A methodological approach for the development and verification of artificial neural networks based on an application to wave–structure interaction processes. Issue 3 (3rd July 2018)

• Record Type: Journal Article
• Authors: Formentin, Sara Mizar; Zanuttigh, Barbara
• Is Part Of: Coastal engineering journal. Volume 60:Issue 3(2018)
• Page Start: 260
• Page End: 279
• Publication Date: 2018-07-03
• Subjects: Artificial neural network -- input parameters -- training -- database -- generalization -- wave–structure interaction; Coastal engineering -- Periodicals; Coasts -- Periodicals; Hydraulic engineering -- Periodicals; Coast changes -- Periodicals; Shore protection -- Periodicals; Coast changes; Coastal engineering; Coasts; Hydraulic engineering; Shore protection; Periodicals; 627.58
• DOI: 10.1080/21664250.2018.1503402
• Languages: English
• ISSNs: 2166-4250

Abstract

ABSTRACT: This contribution presents a set of original and innovative methodological procedures for the development and the verification of artificial neural networks (ANNs). The procedures are conceived to provide a practical support in the build-up and verification processes of ANNs and address the following open issues: (i) the possibility to a-priori know the minimum size and the best assortment of the training database to optimize the ANN performance; (ii) the verification of the adequacy and effectiveness of the ANN input parameters, in order to avoid redundant information that may cause overfitting; and (iii) the assessment of the reliability of the ANN predictions when applied to new data. For this purpose, the procedures are applied to an existing ANN tool that the authors have recently designed for the prediction of the main parameters characterizing wave interaction with coastal and harbor structures. The main outcomes of the analyses suggest that (i) the size of training database depends on the ANN degrees of freedom; (ii) the use of three literature indicators allows to verify the appropriate selection of the input parameters; and (iii) the Euclidean distance among training data and new inputs can provide a useful a-priori indication of the reliability of the ANN predictions.