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Hydraulic informed multi-layer perceptron for estimating discharge coefficient of labyrinth weir.
- Source :
-
Engineering Applications of Artificial Intelligence . Aug2023:Part C, Vol. 123, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Data science techniques (DST) are the most popular approaches for estimating the discharge coefficient (C d) of labyrinth weirs (LW). This study proposes a hydraulic-informed multi-layer perceptron (HI-MLP) by incorporating the effects of hydraulic phenomena on C d , which were previously neglected due to their immeasurability. The HI-MLP is not a black box and selects its internal parameters considering the nape behavior attributes. HI-MLP is trained using Levenberg–Marquardt and Genetic algorithms, resulting in HI-MLP-LM and HI-MLP-GA, which are compared against adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and standard MLP. Evaluating the models through randomly selected testing data shows that HI-MLP-GA is the most accurate, with an MAEP of 0.732%, while all techniques also had acceptable performances. Conversely, when estimating C d for LWs with excluded intermediate and extrapolated geometries, only HI-MLP-LM and HI-MLP-GA could predict accurate values with average MAEP of 1.94% and 0.62%, respectively. This value in ANFIS and SVR was equivalent to 27.01% and 6.47%, respectively. Furthermore, hydraulic-informed techniques could be trained with at least a 25% smaller dataset, while the robustness analysis indicated that they are less prone to overfitting. HI-MLP-GA has the highest computational effort and is almost five times slower than HI-MLP-LM. Nevertheless, due to the disappearance of the vanishing gradient and considering the higher generalizability of HI-MLP-GA, GA is still a more desirable learning algorithm than LM. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 123
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 164285197
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.106435