Comprehensive Feature Analysis for Sewer Deterioration Modeling
| Autor: | Bolette Dybkjær Hansen, David Getreuer Jensen, Mads Uggerby, Thomas B. Moeslund, Søren Højmark Rasmussen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
lcsh:Hydraulic engineering
Computer science Deterioration model Geography Planning and Development 0211 other engineering and technologies 020101 civil engineering 02 engineering and technology Aquatic Science computer.software_genre Biochemistry inspection strategy 0201 civil engineering lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 021105 building & construction Sanitary sewer parameter importance Dimension (data warehouse) sewer Feature analysis Water Science and Technology Parameter importance lcsh:TD201-500 feature analysis Sewer Inspection Strategy deterioration model Identification (information) Ageing Feature (computer vision) ageing Data mining computer |
| Zdroj: | Water Volume 13 Issue 6 Hansen, B D, Rasmussen, S H, Uggerby, M, Moeslund, T B & Jensen, D G 2021, ' Comprehensive Feature Analysis for Sewer Deterioration Modeling ', Water, vol. 13, no. 6, 819 . https://doi.org/10.3390/w13060819 Water, Vol 13, Iss 819, p 819 (2021) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w13060819 |
| Popis: | Timely maintenance of sewers is essential to preventing reduced functionality and breakdown of the systems. Due to the high costs associated with inspecting a sewer system, substantial research has focused on sewer deterioration modeling and identification of the most useful features. However, there is a lack of consensus in the findings. This study investigates how the feature importance depends on the definition of bad pipes and how the feature importance changes between utilities with similar data bases. A dataset containing 318,457 pipes from 35 utilities with a condition state (CS) ranging from one to four was used. The dataset was cleaned, and a backward step analysis (BSA) was applied to two ways of binarizing the CS. Additionally, a BSA was applied for each utility with ≥100 pipes in CS four. The results showed that a selective definition of bad pipes reduced the performance and changed the order of which features contributed the most. In each case, either year of construction, age, groundwater, year of rehabilitation, or dimension was the most important feature. On average 6.5 features contributed to the utility-specific models. The feature analysis was sensitive to the inspection strategy, the size of the dataset, and interdependency between the features. |
| Databáze: | OpenAIRE |
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