- P-ISSN1738-6764
- E-ISSN2093-7504
- KCI
ISSN : 1738-6764
Development of AI-Based Water Pipe Aging Condition Evaluation Algorithm
Min, Byung-Won
Oh, Yong-Sun
Abstract
As interest in the development of artificial intelligence technology in the water supply field increases, an artificial neural network algorithm that can predict improved decision ratings through repetitive learning using results of aging pipe condition evaluation data should be developed and the most reliable prediction model should be presented through a verification process. An algorithm was developed to predict pipeline ratings by updating weights through backpropagation so that 12 items of indirect evaluation data according to the 2020 Han River Basin's basic plan could be pre-processed, such as standardizing input values and applying artificial neural network algorithms. As a result of algorithm accuracy verification, if there is sufficient data and the repetitive learning and upgrades are continuously conducted, the prediction accuracy will become higher and a reliable AI-based water supply pipe condition evaluation model to be used nationwide will be developed in the future.
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