4.7 Article

Regression models for sediment transport in tropical rivers

Journal

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 28, Issue 38, Pages 53097-53115

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-021-14479-0

Keywords

Machine learning; Sediment transport; Total bed material load; Tropical rivers; Malaysia rivers

Funding

  1. REDAC, USM
  2. Public Service Department of Malaysia under the Hadiah Latihan Persekutuan (HLP) programme

Ask authors/readers for more resources

The study shows that machine learning models like EPR can provide better prediction accuracy for sediment transport models in Malaysian rivers, but further improvement is needed to better predict sediment transport.
The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available