predict tsunami triggers [26].
- Wave Propagation: Modeling tsunami wave interactions with bathymetry and coastal topography to estimate inundation zones [30].
- Infrastructure Assessment: Evaluating the impact of tsunami waves on coastal structures, informing resilient design standards [31].
2.4. AI and Machine Learning in Tsunami Prediction
- Data-Driven Prediction: ML models analyze seismic, oceanic, and satellite data to predict tsunami events. For example, convolutional neural networks (CNNs) have identified tsunami precursors in seismic waveforms, improving early warning systems [20]. Studies in Japan and Indonesia have demonstrated ML’s ability to provide minutes of warning before tsunami impact [37,38].
- Real-Time Monitoring: AI systems integrate data from ocean buoys, tide gauges, and seismic stations to detect tsunami signals in real time. Recurrent neural networks (RNNs) have been used to predict tsunami wave paths, enabling timely evacuations [36]. The Indonesia Tsunami Early Warning System employs ML to process real-time seismic data [36].
- Resource Optimization: AI optimizes emergency resource allocation by predicting high-risk areas. For instance, ML models prioritize medical and rescue deployments based on forecasted inundation zones [39].
2.5. Successes and Challenges
- Complexity of Tsunami Triggers: Tsunamis result from diverse sources (earthquakes, landslides, volcanic activity), complicating ML predictions [32].
- Data Quality: Incomplete or noisy datasets limit model performance [36].
- False Positives/Negatives: ML models struggle to distinguish tsunamigenic from non-tsunamigenic seismic events, risking false alarms or missed events [23].
- Interpretability: Deep learning models, such as neural networks, often lack transparency, reducing trust in high-stakes scenarios [31].
2.6. Data Analytics and Analytical–Numerical Techniques in Tsunami Research
- Enhanced Data Processing: Data analytics tools manage terabytes of geophysical data, extracting patterns that indicate tsunami precursors. Wavelet transformations and Fourier analysis remove noise from seismic signals, improving feature detection [28].
- Improved Prediction Accuracy: ML models trained on historical tsunami data identify trends that enhance forecast precision. For example, Bayesian inference has refined probabilistic tsunami hazard models [12].
- Risk Assessment: Data-driven models assess tsunami impacts on coastal regions, informing infrastructure planning [31].
- Integration with Technologies: Analytics integrates with IoT, remote sensing, and satellite imagery, creating comprehensive monitoring systems [18].
2.6.1. Analytical–Numerical Techniques
- Normalize heterogeneous datasets, ensuring compatibility with ML algorithms [12];
- Reduce error margins by filtering redundant information [29];
- Enhance feature selection, identifying key tsunami precursors [26].
2.6.2. Case Studies
- Southern California Earthquake Center (SCEC): The SCEC uses data analytics to model fault systems, predicting seismic risks that inform tsunami preparedness [42].
- Japan’s EEWS: Real-time analytics of seismic data enables seconds-to-minutes warnings, reducing casualties [41].
- IRIS Data Management: Global seismic data analysis enhances our understanding of tsunami-generating processes [41].
- Aftershock Modeling: ML models predict aftershock patterns, aiding post-tsunami recovery in regions like Nepal [42].
3. Methodology
3.1. Data Collection and Preprocessing
- Removed 7 redundant columns (e.g., nat, continent);
- Standardized formats for time, latitude, and longitude;
- Selected 6 critical features: magnitude, depth, latitude, longitude, aftershock occurrence, and seismic moment (derived from msqType and dmin).
3.2. Dataset Suitability for Tsunami Occurrence Prediction
3.3. Exploratory Data Analysis (EDA)
3.4. Machine Learning Model Implementations
- Random Forest: The Random Forest model is selected for its robust performance in handling the complex and imbalanced datasets encountered in tsunami occurrence forecasting. As a collaborative wisdom method, it builds numerous choice trees in the preparation and combines these results to recover prognostic exactitude and regulator overfitting. This model excels in capturing nonlinear relationships and interactions among features like earthquake magnitude, depth, and location, which are crucial for accurate predictions. Its ability to rank the feature importance also aids our understanding of which factors most significantly influence tsunami occurrences caused by earthquakes and is a powerful tool for forecasting seismic events.
