Abstract
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.
Highlights
Since the advent of the Fourth Industrial Revolution, techniques for estimating demand- and pressure-based data are improving for facets of water distribution system (WDS) including planning, design, operation, and strategic decisions [1,2,3,4]
Artificial neural networks (ANNs) and fuzzy logic techniques of forecasting water demand are advanced methods that are classified as nonparametric approaches [2,11,12,13], which are applied to both long- and short-term demand forecasting
The Galsan block pressure data applied in this study was obtained from June 2019 to November 2019 at 1-min intervals, and the daily average temperature was obtained from Korea Water Management Information System (WAMIS)
Summary
Since the advent of the Fourth Industrial Revolution, techniques for estimating demand- and pressure-based data are improving for facets of water distribution system (WDS) including planning, design, operation, and strategic decisions [1,2,3,4]. Data forecasting studies have recently been conducted for efficient system operation and maintenance of water utilities [19,20,21,22,23] These studies have performed water demand or pressure forecasting that generated theoretical synthetic data for real-time pump operation. Sim et al [37] applied and compared several imputation models (i.e., the performance of listwise deletion, mean imputation, group mean imputation, predictive mean imputation, hot-deck, k-NN, and k-means clustering) in the hypothetical computing application dataset to identify the best approach These approaches estimate that if any value is missing, it is assumed to be zero or the representative values (e.g., mean value) that are considered the neighborhood values in the training process. The proposed pressure data forecasting approach can be applied for effective operation in real-world WDS that do not have sufficient number of installed pressure meters
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