Mathematical Graph Analysis for Water Distribution Network Monitoring
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Solution Overview
Problem
Current systems for monitoring water utility networks fail to effectively utilize Geographical Information Systems (GIS) and asset management data for automated analysis and decision-making, leading to inefficiencies in leak detection and network maintenance.
Innovation Solution
The method involves retrieving GIS and asset management data, generating mathematical graph elements, and creating connections between them to form a mathematical graph for comprehensive analysis, which includes identifying flow monitoring zones, determining optimal meter locations, and prioritizing maintenance areas based on historical leak data using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If GIS data and asset management data are merely displayed without automated analysis, then system complexity is reduced and ease of operation is maintained, but productivity and measurement precision are significantly limited
Solution Approach 1:
The system performs automated analysis of GIS and asset management data using mathematical graphs and machine learning models without requiring manual intervention. The system self-services by automatically detecting anomalies, predicting leaks, and generating maintenance priorities, thereby improving productivity while managing complexity through automation rather than manual processes
Solution Approach 2:
A mathematical graph structure serves as an intermediary between raw GIS/asset data and analysis results. The graph models network topology and relationships, enabling automated analysis without requiring complex direct processing of raw data, thus improving productivity while abstracting away system complexity
2Measurement precision
If comprehensive GIS data is collected and analyzed using mathematical graphs, then measurement precision and anomaly detection accuracy are improved, but loss of time for data processing increases
Solution Approach 1:
The system pre-processes GIS and asset management data into mathematical graph structures in advance, organizing network topology and relationships before analysis is needed. This preliminary action enables faster real-time anomaly detection by having data ready in an analysis-ready format, improving measurement precision without proportionally increasing processing time during critical operations
Solution Approach 2:
Manual data analysis processes are replaced with automated machine learning models that analyze mathematical graphs. This substitution enables comprehensive data analysis with high accuracy while reducing manual processing time, as algorithms can process data much faster than human operators
3Reliability
If machine learning models are used for leak prediction and maintenance prioritization, then productivity and reliability are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The mathematical graph serves as an intermediary that structures GIS and asset data in a format suitable for machine learning analysis. The graph models network topology, relationships between assets, and flow patterns, enabling reliable leak prediction while abstracting the complexity of data preparation and feature engineering from the implementation process
Solution Approach 2:
The mathematical graph structure serves multiple functions: it models network topology, enables anomaly detection, supports leak prediction, and facilitates maintenance prioritization. This universal data structure reduces implementation complexity by providing a single framework that handles multiple analysis tasks rather than requiring separate systems for each function
Data Source
AI summary
A computerized method for modeling a utility network. The method includes retrieving geographical information system (GIS) data, asset management data, and sensor archive data of one or more assets of the utility network. The method also includes generating one or more mathematical elements from the one or more assets and creating probable connections between the one or more mathematical graph elements based on the GIS and asset data. A mathematical graph is generated based on the probable connections, the mathematical graph including one or more asset characteristics of the one or more assets. Analysis is performed on the utility network using the mathematical graph and the mathematical graph data is stored for use by other systems within the utility network.