War Prediction Model

Overview

This project uses machine learning to predict the probability of armed conflict for any country, combining multiple data sources and academic research on conflict prediction.

Technical Documentation

World Conflict Risk Map

Interactive visualization showing conflict probability by country. Click any country or use the dropdown to see detailed risk analysis.

Model Architecture

The prediction model uses an ensemble of machine learning algorithms trained on historical conflict data:

flowchart TB
    subgraph Input["📥 Input Features"]
        E[Economic Indicators]
        P[Political Stability]
        M[Military Activity]
        G[Geographic Factors]
        H[Historical Conflicts]
    end
    
    subgraph Model["🤖 Ensemble Model"]
        XG[XGBoost]
        RF[Random Forest]
        LSTM[LSTM Temporal]
        LR[Logistic Regression]
    end
    
    subgraph Output["📊 Output"]
        PROB[Probability 0-1]
        CONF[Confidence Score]
        FACTORS[Factor Weights]
    end
    
    E --> XG & RF & LSTM & LR
    P --> XG & RF & LSTM & LR
    M --> XG & RF & LSTM & LR
    G --> XG & RF & LSTM & LR
    H --> XG & RF & LSTM & LR
    
    XG & RF & LSTM & LR --> PROB
    XG & RF & LSTM & LR --> CONF
    XG & RF & LSTM & LR --> FACTORS

Live Conflict Data Feed

Real-time conflict event visualization from the Geopolitical Risk API, powered by GDELT data.

Global Conflict Events (5-Year Trend)

Chart temporarily unavailable — API offline

Checking API status… · Data: GDELT conflict events · API Status ↗

Data Sources

Academic References

1. Hegre, H., et al. (2019). “Predicting Armed Conflict, 2010-2050.” Journal of Peace Research, 56(4). DOI: 10.1177/0022343319834048
2. Mueller, H., & Rauh, C. (2018). “Reading Between the Lines: Prediction of Political Violence.” American Political Science Review, 112(2). DOI: 10.1017/S0003055417000533
3. Ward, M. D., et al. (2013). “Learning from the Past and Predicting the Future.” International Interactions, 39(3). DOI: 10.1080/03050629.2013.782306
4. Muchlinski, D., et al. (2016). “Comparing Random Forest with Logistic Regression.” Political Analysis, 24(2). DOI: 10.1093/pan/mpv024

This is a research project by Stabilarity Research Hub. Predictions are based on publicly available data and academic models. Not financial or political advice.