- Logistic Regression: Logistic Regression is the perfect standard owing to its plainness and interpretability in binary classification tasks. It operates by modeling the probability of a given event (in this case, the likelihood of a significant earthquake) based on the linear combination of input features. The simplicity and Logistic Regression capture the connection among the predictors, and the goal is adjustable when these relationships are approximately linear. This model provides a clear benchmark to compare the performance of more complex models like the Random Forest and allows for the straightforward interpretation of the impact of individual features on earthquake occurrence probabilities. Its results also serve as a point of reference for evaluating the added value of using more sophisticated modeling techniques.
- Geographical Constraints: The dataset used in this study was limited to earthquake-prone regions with significant seismic activity, ensuring that the model did not learn from irrelevant non-seismic areas. Latitude and longitude coordinates were standardized using a fixed geographic reference system to ensure spatial consistency. This constraint prevented the model from biasing predictions based on location-based inconsistencies and ensured that earthquake-prone regions were given appropriate weighting in the forecasting process.
- Seismic Magnitude Range: The machine learning models were trained on earthquakes with magnitudes between 4.0 and 9.1, as smaller seismic events (below 4.0) are generally undetectable by many monitoring systems and do not pose significant hazards. This threshold ensured that the model focused only on earthquakes that have practical implications for disaster preparedness and mitigation. The exclusion of smaller tremors also improved model accuracy by reducing noise in the dataset.
- Time Constraints and Temporal Data Processing: The dataset covered seismic activity from 1995 to 2023 (28 years of data). A well-defined time window allowed the model to analyze long-term seismic trends while ensuring that the dataset included modern earthquake activity. The timestamps of tsunami occurrences caused by earthquakes were standardized to remove inconsistencies, ensuring that the model was trained on correctly formatted data. The data were divided into training (80%) and testing (20%) subsets while temporal integrity was maintained to prevent information leakage.
- Feature Selection and Data Preprocessing Constraints: To improve model performance, only seismically relevant features were included in the training:
- –
- Magnitude—Measures the earthquake’s strength.
- –
- Depth—Affects the severity of surface shaking.
- –
- Latitude and Longitude—Define the earthquake’s location.
- –
- Time—Captures seasonal and temporal seismic patterns.
- –
- Aftershock Occurrence—Helps identify earthquake sequences.
Features that did not contribute significantly to model accuracy were removed to reduce noise and prevent overfitting. Data normalization techniques such as min–max scaling were applied to ensure that all numerical inputs were within a comparable range, improving the stability of AI model training. - Computational Constraints and Model Training Parameters: Since the dataset was large and complex, computational efficiency was a major consideration. Batch processing techniques were applied to optimize memory usage and training efficiency, preventing computational overload. The Random Forest model was configured with 100 decision trees, and the Logistic Regression model was trained using L2 regularization to prevent excessive variance in predictions.
3.5. Evaluation and Validation
4. Predictive Analysis and Results
4.1. Training Time and Analysis
4.2. Comparison with Experimental Data
4.3. Data Interpretation and Implications
4.4. Learning Curves and Model Performance Visualization
5. Challenges and Limitations
5.1. Data Challenges
- Tsunami occurrence prediction relies on diverse datasets, including seismic records, geological surveys, satellite imagery, and sensor data.
- Access to these data is limited due to geographical, political, and financial constraints. In many regions with lower economic resources, the density and quality of seismic monitoring networks may be inadequate compared to imperfect and partial datasets that mark the predictive recital of AI copies.
- The mixing of heterogeneous data sources presents another significant challenge.
- Seamlessly integrating and maintaining the consistency of these datasets is complex and introduces errors if not appropriately handled. The varied nature of significant data sources, such as satellite imagery and IoT sensor data, involves varying levels of noise and redundancy that need to be filtered out during preprocessing.
