Abstract
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.
Highlights
Global water scarcity concerns are increasing due to climate change, urban development, population growth, industrial development, economic expansion, and the cost of drinking water [1].It is imperative that governments invest in integrated management plans that address consequences of water problems such as scarcity of available water resources, sufficient distribution and pipeline maintenance
This study presented a novel approach to improve the accuracy of models in forecasting daily water consumption values
The residential water consumption dataset from the City of Kelowna residential daily water consumption values
Summary
A robust operation of urban drinking water supply systems requires future water consumption values to inform the development of an efficient water consumption management plan to mitigate anticipated stressors on the system. Forecasting consumption values in short-, mid-, and long-term (i.e., less than a week, a week to a month, a month to a year or more, respectively) time periods play a crucial role in water distribution systems’ (WDS) daily operation basis by informing important factors such as optimized pumping, pipeline maintenance, minimizing energy and water supply cost, improving system reliability and the quality of allocated water [7,8,9]. Information about future water consumption helps related authorities develop an integrated, efficient plan that reduces long-term supplier and consumer water stress. Consumption forecasting requires a balance of optimizing the quantity of input variables with improvements in forecasting accuracy
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
