Application of black-box models based on artificial intelligence for the prediction of chlorine and TTHMs in the trunk network of Bogotá, Colombia

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Laura Enríquez, Laura González, J. Saldarriaga
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引用次数: 0

Abstract

The chlorine and total trihalomethane (TTHM) concentrations are sparsely measured in the trunk network of Bogotá, Colombia, which leads to a high uncertainty level at an operational level. For this reason, this research assessed the prediction accuracy for chlorine and TTHM concentrations of two black-box models based on the following artificial intelligence techniques: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) as a modelling alternative. The simulation results of a hydraulic and water quality analysis of the network in EPANET and its multi-species extension EPANET-MSX were used for training the black-box models. Subsequently, the Threat Ensemble Vulnerability Assessment-Sensor Placement Optimization Tool (TEVA-SPOT) and Evolutionary Polynomial Regression-Multi-Objective Genetic Algorithm (EPR-MOGA-XL) were jointly applied to select the most representative input variables and locations for predicting water quality at other points of the network. ANNs and ANFIS were optimized with a multi-objective approach to reach a compromise between training performance and generalization capacity. The ANFIS models had a higher mean Training and Test Nash–Sutcliffe Index (NSI) in contrast with ANNs. In general, the models had a satisfactory mean prediction performance. However, some of them did not achieve suitable Test NSI values, and the prediction accuracy for different operational statuses was limited.
基于人工智能的黑箱模型在哥伦比亚波哥大主干网氯和TTHMs预测中的应用
在哥伦比亚波哥大主干网中,氯和总三卤甲烷(TTHM)浓度很少测量,这导致在操作层面上具有很高的不确定性。基于此,本研究基于以下人工智能技术:人工神经网络(ann)和自适应神经模糊推理系统(ANFIS)作为建模替代方案,评估了两种黑盒模型对氯和TTHM浓度的预测精度。利用EPANET及其多物种扩展EPANET- msx网络的水力和水质分析仿真结果对黑盒模型进行训练。随后,联合应用威胁集成漏洞评估-传感器放置优化工具(TEVA-SPOT)和进化多项式回归-多目标遗传算法(EPR-MOGA-XL)选择最具代表性的输入变量和位置,用于预测网络其他点的水质。采用多目标方法对人工神经网络和人工神经系统进行优化,以达到训练性能和泛化能力的折衷。与人工神经网络相比,ANFIS模型具有更高的平均训练和测试Nash-Sutcliffe指数(NSI)。总体而言,模型具有令人满意的均值预测性能。然而,其中一些没有达到合适的Test NSI值,并且对不同运行状态的预测精度受到限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Hydroinformatics
Journal of Hydroinformatics 工程技术-工程:土木
CiteScore
4.80
自引率
3.70%
发文量
59
审稿时长
3 months
期刊介绍: Journal of Hydroinformatics is a peer-reviewed journal devoted to the application of information technology in the widest sense to problems of the aquatic environment. It promotes Hydroinformatics as a cross-disciplinary field of study, combining technological, human-sociological and more general environmental interests, including an ethical perspective.
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