thedigitalbeni/water-demand-forecast-ai
Domain:
environment and energyCreator:
the
Host:
💧 An AI-powered water demand forecasting system built with Flask and Scikit-Learn. Developed during the Ethiopia AI Institution Summer Camp 2024 to predict seasonal water needs and support sustainable resource management.
# 💧 Water Demand Forecasting AI
> **Predicting Tomorrow's Needs, Today.** An AI-driven solution developed during the **Ethiopia AI Institution Program - AI Summer Camp 2024**.
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## 🌟 The Vision
Water is our most precious resource. This project was born from a simple but powerful question: *How can we use Artificial Intelligence to prevent water scarcity?*
Developed as a final project for the **AI Summer Camp 2024**, this tool uses Machine Learning to forecast water demand based on temporal patterns, helping communities and governments plan for a sustainable future.
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## 🧠 How the AI Works
The system uses a **Regression-based Machine Learning model** trained on historical/synthetic water consumption datasets.
### 🔬 The Prediction Logic:
The model analyzes three core features:
1. 📅 **Year:** Captures long-term population growth and infrastructure trends.
2. 🍂 **Month:** Identifies seasonal changes (dry vs. wet seasons).
3. ☀️ **Day:** Accounts for monthly usage cycles.
By processing these inputs through a trained Scikit-Learn pipeline, the system outputs the **Expected Water Demand** with high precision.
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## 🚀 Key Features
- **Intuitive Web UI:** Simple dashboard for quick predictions.
- **Real-Time Inference:** Instant results from the pickled ML model.
- **Containerized Deployment:** Fully Dockerized for "one-click" setup.
- **Future-Ready:** Designed to integrate with regional maps (GIS) and local Ethiopian datasets.
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## 🛠 Tech Stack
- **Backend:** Flask (Python)
- **AI/ML:** Scikit-Learn, Pandas, NumPy, Pickle
- **Frontend:** HTML5, CSS3, Modern JavaScript
- **DevOps:** Docker, Docker Compose, Poetry
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## 📂 Repository Structure
```text
├── static/ # CSS & UI Assets
├── templates/ # Flask HTML components
├── app.py # Main Flask Server & Inference Logic
├── model.pkl # Pre-trained Scikit-Learn Model
├── Dockerfile # Container configuration
└── pyproject.toml # Dependency management …