Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil
Vol 11 No 1 (2022): Cantilever

Explicit Artificial Neural Networks For Predicting Gradually Varied Flow

Muhammad Cahyono (Faculty of Civil and Environmental Engineering, Institute of Technology Bandung)



Article Info

Publish Date
07 Jun 2022

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.

Copyrights © 2022






Journal Info

Abbrev

cantilever

Publisher

Subject

Civil Engineering, Building, Construction & Architecture Engineering Transportation

Description

Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil is a research journal and study in civil engineering that presents research results in the fields of building and structural engineering, transportation, water resources engineering and management, geotechnical engineering, construction ...