Classification of water subscribers by machine learning algorithms

The problem of water scarcity and water crisis (e.g., stable water resources, reduced rainfall, increased urban population growth and lack of proper management of water consumption in urban and domestic water) has recently become a significant issue. Therefore, examining the behaviour of Tehran Prov...

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Bibliographic Details
Published in:Water and environment journal : WEJ 2024-02, Vol.38 (1), p.45-58
Main Authors: Dahesh, Arezoo, Tavakkoli‐Moghaddam, Reza, Tajally, AmirReza, Erfani‐Jazi, Aseman, Babazadeh‐Behestani, Milad
Format: Article
Language:English
Subjects:
Algorithms
bagging classifier
Classification
Consumption patterns
Domestic water
ensemble algorithms
Learning algorithms
Machine learning
Neural networks
Population growth
Rainfall
supervised learning
Support vector machines
Urban populations
urban water systems
Wastewater
Water consumption
Water crises
Water management
Water resources
Water scarcity
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Summary:The problem of water scarcity and water crisis (e.g., stable water resources, reduced rainfall, increased urban population growth and lack of proper management of water consumption in urban and domestic water) has recently become a significant issue. Therefore, examining the behaviour of Tehran Province Water and Wastewater (TPWW) subscribers to identify high‐consumption subscribers and explain methods to encourage and educate them more about the correct water consumption pattern can help deal with this crisis. This study aims to use machine learning algorithms to build a robust model for the classification of subscribers in Tehran. Then, new subscribers can be classified into similar classes. For this reason, ensemble algorithms, support vector machines and neural networks are used to predict subscribers' behaviour. Then, the random forest algorithm from the set of ensemble algorithms is considered the best model for the TPWW case with 99% and 98% in train and test accuracy, respectively.
ISSN:1747-6585
1747-6593
DOI:10.1111/wej.12892