Related Experiment Video
Updated: May 17, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Integrating machine learning and multi-criteria decision analysis for health risk management in water distribution
Uchit Sangroula1, Victor Viñas2,3, Michael Odhiambo4
1Department of Architecture and Civil Engineering, Water Environment Technology, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden. uchit@chalmers.se.
Abstract:
Leakages and breaks in water distribution networks (WDNs) cause significant water losses and pose health risks due to pathogen intrusion. The Water Safety Plan (WSP), developed by the World Health Organization (WHO), provides a comprehensive framework for identifying, assessing, and controlling risks within water supply systems. This study demonstrates the application of the WSP framework through a case study of a WDN in Sweden. Pipe break probabilities were estimated using three classification models: Logistic regression, random forest, and extreme gradient boosting (XGBoost), while hydraulic and health consequences were evaluated using hydraulic modelling and Quantitative Microbial Risk Assessment (QMRA) to quantify the overall health risk. A Multi-Criteria Decision Analysis (MCDA) approach, specifically the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was utilized to prioritize risk mitigation strategies through breakage and leakage control measures. The proposed approach integrates predictive modelling, consequence evaluation, and decision analysis, offering a structured method for water utilities in prioritizing interventions and improving the overall safety and reliability of WDNs.
Related Concept Videos
Dimensional Analysis
In fluid mechanics, dimensional...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Multiple Pipe Systems
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
Steps in Outbreak Investigation
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
