Investigation of KNN and Decision Tree Methods’ Efficiency in Developing Prediction Models for Sewer Pipes
Publication: Pipelines 2023
ABSTRACT
Wastewater collection systems become less effective over time, necessitating ongoing changes and the creation of asset management frameworks on the side of utility owners to keep their assets operating as intended. Since inspecting every sewer pipe is time-consuming and costly, developing prediction models that can anticipate sewer pipes’ current and future state is vital. In this research, two machine learning methods including k-nearest neighbors (KNN) and decision tree have been investigated in order to create a prediction model for sewer pipes of two cities in USA. The data which is used is a combined data from Tampa, Florida, and Dallas, Texas, containing nine physical and environmental variables. F1-score and area under the curve (AUC) were calculated to compare the effectiveness of developed models. It was concluded that the decision tree model performed better than the other. The overall F1-score for the decision tree model was 0.73 and for KNN was 0.70.
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Pipelines 2023
Pages: 158 - 169
History
Published online: Aug 10, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Asset management
- Business management
- Computer programming
- Computing in civil engineering
- Ecosystems
- Environmental engineering
- Financial management
- Infrastructure
- Lifeline systems
- Management methods
- Pipeline management
- Pipeline systems
- Pipes
- Practice and Profession
- Sewer pipes
- Sewers
- Steel pipes
- Trees
- Vegetation
- Wastewater management
- Water treatment
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