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Article

Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data

1
Department of Civil and Architectural Engineering and Mechanics, University of Arizona, Tucson, AZ 85721, USA
2
School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(9), 3832; https://doi.org/10.3390/su12093832
Received: 3 April 2020 / Revised: 4 May 2020 / Accepted: 6 May 2020 / Published: 8 May 2020
(This article belongs to the Special Issue Safety in the Operation of Water Supply Systems)
Forecasting hydraulic data such as pressure and demand in water distribution system (WDS) is an important task that helps ensure efficient and accurate operations. Despite high-performance data prediction, missing data can still occur, making it difficult to effectively operate WDS. Though the pressure data are directly related to the rules of operation for pumps or valves, few studies have been conducted on pressure data forecasting. This study proposes a new missing and incomplete data control approach based on real pressure data for reliable and efficient WDS operation and maintenance. The proposed approach is: (1) application of source data from high-resolution, real-world pressure data; (2) development of a cross-domain artificial neural network (CDANN), combining the standard artificial neural networks (ANNs) and the cross-domain training approach for missing data control; and (3) analysis of standard data mining according to external factors to improve prediction accuracy. To verify the proposed approach, a real-world network located in South Korea was used, and the forecasting results were evaluated through performance indicators (i.e., overall, special points, and percentage errors). The performance of the CDANN is compared with that of standard ANNs, and CDANN was found to provide better predictions than traditional ANNs. View Full-Text
Keywords: water distribution system; missing data control; pressure data prediction; cross-domain artificial neural network; data categorization standard water distribution system; missing data control; pressure data prediction; cross-domain artificial neural network; data categorization standard
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MDPI and ACS Style

Choi, Y.H.; Jung, D. Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data. Sustainability 2020, 12, 3832. https://doi.org/10.3390/su12093832

AMA Style

Choi YH, Jung D. Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data. Sustainability. 2020; 12(9):3832. https://doi.org/10.3390/su12093832

Chicago/Turabian Style

Choi, Young H., and Donghwi Jung. 2020. "Development of Cross-Domain Artificial Neural Network to Predict High-Temporal Resolution Pressure Data" Sustainability 12, no. 9: 3832. https://doi.org/10.3390/su12093832

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