Abstract
The aim of the work was to compare water consumption forecasting in two towns of different sizes. The objects of research were the town of Toruń and the town of Żnin in central Poland. Two models were built for each. The models were constructed using the multiple regression method. In constructing the models, explanatory variables determined by Principal Component Analysis (PCA) were used. The set of explanatory variables identified to construct each individual model differed. The models for Toruń obtained better forecast quality assessment criteria values. This was mainly due to the water supply system in the small town (Żnin) being less resilient to sudden, short-term changes in consumers’ water use. At the same time, the importance of the location of the meteorological stations from which data was taken to build the model was emphasised.
Highlights
The infrastructure for supplying water to urban residents is classed as critical
Explanatory variables determined by Principal Component Analysis (PCA) were used
Comparative analysis of the forecasting models for water consumption in the towns of different sizes showed that the models built for the larger of the two (Toruń) had better forecast accuracy
Summary
The infrastructure for supplying water to urban residents is classed as critical. even at the design and construction stage, it is necessary to consider all conditions that may negatively impact its operation. One basic requirement is to quantify water demand in a prospective period, taking into account the needs of all end-users. This is done using guidelines that provide a normative measure of water demand. Water consumption forecasts are an important additional element for facilitating the rational operation of water supply systems. Local conditions and the unique characters of objects of study (including size of water supply system) can be decisive in determining results, so are of significant importance. The aim of the work is to compare water consumption forecasts in two towns of different sizes. This was done using a multiple regression model. A set of explanatory variables was first defined using Principal Component Analysis (PCA)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
