Comparison of Bottom-Up and Top-Down Procedures for Water Demand Reconstruction

Abstract

This paper presents a comparison between two procedures for the generation of water demand time series at both single user and nodal scales, a top-down and a bottom-up procedure respectively. Both procedures are made up of two phases. The top-down procedure adopted includes a non-parametric disaggregation based on the K-nearest neighbours approach. Therefore, once the temporal aggregated water demand patterns have been defined (first phase), the disaggregation is used to generate water demand time series at lower levels of spatial aggregation (second phase). In the bottom-up procedure adopted, demand time series for each user and for each time step are generated applying a beta probability distribution with tunable bounds or a gamma distribution with shift parameter (first phase). Then, a Copula based re-sort is applied to the demand time series generated to impose existing rank cross-correlations between users and at all temporal lags (second phase). For the sake of comparison, two case studies were considered, both of which are related to a smart water network in Naples (Italy). The results obtained show that the bottom-up procedure performs significantly better than the top-down procedure in terms of rank-cross correlations at fine scale. However, the top-down procedure showed a better performance in terms of skewness and rank cross-correlation when the aggregated demands were considered. Finally, the level of aggregation in nodes was found to affect the performance of both the procedures considered.

Bottom-Up Generation of Peak Demand Scenarios in Water Distribution Networks

“…In this context, it must be noted that limitation is not inherent in the top-down approach but rather in the way practitioners apply it. In fact, there are notable examples in the scientific literature [28,29], where this limitation does not appear in the context of the top-down generation of demand time series. However, the top-down generation of peak demand scenarios has not been much explored so far.…”

Generic Framework for Downscaling Statistical Quantities at Fine Time-Scales and Its Perspectives towards Cost-Effective Enrichment of Water Demand Records

“…Stochastic disaggregation entails the generation of a synthetic series that reproduces the marginal and stochastic characteristics at a finer scale and is fully consistent with (i.e., sum up exactly to) the given values at a coarser level. In the field of water demand modelling, such approaches have been implemented in a stochastic top-down allocation concept to disaggregate the total water demands to the nodes of a system (e.g., [45,75]), but the temporal disaggregation of water demand series at fine temporal scales has received much less attention. In this vein, the cost-effective enhancement of fine-resolution water demand measurements can be favored by relevant developments from the field of hydrometeorological modelling (e.g., [68,74]), which also reports similar practical applicability in the enhancement of limited and short rainfall series at hourly and sub-hourly scales.…”

Usage of IoT Framework in Water Supply Management for Smart City in Nepal

“…The result displayed that bottom-up approach procedure performs better than top-down in terms of skewness and rank crosscorrelation at fine scale whereas; top-down procedure did better in terms of skewness and rank cross-correlation when aggregated demand was considered. Nevertheless, when the aggregation was considered at nodes there was affect in the performance of both top-down and bottom-up procedure [51].…”