Investigation of KNN and Decision Tree Methods’ Efficiency in Developing Prediction Models for Sewer Pipes-Reference-Cited by-同舟云学术

Investigation of KNN and Decision Tree Methods’ Efficiency in Developing Prediction Models for Sewer Pipes

Published:2023-08-10 Issue: Volume: Page:
ISSN:
Container-title:Pipelines 2023
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Author:

Shirkhanloo Salar¹²³⁴²,Arjun Madhuri¹²³⁴²,Najafi Mohammad¹²³⁴²,Kaushal Vinayak¹²³⁴²,Kaur Kawalpreet¹²³⁴²,Jibreen Ahmad¹²³⁴²

Affiliation:

1. Clinical Assistant Professor, Univ. of North Texas, Denton, TX.

2. Ph.D. Candidate, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX.

3. Professor and Director, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX.

4. Assistant Professor of Instruction, Dept. of Civil Engineering, Center for Underground Infrastructure Research and Education, Univ. of Texas at Arlington, Arlington, TX.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485033.018

Reference13 articles.

1. Ebrahimi, M., and Hojat Jalali, H. Spatial Variability Effects of Wall Erosion on Assessment of Reinforced Concrete Sanitary Sewer Pipes (RCSSPs). In Tran-SET 2022 (pp. 330–338).

2. Automated Condition Assessment of Sanitary Sewer Pipes Using LiDAR Inspection Data

3. Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1–758). New York: springer.

4. A Review on Evaluation Metrics for Data Classification Evaluations

5. Kaur, K. K., Najafi, M. M., Ghalambor, S. S., and Jibreen, A. (2022). Evaluation of a Hybrid Polyurea Spray Applied Pipe Lining for Structural Pipe Rehabilitation.