Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Abstract

Assessment of sewer condition is one of the critical steps in asset management and support investment decisions; therefore, condition assessment models with high accuracy are important that can help utility managers and other authorities correctly assess the current condition of the sewage network and effectively initiate maintenance and rehabilitation strategies. The main objective of this research is to assess the potential application of machine learning (ML) algorithms for predicting the condition of sewer pipes with a case study in Ålesund city, Norway. Nine physical factors (i.e., age, diameter, depth, slope, length, pipe type, material, pipe form, and connection type) and ten environmental factors (i.e., rainfall, geology, landslide area, building area, population, land cover, groundwater, traffic volume, distance to road, and soil type) were used to assess the sewer conditions employing seventeen ML models. After processing the sewer inspections, 1159 of 1449 individual pipelines were used to train the sewer condition model. The performance of ML models was validated using the 290 remaining inspected sewer pipes. The area under the Receiver Operating Characteristic (AUC-ROC) curve and accuracy (ACC) showed that the Random Forest (AUC-ROC = 77.6% and ACC = 78.3%) is a sensitive model for predicting the condition of sewer pipes in the study area. Based on the Random Forest model, maps of predicted conditions of sewers were generated that may be useful for utilities and water managers to establish future sewer system maintenance strategies.

Keywords

• Geographic information system
• machine learning
• predictive maintenance
• sewer network
• sewer condition assessment