5.2. Model Limitations
5.3. Computational and Infrastructural Constraints
5.4. Analytical–Numerical Methodologies for Model Validation
5.5. Practical Field Testing Considerations
5.6. Error Analysis and Uncertainty Discussion
5.7. Future Applications
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Anderson, T.R.; White, G.J. Predicting earthquake hazards using neural networks and big data. J. Geophys. Res. Solid Earth 2021, 126, 1–15. [Google Scholar]
- Ahmed, S.; El-Mahdy, M. Hybrid AI models for accurate earthquake forecasting using seismic and envi-ronmental data. J. Tsunami Occur. Predict. Res. 2024, 10, 145–158. [Google Scholar]
- Bhatia, R.; Mehta, S. Machine learning applications in earthquake prediction: An integrated approach. Seism. Hazard Anal. J. 2023, 19, 275–290. [Google Scholar]
- Behr, M.; Khoshgoftaar, T.M. Enhancing earthquake prediction models with ensemble learning techniques. Earth Sci. Inform. 2022, 15, 123–136. [Google Scholar]
- Chen, L.; Zhang, H. Integrating machine learning and seismic data for earthquake prediction. Seismol. Res. Lett. 2023, 94, 287–298. [Google Scholar] [CrossRef]
- Chen, Y.; Feng, R. AI-enhanced seismic monitoring systems for earthquake prediction. J. Seismol. Earthq. Eng. 2024, 28, 421–434. [Google Scholar]
- Chang, Y.; Lin, T. Big data and AI: Revolutionizing earthquake early warning systems. Int. J. Disaster Risk Reduct. 2022, 67, 102717. [Google Scholar]
- Evans, R.; Martin, K. AI-driven models for seismic event prediction: A comparative study. Geophys. J. Int. 2021, 228, 1885–1898. [Google Scholar] [CrossRef]
- Das, S.; Choudhury, P. AI-driven seismic hazard analysis: A big data approach. Earthq. Eng. Struct. Dyn. 2024, 53, 199–210. [Google Scholar] [CrossRef]
- Feng, X.; Zhang, Y. Big data analytics in earthquake forecasting: A machine learning perspective. J. Seismol. 2023, 27, 12–25. [Google Scholar]
- Gao, Y.; Liu, J. Deep learning models for real-time earthquake forecasting. Geophys. J. Int. 2022, 230, 1673–1685. [Google Scholar]
- Garg, N.; Sethi, R. Leveraging AI and deep learning for enhanced earthquake forecasting accuracy. Earthq. Sci. 2023, 36, 89–102. [Google Scholar]
- Gupta, R.; Kumar, S. Application of big data analytics in seismic risk prediction. Nat. Hazards 2023, 114, 659–674. [Google Scholar]
- Hamid, M.; Ali, K. AI-based predictive models for earthquake hazard assessment. Environ. Model. Softw. 2021, 144, 105182. [Google Scholar] [CrossRef]
- Hasan, M.; Tariq, M. Applying reinforcement learning for real-time earthquake forecasting. Nat. Disasters Rev. 2023, 22, 154–168. [Google Scholar] [CrossRef]
- Huang, W.; Song, L. Predicting earthquake occurrences using big data and AI techniques. Pure Appl. Geophys. 2022, 179, 3547–3561. [Google Scholar]
- Kaur, S.; Aggarwal, R. Predictive analytics for earthquake magnitudes using AI techniques. Nat. Hazards Earth Syst. Sci. 2022, 22, 1379–1390. [Google Scholar]
- Omira, R.; Baptista, M.A.; Matias, L. Probabilistic tsunami hazard in the Northeast Atlantic from near- and far-field tectonic sources. Pure Appl. Geophys. 2022, 179, 901–920. [Google Scholar] [CrossRef]
- Ivanov, D.; Petrov, P. Analyzing seismic data with machine learning for earthquake prediction. Bull. Seismol. Soc. Am. 2023, 113, 65–78. [Google Scholar]
- Makinoshima, F.; Oishi, Y.; Yamazaki, T.; Furumura, T.; Imamura, F. Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks. Nat. Commun. 2021, 12, 2253. [Google Scholar] [CrossRef]
- Jackson, D.D.; Ventura, S.J. The role of AI in earthquake forecasting: A review of recent advances. Nat. Commun. 2024, 15, 235. [Google Scholar]
