README.md

---
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.