https://github.com/11sshukla/tsunami_alert_system
Tsunami Risk Prediction System This project implements a machine learning system to predict tsunami risk levels for three states. By analyzing relevant data, the system categorizes risk into low, moderate, and high. Overview: Utilizing a Lightgbm ML algorithm, this system assesses tsunami likelihood.
https://github.com/11sshukla/tsunami_alert_system
folium folium-maps lightgbm machine-learning pandas python seaborn sklearn
Last synced: 2 months ago
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Tsunami Risk Prediction System This project implements a machine learning system to predict tsunami risk levels for three states. By analyzing relevant data, the system categorizes risk into low, moderate, and high. Overview: Utilizing a Lightgbm ML algorithm, this system assesses tsunami likelihood.
- Host: GitHub
- URL: https://github.com/11sshukla/tsunami_alert_system
- Owner: 11SShukla
- Created: 2025-02-25T12:37:46.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-02-25T13:04:08.000Z (2 months ago)
- Last Synced: 2025-02-25T13:47:33.526Z (2 months ago)
- Topics: folium, folium-maps, lightgbm, machine-learning, pandas, python, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Tsunami Risk Prediction System
This project develops a machine learning-based system to predict tsunami risk levels for three states, categorizing them into low, moderate, and high risk. By analyzing relevant data, the system aims to provide early warnings and support disaster preparedness efforts.
**Overview:**
The system employs a Lightgbm ML algorithm to analyze data and predict the likelihood of a tsunami. The risk is classified into three levels:
* **Low Risk:** Minimal probability of a tsunami.
* **Moderate Risk:** Potential for a tsunami event.
* **High Risk:** Significant likelihood of a tsunami.
**Potential Applications:**
* Early warning systems.
* Disaster preparedness.
* Risk assessment.
**Future Enhancements:**
* Real-time data integration.
* Improved accuracy.
* Expand to more states.