- Kim, J.; Lee, H. Earthquake prediction using machine learning and data mining techniques. J. Asian Earth Sci. 2023, 245, 105946. [Google Scholar]
- Behrens, J.; Løvholt, F.; Jalayer, F.; Lorito, S.; Salgado-Gálvez, M.A.; Sørensen, M.; Abadie, S.; Aguir-re-Ayerbe, I.; Aniel-Quiroga, I.; Babeyko, A.; et al. Probabilistic tsunami hazard and risk analysis: A review of research gaps. Front. Earth Sci. 2021, 9, 628772. [Google Scholar] [CrossRef]
- Gusman, A.R.; Supendi, P.; Nugraha, A.D.; Power, W.; Latief, H.; Sunendar, H.; Widiyantoro, S.; Daryono; Wiyono, S.H.; Hakim, A.; et al. Source model for the tsunami inside Palu Bay following the 2018 Palu earthquake, Indonesia. Geophys. Res. Lett. 2019, 46, 8721–8730. [Google Scholar] [CrossRef]
- Li, Q.; Yu, W. Using convolutional neural networks for earthquake magnitude prediction. Comput. Geosci. 2024, 171, 105246. [Google Scholar]
- Mulia, I.E.; Ueda, N.; Miyoshi, T.; Gusman, A.R.; Satake, K. Machine learning-based tsunami inundation prediction derived from offshore observations. Nat. Commun. 2022, 13, 5489. [Google Scholar] [CrossRef]
- Setiyono, U.; Gusman, A.R.; Satake, K.; Fujii, Y. Pre-computed tsunami inundation database and forecast simulation in Pelabuhan Ratu, Indonesia. Pure Appl. Geophys. 2021, 178, 3219–3242. [Google Scholar] [CrossRef]
- Heidarzadeh, M.; Harada, T.; Satake, K.; Ishibe, T.; Gusman, A.R. Comparative analysis of the 2018 Fiji deep earthquake doublet: Deep normal-fault earthquakes generating tsunami. Geophys. Res. Lett. 2018, 45, 13043–13051. [Google Scholar] [CrossRef]
- Nakata, K.; Katsumata, A.; Mulia, I.E.; Gusman, A.R.; Nakano, M.; Kumagai, H. Tsunami data assimilation of cabled ocean bottom pressure records for the 2015 Torishima volcanic tsunami earthquake. J. Geophys. Res. Solid Earth 2021, 126, e2020JB021290. [Google Scholar] [CrossRef]
- Scorzini, A.R.; Di Bacco, M.; Sugawara, D.; Suppasri, A. Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment. Commun. Earth Environ. 2024, 5, 301. [Google Scholar] [CrossRef]
- Maeda, T.; Obara, K.; Shinohara, M.; Kanazawa, T.; Uehira, K. Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real-time tsunami forecasting. Geophys. Res. Lett. 2021, 48, e2021GL094230. [Google Scholar] [CrossRef]
- Cesario, E.; Giampá, S.; Baglione, E.; Cordrie, L.; Selva, J.; Talia, D. Machine Learning for Tsunami Waves Forecasting Using Regression Trees. Big Data Res. 2024, 36, 100452. [Google Scholar] [CrossRef]
- Wang, Y.; Imai, K.; Miyashita, T.; Ariyoshi, K.; Takahashi, N.; Satake, K. Coastal tsunami prediction in Tohoku region, Japan, based on S-net observations using artificial neural network. Earth Planets Space 2023, 75, 154. [Google Scholar] [CrossRef]
- Asunción, R.A.; Sánchez, S.; Morales, J.; Carrasco, F.; Martín, J.B. Prediction of Tsunami Alert Levels Using Deep Learning. Earth Space Sci. 2024, 11, e2023EA003385. [Google Scholar] [CrossRef]
- Yamanaka, Y.; Nakagawa, H. Machine learning approach for tsunami forecasting based on tide gauge data. J. Disaster Res. 2021, 16, 24–33. [Google Scholar] [CrossRef]
- Dharmawan, W.; Diana, M.; Tuntari, B.; Astawa, I.M.; Rahardjo, S.; Nambo, H. Tsunami tide prediction in shallow water using recurrent neural networks: Model implementation in the Indonesia Tsunami Early Warning System. J. Reliab. Intell. Environ. 2023, 10, 177–195. [Google Scholar] [CrossRef]
- Fauzi, A.; Mizutani, N. Machine learning algorithms for real-time tsunami inundation forecasting: A case study in Nankai region. Pure Appl. Geophys. 2020, 177, 1437–1450. [Google Scholar] [CrossRef]
