Classification of water subscribers by machine learning algorithms

• 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
• ISSN: 1747-6585; 1747-6593
• DOI: 10.1111/wej.12892

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.