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All Journal Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil
Muhammad Cahyono
Faculty of Civil and Environmental Engineering, Institute of Technology Bandung
Civil Engineering, Building, Construction & Architecture Engineering Transportation

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Explicit Artificial Neural Networks For Predicting Gradually Varied Flow Muhammad Cahyono
Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil Vol 11 No 1 (2022): Cantilever
Publisher : Department of Civil Engineering and Planning, Faculty of Engineering, Sriwijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35139/cantilever.v11i1.131

Abstract

The ANN procedure was used to develop an explicit equation for predicting the water level profile in a gradually varied flow. The equation consists of a series of hyperbolic tangent functions, with the number of series being the same as the number on the node in the hidden layer. The ANN model consists of 3 layers: the input layer consists of four nodes, the hidden layer has seven nodes and one node in the output layer. The input parameters used are parameters related to distance, discharge, roughness, and depth of flow at the downstream end of the channel. The output parameter is the flow depth at various points. The model has been used to estimate the water level profile for different flow conditions. The comparison between the explicit ANN model and the numerical model results is satisfactory. The models can be extended to study more complex flows and non-prismatic channels. The model is promising as a tool in decision support.
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