A Performance Comparison of Various Artificial Intelligence Approaches for Estimation of Sediment of River Systems

Abstract

Sediment is a universal issue that is 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 was found that there is strong correlation between sediment and suspended solid with correlation coefficient of R = 0.9. The developed ML model successfully estimated the sediment load with competitive results from ANN, Decision Tree, AdaBoost and SVM. The best result was produced by SVM (v-SVM version) where very low RMSE was 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.

Prediction of COD in Industrial Wastewater Treatment Plant using Artificial Neural Network

“…Arti cial Neural Networks (ANNs), as computational techniques, are non-linear models designed to mimic the functionality and decision-making processes of the human brain 4 . ANNs have found increasing application in various environmental modeling studies 5,6 and investigations into water quality issues 7,8 .…”

Prediction of COD in Industrial Wastewater Treatment Plant using an Artificial Neural Network

“…Arti cial neural networks (ANNs), as computational techniques, are nonlinear models designed to mimic the functionality and decision-making processes of the human brain 4 . ANNs have been increasingly applied in various environmental modeling studies 5,6 and investigations into water quality issues 7,8 . In the domain of wastewater treatment plant (WWTP) modeling, ANNs have been successfully employed for the prediction of WWTP parameters [9][10][11] , process control [12][13][14][15] , and the estimation of output parameters and characteristics 16,17 .…”

Prediction of COD in industrial wastewater treatment plant using an artificial neural network

“…Among the widely and successfully used methods are various Artificial neural networks (ANNs) 5 , response surface methodology (RSM) 7,8 , radial basis function (RBF) 8 , and fuzzy logic 9 .ANNs, as computational techniques, are nonlinear models designed to mimic the functionality and decisionmaking processes of the human brain 10 . ANNs have been increasingly applied in various environmental modeling studies 11,12 and investigations into water quality issues 13,14 . In the domain of wastewater treatment plant (WWTP) modeling, ANNs have been successfully employed for the prediction of WWTP parameters [15][16][17] , process control [18][19][20][21] , and the estimation of output parameters and characteristics 22,23 .…”