Department of water engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Department of Bioresource Engineering, Faculty of Agriculture and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada.
Environ Monit Assess. 2018 Jun 13;190(7):397. doi: 10.1007/s10661-018-6769-1.
Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R, and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.
氯化作用，即饮用水源的基本处理方法，广泛应用于配水系统中的水消毒和病原体消除。此后，在配水系统性能方面，正确预测氯的消耗非常重要。在这方面，与传统方法（例如数值方法）相比，数据挖掘技术——其具有发现因变量与自变量之间关系的能力——可以被视为替代方法。本研究检验了基于数据挖掘方法的三种关键方法在预测四个配水网络中氯含量方面的适用性。ANNs（人工神经网络，包括多层感知器神经网络 MLPNN 和径向基函数神经网络 RBFNN）、SVM（支持向量机）和 CART（分类和回归树）方法被用于估计伊朗克尔曼省三个村庄的配水网络中剩余氯的浓度。每天采集 3 年的生产水（流量）、氯消耗和剩余氯数据。使用多个统计标准（NSC、RMSE、R 和 SEP）对所研究的模型进行评估，结果表明，一般来说，MLPNN 具有预测氯含量的最大能力，其次是 CART、SVM 和 RBF-ANN。数据驱动方法在配水网络中的性能较弱，在某些情况下，可能归因于不当的氯化管理，而不是方法的能力。
