Author(s): Peyman Yousefi; Gholamreza Naser; Hadi Mohammadi
Linked Author(s):
Keywords: Disaggregation; Embedding Dimension; Peachland; Random Cascade; Temporal Scale; Water Demand
Abstract: A comprehensive understanding of water demand and its availability is needed for decision makers to effectively manage their resources and understand related risks. Historical data plays a crucial role in developing an integrated plan for water distribution systems’management. The key is to provide highresolution temporal-scale of demand data in urban areas. In the literature, many studies of water demand forecasting are available, while a vast majority of them are focused on monthly scales. Since monitoring of time series is a time consuming and costly procedure, the popularity of disaggregation methods is more desirable recently. The objective of this research is to transfer low resolution into high-resolution data using random cascade disaggregation and non-linear deterministic methods. This study defined a new technique to apply previously proposed random cascade method to disaggregate continuous data of the city of Peachland. The accuracy of the results was more than 90%. It represented an acceptable application of the models. The proposed approach helps operators have access to daily demand without acquiring high-resolution values. Although the disaggregated values may not be exactly equal with observed values, it offers a practical solution for the low equipped WDS and may lead to less drinking water-related problems.
Year: 2018