<abstract xmlns="http://www.w3.org/1999/xhtml">

This study presents the use, and its advantages, of artificial intelligence methods to predict the discharge coefficient (<italic>Cw</italic>), considering the approach conditions of the labyrinth weir type D. The study suggests modifying the training and validation rates in AI tools, which are often fixed without proper justification in previous studies. Unlike most studies that use geometric dimensions as inputs, this work focuses on the approach conditions (the emplacement of the labyrinth weir and filling the alveoli upstream and downstream) of the labyrinth weir type D. The results, based on laboratory experiments, show that these modified inputs significantly impact the e ciency and cost of constructing the weir. Moreover, the <italic>C</italic>w predictions based on these inputs are highly satisfactory compared to laboratory test results. In terms of training and validation ratios, the study confirms that the optimal ratio is 70/30 for accurate and highly satisfactory predictions.
</abstract>

This study presents the use, and its advantages, of artificial intelligence methods to predict the discharge coefficient (Cw), considering the approach conditions of the labyrinth weir type D. The study suggests modifying the training and validation rates in AI tools, which are often fixed without proper justification in previous studies. Unlike most studies that use geometric dimensions as inputs, this work focuses on the approach conditions (the emplacement of the labyrinth weir and filling the alveoli upstream and downstream) of the labyrinth weir type D. The results, based on laboratory experiments, show that these modified inputs significantly impact the e ciency and cost of constructing the weir. Moreover, the Cw predictions based on these inputs are highly satisfactory compared to laboratory test results. In terms of training and validation ratios, the study confirms that the optimal ratio is 70/30 for accurate and highly satisfactory predictions.

Prediction of the Discharge Coefficient of a Labyrinth Weir Type D by an Artificial Neural Network Method

Department of Civil and Hydraulic Engineering

Laboratory LGCE, Faculty of Sciences and Technologies, University of Jijel

Laboratory of Hydraulic Planning and Environment

Archives of Hydro-Engineering and Environmental Mechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This study presents the use, and its advantages, of artificial intelligence methods to predict the discharge coefficient (Cw), considering the approach...

Prediction of the Discharge Coefficient of a Labyrinth Weir Type D by an Artificial Neural Network Method

Publié en ligne: 05 juil. 2024

Pages: 59 - 72

Reçu: 20 mars 2024

DOI: https://doi.org/10.2478/heem-2024-0004

Mots clés
ANN model, approach conditions, discharge coefficient, data splitting, labyrinth weir

© 2024 Faris Belaabed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Prediction of the Discharge Coefficient of a Labyrinth Weir Type D by an Artificial Neural Network Method

Publié en ligne: 05 juil. 2024

Pages: 59 - 72

Reçu: 20 mars 2024

DOI: https://doi.org/10.2478/heem-2024-0004

Mots clésANN model, approach conditions, discharge coefficient, data splitting, labyrinth weir

© 2024 Faris Belaabed et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
ANN model, approach conditions, discharge coefficient, data splitting, labyrinth weir