Laura Enriquez, Laura González, Juan G. Saldarriaga

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

  • Atmospheric Science
  • Geotechnical Engineering and Engineering Geology
  • Civil and Structural Engineering
  • Water Science and Technology
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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.

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