Optimization of the ANNs Predictive Capability Using the Taguchi Approach: A Case Study

Andrea Manni, Giovanna Saviano and Maria Grazia Bonelli
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Andrea Manni: Chemical Research 2000 S.r.l., Via S. Margherita di Belice 16, 00133 Rome, Italy
Giovanna Saviano: Department of Chemical, Materials and Environmental Engineering (DICMA), “La Sapienza” University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Maria Grazia Bonelli: Programming and Grant Office Unit (UPGO), Italian National Research Council (CNR), Piazzale Aldo Moro 7, 00185 Rome, Italy

Mathematics, 2021, vol. 9, issue 7, 1-16

Abstract: Artificial neural networks (ANNs) are a valid alternative predictive method to the traditional statistical techniques currently used in many research fields where a massive amount of data is challenging to manage. In environmental analysis, ANNs can analyze pollution sources in large areas, estimating difficult and expensive to detect contaminants from other easily measurable pollutants, especially for screening procedures. In this study, organic micropollutants have been predicted from heavy metals concentration using ANNs. Sampling was performed in an agricultural field where organic and inorganic contaminants concentrations are beyond the legal limits. A critical problem of a neural network design is to select its parametric topology, which can prejudice the reliability of the model. Therefore, it is very important to assess the performance of ANNs when applying different types of parameters of the net. In this work, based on Taguchi L 12 orthogonal array, turning experiments were conducted to identify the best parametric set of an ANNs design, considering different combinations of sample number, scaling, training rate, activation functions, number of hidden layers, and epochs. The composite desirability value for the multi-response variables has been obtained through the desirability function analysis (DFA). The parameters’ optimum levels have been identified using this methodology.

Keywords: artificial neural network; Design of Experiment (DoE); parametric design; forecasting; environmental pollution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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