Author(s): Francesco Del Punta; Hauke Sonnenberg; Antoine Daurat; Yoann Sadowski; Frederic Cherqui; Nicolas Caradot

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Keywords: No keywords

Abstract: This study explores the application of a machine learning model, specifically a Random Forest classifier, to predict the condition of uninspected pipes using available structural, operational, and environmental data. Originally developed for Berlin, Germany, the model has been adapted and applied to the sewer network of Lausanne, Switzerland. Model performance was evaluated using custom metrics, with results compared to previous applications in Berlin. Despite challenges related to class imbalance, the model demonstrated promising accuracy, supporting its potential as a decision-making tool for inspection prioritization.

DOI: https://doi.org/10.71573/z36dgw29

Year: 2025