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Smart Water Leak Detection & Monitoring Dashboard

Overview

The Smart Water Leak Detection System leverages Machine Learning and Interactive Dashboards to detect and monitor water leakages in pipelines. It uses IoT sensor data (pressure, flow rate, vibration, temperature, etc.) and provides real-time leak detection, what-if analysis, batch predictions, and geo-visualization.

This project consists of:

  • ML Models (Jupyter Notebook): Training and evaluation (water_leak_detection.ipynb).
  • Frontend Dashboard (Streamlit App): Interactive monitoring (app.py).

Features

  • Leak Prediction – Detect leakage for individual pipeline inputs using trained ML models (Random Forest, Decision Tree, Logistic Regression, SVM).
  • What-if Analysis – Simulate different sensor values with sliders and instantly see prediction changes.
  • Zone Map & Geo Analysis – Interactive GIS map showing leak locations, active/dead pipes, and zone-wise analytics.
  • Batch Prediction – Upload a CSV dataset for bulk leak detection with downloadable results.
  • Analytics & Insights – Feature importance, model comparisons, and system insights.
  • AI Assistant (Groq LLaMA Model) – Ask AI for leak trends, maintenance insights, and generate plots dynamically.
  • About Page – Quick overview of the system.

Tech Stack

  • Frontend: Streamlit
  • Visualization: Plotly, Folium (maps), Streamlit-Folium
  • Machine Learning: Scikit-learn (Random Forest, Decision Tree, Logistic Regression, SVM)
  • AI Assistant: Groq API (LLaMA models)
  • Data Handling: Pandas, NumPy

Project Structure

Smart-Water-Leak-Detection/
│── frontend/                 # Streamlit or Flask/Django app
│    └── app.py
│
│── ml_model/                 # Notebooks + model files
│    └── water_leak_detection.ipynb
│
│── data/                     # Datasets
│    ├── location_aware_gis_leakage_dataset.csv
│    └── testing.csv
│── requirements.txt          # Dependencies
│── .gitignore
│── README.md

Installation & Setup

Clone Repository

git clone https://github.com/your-username/Smart-Water-Leak-Detection.git
cd Smart-Water-Leak-Detection

Create Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate     # On Linux/Mac
venv\Scripts\activate        # On Windows

Install Dependencies

pip install -r requirements.txt

Run ML Notebook (Optional - Training Models)

Open water_leak_detection.ipynb in Jupyter Notebook / JupyterLab:

jupyter notebook water_leak_detection.ipynb

This will allow you to retrain and export .pkl models.

Run Streamlit App

streamlit run app.py

The app will open in your browser at:

http://localhost:8501

How to Use

Navigate using Sidebar Menu:

  • Predict Leakage Input pipeline parameters & predict.
  • What-if Analysis Adjust sliders for simulations.
  • Zone Map & Geo Analysis Visualize leaks on interactive maps.
  • Batch Prediction Upload CSV for bulk predictions.
  • Analytics & Insights Explore feature importance & model comparison.
  • AI Assistant Ask questions and request plots.

Dataset Requirements (for Batch Prediction & AI Assistant):

  • Columns: Pressure, Flow_Rate, Temperature, Vibration, RPM, Operational_Hours, Latitude, Longitude, Zone, Block, Pipe, Location_Code, Leakage_Flag(optional)

Model Performance

  • Random Forest: 0.98 accuracy (best performing)
  • Decision Tree: 0.91 accuracy
  • SVM: 0.90 accuracy
  • Logistic Regression: 0.89 accuracy