- Musa, A.; Watanabe, O.; Matsuoka, H.; Hokari, H.; Inoue, T.; Murashima, Y.; Ohta, Y.; Hino, R.; Koshimura, S.; Kobayashi, H. Real-time tsunami inundation forecast system for tsunami disaster prevention and mitigation. J. Supercomput. 2018, 74, 3093–3113. [Google Scholar] [CrossRef]
- Ho, K.C.; Ko, J.Y.T.; Huang, H.H.; Lee, S.J. A Novel Approach to Tsunami Prediction Using Ambient Noise-Derived Green’s Functions. Geophys. Res. Lett. 2025, 52, e2024GL113971. [Google Scholar] [CrossRef]
- Mulia, I.E.; Gusman, A.R.; Satake, K. Applying a deep learning algorithm to tsunami inundation database of megathrust earthquakes. J. Geophys. Res. Solid Earth 2020, 125, e2020JB019690. [Google Scholar] [CrossRef]
- Goto, K.; Ishizawa, T.; Ebina, Y.; Imamura, F. Tsunami data assimilation of Himawari-8 infrared bands ob-servation for the 2018 Sulawesi (Palu), Indonesia, earthquake. Earth Planets Space 2021, 73, 36. [Google Scholar] [CrossRef]
- Heidarzadeh, M.; Ishibe, T.; Sandanbata, O.; Muhari, A.; Wijanarto, A.B. Numerical modeling of the subaerial landslide source of the 22 December 2018 Anak Krakatoa volcanic tsunami, Indonesia. Ocean Eng. 2019, 195, 106733. [Google Scholar] [CrossRef]
- Liu, Y.; Wu, H. Prediction of Road Traffic Congestion Based on Random Forest. In Proceedings of the 2017 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 9–10 December 2017; pp. 361–364. [Google Scholar] [CrossRef]
- Zhang, S.; Tan, S.; Zhou, J.; Sun, Y.; Ding, D.; Li, J. Geological disaster susceptibility evaluation of a random-forest-weighted deterministic coefficient model. Sustainability 2023, 15, 12691. [Google Scholar] [CrossRef]
- Sepúlveda, I.; Haase, J.S.; Carvajal, M.; Xu, X.; Liu, P.L.F. Modeling the sources of the 2018 Palu, Indonesia, tsunami using videos from social media. J. Geophys. Res. Solid Earth 2020, 125, e2019JB018675. [Google Scholar] [CrossRef]
- Versaci, M.; Laganà, F.; Morabito, F.C.; Palumbo, A.; Angiulli, G. Adaptation of an Eddy Current Model for Characterizing Subsurface Defects in CFRP Plates Using FEM Analysis Based on Energy Functional. Mathematics 2024, 12, 2854. [Google Scholar] [CrossRef]
- Laganà, F.; Pullano, S.A.; Angiulli, G.; Versaci, M. Optimized Analytical–Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use. Algorithms 2024, 17, 592. [Google Scholar] [CrossRef]
Challenge/Limitation | Details |
---|---|
Uncertainty in Predictions | The nonlinear dynamics of the Earth’s crust and ocean make precise predictions of tsunami timing, location, and magnitude challenging [34]. |
False Alarms | Seismic monitoring may trigger false tsunami warnings due to non-tsunamigenic seismic activity [23]. |
Missed Events | Tsunamis from non-seismic sources (e.g., landslides) may lack detectable precursors [35]. |
Data Gaps | Sparse monitoring networks in remote oceanic regions limit data availability [30]. |
Complex Fault–Ocean Interactions | Stress transfer and water displacement in submarine faults are difficult to model accurately [24]. |
Inaccurate Historical Records | Incomplete tsunami records hinder reliable risk assessments [36]. |
Paleotsunami Limitations | Geological records may be incomplete or misinterpreted, affecting long-term forecasts [25]. |
Precursor Ambiguity | Reliable precursors for tsunami events are not fully understood [19]. |
Fault Variability | Different fault segments produce tsunamis with varying characteristics [26]. |
Technological Barriers | Limited access to advanced sensors in developing regions reduce monitoring effectiveness [30]. |
Boundary Condition | Key Components |
---|---|
Geographical Constraints |
|
Seismic Magnitude Range |
