Table of contents
Introduction
Water managers are increasingly overwhelmed by the manual workload involved in cleaning and correcting pipeline data. These tasks are essential for reliable network operations. PipeFusion automates this process, allowing water managers to focus on analysing data and generating valuable insights for better water management.
The problem
Manual correction of pipeline data is time-consuming and error-prone, often leading to inefficiencies and delays in maintenance, diverting water managers' attention from more strategic tasks. Water managers must frequently verify and fix data discrepancies, including geometry and connectivity errors, before moving on to higher-value tasks like data analysis and predictive modelling.
How PipeFusion solves the problem
PipeFusion uses advanced machine learning to automate the process of data cleaning and correction, ensuring that water managers work with accurate and up-to-date information. The system integrates real-time data, making corrections to errors such as:
- Geometry and Connectivity Issues: PipeFusion detects and corrects missing or incorrect connections between pipeline nodes.
- Flow Direction Errors: The system corrects directional inconsistencies, ensuring water flows as intended across the network.
By eliminating manual data cleaning, PipeFusion frees up water managers to focus on higher-order tasks such as predictive analysis, optimising network performance, and improving decision-making.
Results and benefits
Utilities implementing PipeFusion have reported:
- 99% reduction in data errors, resulting in more reliable network data for analysis.
- 30% decrease in the time spent on manual data corrections.
- Enables water managers to focus on higher-value activities, such as network optimisation and incident management.
Conclusion
PipeFusion reduces human error in data cleaning, allowing water managers to redirect their focus toward actionable insights and improving network performance, while significantly reducing the risk of errors in operations.