Version 1
: Received: 9 October 2020 / Approved: 14 October 2020 / Online: 14 October 2020 (10:21:09 CEST)
How to cite:
Norouzi, R.; Daneshfaraz, R.; Abraham, J.; Sihag, P. Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen. Preprints2020, 2020100296. https://doi.org/10.20944/preprints202010.0296.v1
Norouzi, R.; Daneshfaraz, R.; Abraham, J.; Sihag, P. Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen. Preprints 2020, 2020100296. https://doi.org/10.20944/preprints202010.0296.v1
Norouzi, R.; Daneshfaraz, R.; Abraham, J.; Sihag, P. Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen. Preprints2020, 2020100296. https://doi.org/10.20944/preprints202010.0296.v1
APA Style
Norouzi, R., Daneshfaraz, R., Abraham, J., & Sihag, P. (2020). Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen. Preprints. https://doi.org/10.20944/preprints202010.0296.v1
Chicago/Turabian Style
Norouzi, R., John Abraham and Parveen Sihag. 2020 "Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen" Preprints. https://doi.org/10.20944/preprints202010.0296.v1
Abstract
Drops are the most important and most common energy dissipator in irrigation networks and erodible canals and consequently, their performance must be well understood. This study was designed to evaluate the capability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, MLR, and LR to predict the relative energy dissipation (∆E/E0) in vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation with variable discharge, varying drop height, and porosity of the horizontal screens. Parameters yc/h, yd/yc, and p are considered as input variables and ∆E/E0 is the output variable. The efficiency of models was compared using Taylor's diagram, Box Plot of the applied error distribution, correlation coefficient (CC), mean absolute error (MAE) and root-mean-square error (RMSE). Results indicate that the performance of the ANFIS_gbellmf based model with CC value of 0.9953, RMSE value of 0.0069 and MAE value of 0.0042 was superior to other applied models. Also, the linear regression model with CC=0.9933, RMSE=0.0083, and MAE= 0.0067performs better than the multiple linear regression model in this study. Results of a sensitivity study suggest that yc/h is the most effective parameter for predicting ∆E/E0.
Keywords
Drop; relative energy dissipation; screen; soft computing
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
