This project presents an advanced AI-driven disaster management and business continuity system that integrates deep learning and ensemble learning for predictive risk analytics.
Traditional systems rely on static contingency planning. This platform introduces a dynamic, real-time, data-driven framework capable of analyzing complex, high-dimensional data to predict, classify, and respond to potential disruptions before they occur.
- 🧠 Hybrid AI Model (CNN + Ensemble Learning)
- 📡 Multi-source Data Processing (satellite, sensors, logs)
⚠️ Real-time Risk Prediction & Alerts- 📊 Interactive Dashboard for Decision-Making
- 🔍 Explainable AI (SHAP) for Transparency
- 🌍 Scalable Architecture (Public & Private Sector)
flowchart TD
A[Data Sources] --> B[Preprocessing]
B --> C[CNN Feature Extraction]
B --> D[Structured Data Pipeline]
C --> E[Feature Fusion]
D --> E
E --> F[Ensemble Models]
F --> G[Prediction Output]
G --> H[Dashboard & Alerts]
F --> I[SHAP Explainability]
- Python 3.10+
- TensorFlow / Keras → Deep Learning (CNN)
- Scikit-learn → Random Forest, SVM
- XGBoost → Gradient Boosting
- NumPy & Pandas → Data Processing
- Streamlit / Dash → Interactive dashboards
- Matplotlib / Seaborn → Data visualization
- FastAPI / Flask → REST APIs
- Docker → Containerization
- GitHub Actions → CI/CD (optional)
- SHAP (SHapley Additive Explanations)
├── data/
│ ├── raw/
│ ├── processed/
│
├── models/
│ ├── cnn/
│ ├── ensemble/
│
├── notebooks/
│ ├── experimentation.ipynb
│
├── src/
│ ├── data_preprocessing.py
│ ├── feature_engineering.py
│ ├── cnn_model.py
│ ├── ensemble_models.py
│ ├── shap_explainability.py
│ ├── api.py
│
├── dashboard/
│ ├── app.py
│
├── results/
│ ├── metrics/
│ ├── visualizations/
│
├── requirements.txt
├── Dockerfile
└── README.md
# Clone repository
git clone https://github.com/Lenny-Lewis/Resili-AI.git
cd Resili-AI
# Create virtual environment
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txtpython src/cnn_model.py
python src/ensemble_models.pypython src/api.pystreamlit run dashboard/app.py| Metric | Hybrid Model |
|---|---|
| Accuracy | High |
| Precision | Optimized |
| Recall | Strong (High-risk detection) |
| ROC-AUC | Excellent |
- 📉 Risk probability scores
⚠️ Alert triggers for high-risk scenarios- 🔍 SHAP visualizations for feature importance
- 🌊 Climate disaster prediction (floods, droughts)
- 🔐 Cyberattack risk detection
- 🏗️ Infrastructure failure analysis
- 🦠 Pandemic outbreak forecasting
- 🏢 Enterprise continuity planning
This project advances:
- AI-driven resilience engineering
- Predictive governance models
- Explainable AI in critical systems
- Transition from reactive → proactive risk management
- ☁️ Cloud Deployment (AWS / Azure / GCP)
- 📡 Real-time streaming (Apache Kafka)
- 🌍 GIS & Geospatial analytics integration
- 🤖 Reinforcement Learning for adaptive response
- 📱 Mobile alert systems
# .github/workflows/main.yml
name: CI Pipeline
on: [push]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Install Dependencies
run: pip install -r requirements.txt
- name: Run Tests
run: pytest# Build image
docker build -t disaster-ai .
# Run container
docker run -p 8000:8000 disaster-ai✔ Production-ready architecture ✔ Real-world problem solving ✔ Advanced ML + Deep Learning integration ✔ Explainable AI implementation ✔ Scalable system design
Lenny Lewis AI Developer | Data Scientist | Systems Engineer
- 🌐 GitHub: https://github.com/Lenny-Lewis
- 💼 Portfolio: Add your portfolio link here
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MIT License © 2026 Lenny Lewis