PL EN
A performance comparison of various artificial intelligence approaches for estimation of sediment of river systems
 
More details
Hide details
1
Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor, Malaysia
2
Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor, Malaysia
3
Faculty of Engineering, Technology and Built Environment, UCSI University, Jalan Puncak Menara Gading, Taman Connaught, Kuala Lumpur 56000, Malaysia
4
Asset Management Department, Generation Division, Tenaga Nasional Berhad, 59200 Kuala Lumpur, Malaysia
CORRESPONDING AUTHOR
Gasim Hayder   

Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor, Malaysia
 
 
KEYWORDS
TOPICS
ABSTRACT
Sediment is a universal issue that generated in the river catchment and affects the river flow, reservoir capacity, hydropower generation and dam structure. This paper aims to present the result of experimentation in sediment load estimation using various machine learning algorithms as a powerful AI approach. The data was collected from eight locations in upstream area of Ringlet reservoir catchment. The input variables are discharge and suspended solid. It is found that there is strong correlation between sediment and suspended solid with correlation coefficient of R=0.9. The developed ML model successfully estimate the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result is produced by SVM (ν-SVM version) where very low RMSE is generated for both training and testing dataset despite its more complicated hyperparameters setup. The results also show a promising application of machine learning for future prediction in hydro-informatic systems.