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Title: K-Means Clustering Based Model for Fair Water Distribution of Urban Regions Depending on Consumption
Authors: Zaid Alkelani, Mohammad$AAUP$Palestinian
Awad, Mohammed $AAUP$Palestinian
Keywords: Fair Water Distribution
K-means Clustering Algorithm
Urban Water Demand
Issue Date: 4-Nov-2021
Publisher: International Journal of Engineering Research and Applications
Citation: DOI: 10.9790/9622-1110044353
Series/Report no.: Vol. 11, Issue 10, (Series-IV) October 2021, pp. 43-53;
Abstract: The Middle East countries are suffering from problems of the increasing demand for water in light of the scarcity of resources to obtain sufficient quantities and satisfy the needs of citizens of different needs in different fields. These issues drive the water supply companies and authorities to seek other ways to deal with customers and to provide useful policies suitable for the available resource. This paper proposes a smart clustering method to distribute the water in urban regions with the aim to develop a new local classification of neighbourhoods water sustainability according to justice ways to automatically classify them in different categories suitable to their consumption; this methodology uses intelligent clustering techniques that depend on the historical water consumption in each region, rather than the classical methods that distribute the water in a stationary way regardless the quantity of the water used or needs in this region. The data of each region is processed and clustered using the K-Means clustering algorithm to identify the fair distribution of water as a function of water supply days for each region. Our study offered a look at available water resources and the quantities in order to help the water authority to evaluate the challenges and find the alternatives to satisfy the citizens. The K means clustering algorithm achieved superior results in adjusting, rearranging and clarifying the characteristics of water consumption by regrouping similar objects according to quantities and pattern of consumption within clear and organized clusters. Our results showed that this technique will be awesome for the self-classification for every neighbourhood water consumer based on historical data related to water demand belongs to these neighbourhoods
Appears in Collections:Faculty & Staff Scientific Research publications

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