Groundwater resources play a key role in supplying urban water demands in numerous societies. In many parts of the world, wells provide a reliable and sufficient source of water for domestic, irrigation, and industrial purposes. In recent decades, artificial intelligence (AI) and machine learning (ML) methods have attracted a considerable attention to develop Smart Control Systems for water management facilities. In this study, an attempt has been made to create a smart framework to monitor, control, and manage groundwater wells and pumps using a combination of ML algorithms and statistical analysis. In this research, 8 different learning methods and regressions namely support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART), random forest (RF), artificial neural networks (ANNs), generalized regression neural network (GRNN), linear regression (LR), and K-nearest neighbors (KNN) regression algorithms have been applied to create a forecast model to predict water flow rate in Mashhad City wells. Moreover, several descriptive statistical metrics including mean squared error (MSE), root mean square error (RMSE), mean absolute error (MAE), and cross predicted accuracy (CPA) are calculated for these models to evaluate their performance. According to the results of this investigation, CART, RF, and LR algorithms have indicated the highest levels of precision with the lowest error values while SVM and MLP are the worst algorithms. In addition, sensitivity analysis has demonstrated that the LR and RF algorithms have produced the most accurate models for deep and shallow wells respectively. Finally, a Petri net model has been presented to illustrate the conceptual model of the smart framework and alarm management system.

地下水资源在满足许多社会的城市用水需求方面发挥着关键作用。在世界许多地方，水井为家庭、灌溉和工业用途提供可靠且充足的水源。近几十年来，人工智能（AI）和机器学习（ML）方法在开发水管理设施智能控制系统方面引起了相当大的关注。在本研究中，尝试结合机器学习算法和统计分析来创建一个智能框架来监测、控制和管理地下水井和水泵。在这项研究中，8种不同的学习方法和回归，即支持向量回归（SVR）、极限学习机（ELM）、分类回归树（CART）、随机森林（RF）、人工神经网络（ANN）、广义回归神经网络(GRNN)、线性回归 (LR) 和K最近邻 (KNN) 回归算法已用于创建预测模型来预测马什哈德市水井的水流量。此外，还计算了这些模型的几个描述性统计指标，包括均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）和交叉预测精度（CPA），以评估其性能。根据本次调查的结果，CART、RF 和 LR 算法显示出最高的精度水平和最低的误差值，而 SVM 和 MLP 是最差的算法。此外，敏感性分析表明，LR 和 RF 算法分别为深井和浅井生成了最准确的模型。 最后，提出了一个Petri网模型来说明智能框架和警报管理系统的概念模型。