This project focuses on the development of an innovative Water Leakage Detection software leveraging Machine Learning (ML) techniques to enhance the monitoring and maintenance of water supply infrastructure. By harnessing Python for data analysis and utilizing libraries such as NumPy, Pandas, and Scikit-learn, we've built a robust backend system capable of processing and analyzing large datasets to identify potential leakages within the water supply network.
Our ML models are trained on historical data related to water flow rates, pressure levels, and other relevant parameters to establish patterns indicative of leakages. Through continuous learning and optimization, the system can accurately detect and localize leaks, enabling proactive intervention to minimize water loss and prevent potential infrastructure damage. Additionally, the software provides insights and predictive analytics to aid decision-making processes for infrastructure maintenance and resource allocation.
- Machine Learning Algorithms: Implement sophisticated ML algorithms for detecting and localizing water leakages within the supply network.
- Data Analysis and Processing: Utilize Python and associated libraries for efficient data analysis, preprocessing, and feature extraction.
- Real-time Monitoring: Enable real-time monitoring of water supply parameters to promptly identify anomalies and potential leakages.
- Interactive Web Interface: Develop a user-friendly web interface using React for visualization of leak detection results and system insights.
- Scalability and Customization: Design the software with scalability in mind to accommodate varying sizes and complexities of water supply infrastructure, with options for customization based on specific requirements.
For detailed installation instructions, usage guidelines, and technical documentation, please refer to any relevant documentation files in the repository.