Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)
department or school: Applied Science, Faculty of
department or school: Non UBC
department or school: Engineering, School of (Okanagan)
data provider: DSpace
identifier citation: Water 12 (3): 753 (2020)
creator: Yousefi, Peyman
creator: Courtice, Gregory
creator: Naser, Gholamreza
creator: Mohammadi, Hadi
issued: 2020-03-27T18:37:43Z
issued: 2020-03-09
description: This study investigated urban water consumption complexity using chaos theory to improve forecasting performance to help optimize system management, reduce costs and improve reliability. The objectives of this study were to (1) investigate urban water distribution consumption complexity and its role in forecasting technique performance, (2) evaluate forecasting models by periodicity and lead time, and (3) propose a suitable forecasting technique based on operator applications and performance through various time scales. An urban consumption dataset obtained from the City of Kelowna (British Columbia, Canada) was used as a test case to forecast future consumption values using varying lead times under different temporal scales to identify models which may improve forecasting performance. Chaos theory techniques were employed to inform model optimization. This study attempted to address the paucity of studies on chaos theory applications in water consumption forecasting. This was accomplished by applying non-linear approximation, dynamic investigation, and phase space reconstruction for input variables, to improve the accuracy in various periodicity and lead time. To reconstruct the phase space, lag time was calculated using average mutual information for daily resolution as 17 days to reconstruct the phase space. The optimum embedding dimension and correlation exponent for the phase space were 18 and 3.5, respectively. Comparing the results, the non-linear local approximation model provided the best performance. The forecasting horizon for the models was 122 days. Moreover, phase space reconstruction improved the accuracy of the models for the different lead times. The findings of this study may improve forecasting performance and provide evidence to support further investigation of the chaotic behaviour of water consumption values over different time scales.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/73823
note: waterArticleNonlinear Dynamic Modeling of Urban WaterConsumption Using Chaotic Approach (Case Study:City of Kelowna)Peyman Yousefi 1 , Gregory Courtice 1 , Gholamreza Naser 2,* and Hadi Mohammadi 11 School of Engineering, The University of British Columbia, Kelowna, BC V1V1V7, Canada;pe.yousefi@alumni.ubc.ca (P.Y.); greg.courtice@alumni.ubc.ca (G.C.); hadi.mohammadi@ubc.ca (H.M.)2 Associate Professor, School of Engineering, Shippensburg University of Pennsylvania,Shippensburg, PA 17257, USA* Correspondence: gnaser@ship.edu; Tel.: +1-717-477-1359Received: 11 November 2019; Accepted: 11 February 2020; Published: 9 March 2020Abstract: This study investigated urban water consumption complexity using chaos theory to improveforecasting performance to help optimize system management, reduce costs and improve reliability.The objectives of this study were to (1) investigate urban water distribution consumption complexityand its role in forecasting technique performance, (2) evaluate forecasting models by periodicityand lead time, and (3) propose a suitable forecasting technique based on operator applications andperformance through various time scales. An urban consumption dataset obtained from the City ofKelowna (British Columbia, Canada) was used as a test case to forecast future consumption valuesusing varying lead times under different temporal scales to identify models which may improveforecasting performance. Chaos theory techniques were employed to inform model optimization.This study attempted to address the paucity of studies on chaos theory applications in waterconsumption forecasting. This was accomplished by applying non-linear approximation, dynamicinvestigation, and phase space reconstruction for input variables, to improve the accuracy in variousperiodicity and lead time. To reconstruct the phase space, lag time was calculated using averagemutual information for daily resolution as 17 days to reconstruct the phase space. The optimumembedding dimension and corre…
has type: Article
is shown at: 10.14288/1.0389664
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/w12030753
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
subject: water consumption
subject: chaos theory
subject: local approximation
subject: Kelowna
subject: gene expression programming
type: Text
identifier URI: http://hdl.handle.net/2429/73823