|
Time Constraints and Temporal Processing |
|
Feature Selection and Data Preprocessing |
|
Computational Constraints |
|
Attribute | Values |
---|---|
Title | 1. M 7.0—18 km SW of Malango, Solomon Islands |
2. M 6.9—204 km SW of Bengkulu, Indonesia | |
3. M 7.0— | |
4. M 7.3—205 km ESE of Neiafu, Tonga | |
5. M 6.6— | |
Magnitude | 7.00, 6.90, 7.00, 7.30, 6.60 |
Date/Time | 22 November 2022 02:03, 18 November 2022 13:37, 12 November 2022 07:09, |
11 November 2022 10:48, 9 November 2022 10:14 | |
CDI | 8, 4, 3, 5, 2 |
MMI | 7, 4, 3, 5, 2 |
Alert | green, green, green, green, green |
Tsunami | 1, 0, 1, 1, 1 |
SIG | 768, 735, 755, 833, 670 |
NET | us, us, us, us, us |
NST | 117, 99, 147, 149, 131 |
Dmin | 0.51, 2.23, 3.12, 1.86, 5.00 |
Gap | 17.00, 34.00, 18.00, 21.00, 27.00 |
MagType | mww, mww, mww, mww, mww |
Depth | 14.00, 25.00, 579.00, 37.00, 624.46 |
Latitude | −9.80, −4.95, −20.05, −19.29, −25.59 |
Longitude | 159.60, 100.74, −178.35, −172.13, 178.28 |
Location | 1. Malango, Solomon Islands |
2. Bengkulu, Indonesia | |
3. – | |
4. Neiafu, Tonga | |
5. – | |
Continent | Oceania, –, Oceania, –, – |
Country | Solomon Islands, –, Fiji, –, – |
Model | Accuracy | Precision (Class 1) | Recall (Class 1) | F1 Score (Class 1) | Precision (Class 0) | Recall (Class 0) | F1 Score (Class 0) |
---|---|---|---|---|---|---|---|
Random Forest | 0.90 | 0.88 | 0.90 | 0.89 | 0.92 | 0.90 | 0.90 |
Logistic Regression | 0.89 | 0.87 | 0.89 | 0.88 | 0.88 | 0.87 | 0.88 |
K-Nearest Neighbors | 0.86 | 0.84 | 0.87 | 0.85 | 0.86 | 0.85 | 0.85 |
Support Vector Machine | 0.88 | 0.86 | 0.88 | 0.87 | 0.89 | 0.87 | 0.87 |
Aspect | Random Forest Model | Logistic Regression Model |
---|---|---|
Accuracy | 0.90 | 0.89 |
Precision | 0.88 | 0.87 |
Performance Strengths |
|
|
Validation Sources |
| Not specifically mentioned |
Limitations |
|
|
Future Application | Description | Key Impact |
---|---|---|
AI-Based Early Warning Systems | Machine learning models analyze seismic activity in real time and issue alerts before an earthquake strikes. | Enables automated shutdowns of power grids, transportation, and industrial operations, preventing secondary disasters. |
Urban Planning and Infrastructure Resilience | AI helps engineers and city planners to identify high-risk seismic zones and develop earthquake-resistant structures. | Enhances structural safety, reduces building collapse risks, and improves disaster preparedness strategies. |
AI-Driven Emergency Response | Machine learning models predict the most affected regions after an earthquake for efficient rescue deployment. | Facilitates faster rescue operations and better resource allocation in disaster-stricken areas. |
Hybrid AI and Geophysical Models | Combining deep learning with geophysical simulations for improved tsunami occurrence prediction. | Increases accuracy by incorporating real-time IoT sensor data into forecasting models. |
Global Seismic Monitoring Platforms | AI-powered cloud-based systems analyze earthquake risks across multiple regions. | Supports international disaster relief organizations with instant risk assessments. |
AI for Aftershock Prediction | AI detects hidden patterns in seismic activity to predict aftershocks. | Helps minimize post-earthquake hazards and enables better risk management. |
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Share and Cite
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
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 StyleSatish, 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 StyleSatish, 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