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Nepal Earthquake Trend Analysis and Seismic Risk Prediction Based on Severity Classification

Overview

This project analyzes historical earthquake data of Nepal to understand long-term seismic trends and applies machine learning techniques to classify earthquake severity and estimate seismic risk levels.

Using earthquake records from the USGS catalog (1900–2026) and NEMRC catalog (1994-2025), the project combines exploratory data analysis, geospatial visualization, and supervised machine learning to identify seismic patterns and support data-driven risk assessment.

The project focuses on risk estimation and pattern analysis, not exact earthquake prediction.

Objectives

  • Analyze long-term earthquake trends in Nepal.
  • Study spatial distribution of earthquakes.
  • Classify earthquakes based on severity levels.
  • Use classification results to estimate seismic risk.
  • Visualize earthquake distribution using geospatial tools.

Dataset

  • Source: United States Geological Survey (USGS) & National Earthquake Monitoring and Research Center (NEMRC)
  • Region: Nepal and surrounding areas
  • Time Range: 1900 – 2026
  • Typical Features:
    • Date and time
    • Latitude
    • Longitude
    • Depth
    • Magnitude
    • Location description

Project Components

1. Trend Analysis (EDA)

  • Earthquake frequency over time
  • Magnitude distribution analysis
  • Depth vs magnitude relationships
  • Pre and post major earthquake comparisons

2. Geospatial Visualization

  • Earthquake distribution maps
  • Severity-based mapping
  • Regional seismic concentration analysis

3. Machine Learning Model

A supervised learning model is used to classify earthquake severity.

Example severity classes:

  • Low Severity
  • Moderate Severity
  • High Severity

Models evaluated may include:

  • Logistic Regression
  • Decision Tree
  • Random Forest

4. Seismic Risk Estimation

Risk levels are derived from severity and frequency patterns to identify relatively high-risk regions or periods.

Tools & Technologies

  • Python
  • Pandas & NumPy
  • Matplotlib
  • GeoPandas
  • Scikit-learn
  • Jupyter Notebook

Project Workflow

  1. Data collection
  2. Data cleaning and preprocessing
  3. Exploratory data analysis
  4. Feature engineering
  5. Model training and evaluation
  6. Risk interpretation and visualization

Limitations

  • Exact earthquake prediction is scientifically impossible.
  • Results represent statistical patterns and risk estimation only.

Expected Outcome

  • Understanding of Nepal’s earthquake trends
  • Severity classification model
  • Visual seismic risk insights
  • ML pipeline demonstration for seismic data

Future Improvements

  • Incorporate real-time seismic feeds
  • Improve spatial risk modeling
  • Apply time-series forecasting techniques
  • Build an interactive dashboard

Author

Roman Shrestha
Madhav Tharu
Electronics, Communication & Information Engineering
Tribhuvan University, IoE Pashchimanchal Campus