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.

中文翻译：

---

通过机器学习算法对水用户进行分类

水资源短缺和水危机问题（例如，水资源稳定、降雨量减少、城市人口增长加快以及城市和生活用水缺乏适当的管理）最近已成为一个重大问题。因此，检查德黑兰省供水和废水处理（TPWW）用户的行为，识别高消耗用户，并解释鼓励和教育他们更多正确用水模式的方法，有助于应对这场危机。本研究旨在使用机器学习算法为德黑兰的订阅者分类建立一个强大的模型。然后，新订户可以被分为相似的类别。因此，集成算法、支持向量机和神经网络被用来预测订阅者的行为。然后，集成算法集中的随机森林算法被认为是 TPWW 情况的最佳模型，训练和测试准确率分别为 99% 和 98%。