Chapter 26 - Sediment transport with soft computing application for tropical rivers
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{HARUN:2023:HH,
-
author = "Mohd Afiq Harun and Aminuddin {Ab. Ghani} and
Saeid Eslamian and Chun Kiat Chang",
-
title = "Chapter 26 - Sediment transport with soft computing
application for tropical rivers",
-
editor = "Saeid Eslamian and Faezeh Eslamian",
-
booktitle = "Handbook of Hydroinformatics",
-
publisher = "Elsevier",
-
pages = "379--394",
-
year = "2023",
-
isbn13 = "978-0-12-821962-1",
-
DOI = "doi:10.1016/B978-0-12-821962-1.00017-9",
-
URL = "https://www.sciencedirect.com/science/article/pii/B9780128219621000179",
-
keywords = "genetic algorithms, genetic programming, Sediment
Transport, Fluvial environment, Soft computing,
Tropical rivers, Multiple linear regression, Machine
learning",
-
abstract = "This research revised the existing sediment transport
equation for rivers in Malaysia. The current equations
of Ariffin (2004) and Sinnakaudan et al. (2006) were
modified by using MLR and machine learning programs,
namely Evolutionary Polynomial Regression (EPR),
Multi-Gene Genetic Programming (MGGP), and M5 tree
model (M5P). Among the three machine learning models,
in terms of coefficient of determination (R2),
Nash-Sutcliffe coefficient of Efficiency (NSE), Root
Mean Square Error (RMSE), and Mean Absolute Error
(MAE), EPR were able to give the best prediction model
in the evidence of Revised Ariffin (2004) model (R2 =
0.922, NSE = 0.913, RMSE = 3.305, MAE = 1.552),
followed by MGGP (R2 = 0.787, NSE = 0.784, RMSE =
5.217, MAE = 3.054) and M5P (R2 = 0.786, NSE = 0.762,
RMSE = 5.467, MAE = 1.561). The trend was also the same
for Revised Sinnakaudan et al. (2006) whereby EPR had
an excellent prediction accuracy model (R2 = 0.884, NSE
= 0.848, RMSE = 4.377 ,MAE = 2.137), followed by MGGP
(R2 = 0.787, NSE = 0.784, RMSE = 5.207, MAE = 3.054)
and M5P (R2 = 0.622, NSE = 0.615, RMSE = 6.961, MAE =
1.994). In terms of Discrepancy Ratio (DR), only M5P of
both Revised Ariffin (2004) (73.46percent) and Revised
Sinnakaudan (2006) (73.36percent) produced better
results than MLR (66.36percent). However, the data did
not distribute well and is rather flattening at the
lower total bed material load rate. Machine learning is
excellent at improving the prediction distribution at
the high-value data but lacks accuracy compared to the
observed value at the lower data value. This is mainly
due to the type of regression algorithm used and sample
size used in this study",
- }
Genetic Programming entries for
Mohd Afiq Harun
Aminuddin Ab Ghani
Saeid Eslamian
Chun Kiat Chang
Citations