National Center for Studies and Research on Water and Energy (CNEREE), Cadi Ayyad Univ., P.O. Box 511, Marrakech, Morocco.
Lab. of Water, Biodiversity and Climate Change, Faculty of Sciences Semlalia, Cadi Ayyad Univ., P.O. Box 2930, Marrakech, Morocco.
J Environ Qual. 2021 Jan;50(1):144-157. doi: 10.1002/jeq2.20176. Epub 2020 Dec 9.
This study aims to find the most accurate machine learning algorithms as compared to linear regression for prediction of fecal coliform (FC) concentration in the effluent of a multi-soil-layering (MSL) system and to identify the input variables affecting FC removal from domestic wastewater. The effluent quality of two different designs of the MSL system was evaluated and compared for several parameters for potential reuse in agriculture. The first system consisted of a single-stage MSL (MSL-SS), and the second system consisted of a two-stage MSL (MSL-TS). The concentration of FC in the effluent of the MSL-TS system was estimated by three machine learning algorithms: artificial neural network (ANN), Cubist, and multiple linear regression (MLR). The accuracy of the models was measured by comparing the real and predicted values. Significant (p < .001) improvements were noted for the removal of pollutants by the MSL-TS system compared with the MSL-SS system. Overall, the water quality parameters investigated complied with FAO irrigation standards. The predictive performance of the models has been compared and evaluated using several metrics. The results revealed that the ANN model yielded a superior predictive performance (R = .953), followed by the Cubist model (R = .946) and the MLR technique (R = .481). Based on the accurate model (ANN), the degree of influence of each predictor was investigated, and the results show that total suspended solids and pH have proved to be more useful for predicting FC concentrations.
本研究旨在比较线性回归，找到最准确的机器学习算法，用于预测多土层（MSL）系统出水中粪大肠菌群（FC）的浓度，并确定影响生活污水中 FC 去除的输入变量。评估和比较了两种不同设计的 MSL 系统的出水质量，以评估几个参数在农业中的潜在再利用价值。第一个系统由单级 MSL（MSL-SS）组成，第二个系统由两级 MSL（MSL-TS）组成。通过三种机器学习算法：人工神经网络（ANN）、Cubist 和多元线性回归（MLR）来估算 MSL-TS 系统出水中 FC 的浓度。通过比较实际值和预测值来衡量模型的准确性。与 MSL-SS 系统相比，MSL-TS 系统对污染物的去除有显著的（p<.001）改善。总体而言，所研究的水质参数符合粮农组织灌溉标准。通过多种指标比较和评估了模型的预测性能。结果表明，ANN 模型具有较好的预测性能（R=.953），其次是 Cubist 模型（R=.946）和 MLR 技术（R=.481）。基于准确的模型（ANN），研究了每个预测器的影响程度，结果表明总悬浮固体和 pH 值对预测 FC 浓度更有用。
