General Sewer Deterioration Model Using Random Forest

Hansen, Bolette Dybkjær; Jensen, David G.; Rasmussen, Søren Højmark; Tamouk, Jamshid; Uggerby, Mads; Moeslund, Thomas B.

doi:10.1109/ssci44817.2019.9002727

Cited by 5 publications

(8 citation statements)

References 21 publications

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“…The Random Forest model was implemented using the Python library scikit-learn [30]. The number of decision trees was set to 177 and the max depth was set to 26 based on Hansen et al [12]. The remaining hyperparameters were set to the default value.…”

Section: Model Selectionmentioning

confidence: 99%

“…XGBoost did not show better results than Random Forest. Furthermore, Random Forest is often used for deterioration modeling in sewer [12,13,19,31] and in water pipes [32]. For this reason, Random Forest was used for this study.…”

Section: Model Selectionmentioning

confidence: 99%

“…This introduces a selective survival bias in the data [1]. Other factors that complicate deterioration modelling are that the datasets in general are highly skewed, both according to the number of pipes in the different classes and according to the predictor variables [10][11][12]. Furthermore, the size of the natural variability between sewers is unknown.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of deterioration models have been developed; however, a lack of publicly available datasets due to privacy issues makes it difficult to compare the models [5]. Furthermore, the condition state (CS) is typically based on the local standard for CCTV inspection, which can be based on, for example, the European standard [13], Pipeline Assessment Certification Program [14], or a country specific standard [12,[15][16][17]. Moreover, in order to evaluate the deterioration models many authors tend to classify the multiclass or regression problem as a binary problem [12,13,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comprehensive Feature Analysis for Sewer Deterioration Modeling

Hansen

Rasmussen²,

Uggerby³

et al. 2021

Water

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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.

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Section: Model Selectionmentioning

confidence: 99%

Section: Model Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comprehensive Feature Analysis for Sewer Deterioration Modeling

Hansen

Rasmussen²,

Uggerby³

et al. 2021

Water

Self Cite

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“…Failure of pipe assets can cause service disruptions, threats to public health, and damage to surrounding buildings and infrastructure (Tscheikner-Gratl et al, 2019). Because the sewer infrastructure is underground, inspections and rehabilitation activities are expensive and labor-intensive, while the budget is often constrained (Fontecha et al, 2021;Hansen et al, 2019;Yin et al, 2020). Therefore, a maintenance strategy balancing reliability and costs is needed to achieve an adequate level of service.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 14th International Conference on Agents and Artificial Intelligence

2022

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