As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.
“Waterworks Standards”, Korea Water and Wastewater Association, p2, 2010.
S M Lee and D S Kang, “Water pipe deterioration assessment using ANN-Clustering”, Journal of the Korean Water Resources Association, pp959-960, 2018
Y R Choi and K H Kim, “Artificial Intelligence Overview and Application Cases”, ie Magazine , p23, 2016.
S B Cho and J H Kim, “Computational Complexity of Artificial Neural Networks”, Korean Institute of Information Scientists and Engineers, pp.315-318, 1989.
Y I Cho1, M J Oh, Y S Seok, S J Lee and M I Roh, “Resistance Estimation of a Ship in the Initial Hull Design Using Deep Learning”, Korean Journal of Computational Design and Engineering, pp.203-210, 2019.
Cybenko, George. "Approximation by Superpositions of a Sigmoidal Function." Mathematics of Control, Signals, and Systems, pp.303-314, 1989.
S R Jo, H N Sung and B H Ahn, “A Comparative Study on the Performance of SVM and an Artificial Neural Network in Intrusion Detection”, Journal of the Korea Academia-Industrial cooperation Society, pp.703-711, 2016.
C W Lee, S J Hong, J S Park and D G Yoo, “Assessment of Water Supply Pipes Deterioration Based on Deep Learning”, Journal of the Korean Society of Civil Engineers, pp.62-63, 2020.
“Water Pipeline Precision Investigation Manual(Draft)”, Ministry of Environment, pp.10-24, 2020.
K H Han, Y J Ryu, T S Kim and J H Heo, “Input Variables Selection of Artificial Neural Network Using Mutual Information”, Korea Water Resources Assocition, pp.85-87, 2010.
H K Park, J S Lee, W H Song and S S Park, “Technology Forecasting using Patent Outlier Remova”, Journal of the Korean Institute of Intelligent Systems, pp.177-184, 2018.
Y M Go, S W Go, “Alleviation of Vanishing Gradient Problem Using Parametric Activation Functions”Journal of the Korea Information Processing Society, pp.407-420, 2021.
“A Report on the Schematic Plan for the Improvement of the Aged Water Pipeline”, Korea Water Resources Corporation, 2021.
J Y Seo, “Deep Learning TensorFlow Textbook”, Gilbut, p.25, 2021.
S M An, “Deep learning architectures and applications”Journal of the Korea Intelligent Information Systems Society, pp.127-142, 2016.