Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Transferability and comparability of sewer deterioration models – a case study on Norwegian sewer data
The adoption of emerging machine learning (ML) techniques in sewer deterioration modelling remains limited. This is primarily due to a lack of comparisons with popular models to evaluate if they perform superiorly and the extensive datasets required for ML training, which many small- to medium-sized municipalities do not possess. This study compares an emerging ML technique, random survival forest (RSF), with two established models: support vector machine (SVM) and GompitZ. Additionally, it examines the transferability of RSF models to different municipalities. The results indicate that both RSF and GompitZ have unique strengths and weaknesses, suggesting parallel use with statistical models. RSF’s performance was comparable to, or better than, SVM. Furthermore, globally trained RSF models, which use aggregated datasets of multiple municipalities, perform similarly to locally trained models, which are trained on representative municipal data. However, globally trained models are promising for municipalities lacking data or expertise to develop their own models.
Transferability and comparability of sewer deterioration models – a case study on Norwegian sewer data
The adoption of emerging machine learning (ML) techniques in sewer deterioration modelling remains limited. This is primarily due to a lack of comparisons with popular models to evaluate if they perform superiorly and the extensive datasets required for ML training, which many small- to medium-sized municipalities do not possess. This study compares an emerging ML technique, random survival forest (RSF), with two established models: support vector machine (SVM) and GompitZ. Additionally, it examines the transferability of RSF models to different municipalities. The results indicate that both RSF and GompitZ have unique strengths and weaknesses, suggesting parallel use with statistical models. RSF’s performance was comparable to, or better than, SVM. Furthermore, globally trained RSF models, which use aggregated datasets of multiple municipalities, perform similarly to locally trained models, which are trained on representative municipal data. However, globally trained models are promising for municipalities lacking data or expertise to develop their own models.
Transferability and comparability of sewer deterioration models – a case study on Norwegian sewer data
Skjelde, Joakim (Autor:in) / Daulat, Shamsuddin (Autor:in) / Roghani, Bardia (Autor:in) / Rokstad, Marius M. (Autor:in) / Tscheikner-Gratl, Franz (Autor:in)
Urban Water Journal ; 21 ; 1242-1253
25.11.2024
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
SEWER DETERIORATION PREDICTION SYSTEM AND SEWER DETERIORATION PREDICTION METHOD
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Development of prediction models for sewer deterioration
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Determining factors influencing sewer structural deterioration: Leuven (Belgium) case study
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Engineering Index Backfile | 1964