README.md

metadata

title: Liquefaction Probability Calculator
emoji: 🌊
colorFrom: blue
colorTo: red
sdk: streamlit
sdk_version: 1.29.0
app_file: app.py
pinned: false

Liquefaction Probability Calculator

This Streamlit app calculates the probability of soil liquefaction based on SPT data, soil type data, and earthquake data using a deep learning model with SHAP explanations.

Model Architecture

The model uses a combination of:

• Attention mechanisms for processing SPT and soil type data
• FFT-based attention for earthquake data processing
• Dense layers for combining features and making predictions

Input Data Format

The app expects an Excel file (.xlsx) with three sheets:

1. 'SPT' - Contains Standard Penetration Test data (10 values)
2. 'soil_type' - Contains soil type classification data (10 values)
3. 'EQ_data' - Contains earthquake acceleration time series data (5000 values)

Required Columns

• SPT sheet: SPT values, water table depth, epicentral distance, depth, distance to water, VS30
• soil_type sheet: Soil type classification values
• EQ_data sheet: Earthquake acceleration time series

How to Use

1. Upload your Excel file using the file uploader
2. Click "Calculate Liquefaction Probability"
3. View the results:
   • Liquefaction probability for each sample
   • SHAP analysis explaining the predictions and feature importance

Results Interpretation

• Probability values range from 0 to 1
• Values closer to 1 indicate higher liquefaction probability
• SHAP plots show how each feature contributes to the prediction:
  • Red bars indicate features increasing liquefaction probability
  • Blue bars indicate features decreasing liquefaction probability

Technical Details

• Model: Deep learning model with attention mechanisms
• Input features: SPT values, soil types, earthquake data, and site characteristics
• Output: Binary classification (liquefaction probability)
• Explanation: SHAP (SHapley Additive exPlanations) values

Dependencies

Main dependencies include:

• streamlit==1.29.0
• pandas==2.1.4
• numpy==1.24.3
• torch==2.1.2
• matplotlib==3.8.2
• shap==0.44.0
• scikit-learn==1.3.2
• openpyxl==3.1.2

See requirements.txt for the complete list of dependencies.