Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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SES 1-4-3: Asset Management Location: HSB2 Session Chair: Franz Tscheikner-Gratl Session Chair: Emma Madeleine Girot | |
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5:15pm - 5:30pm
Application of a predictive machine-learning model to forecast sewer’s pipes condition. A case study in Lausanne, Switzerland 1: Kompetenzzentrum Wasser Berlin, Deutschland; 2: 2Institut National des Sciences Appliquées, Lyon, France; 3: Ville de Lausanne - Service de l ‘eau, Rue de Genève 36, 7416-1001 Lausanne, Switzerland 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. | |