Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments
Document Type
Article
Publication Date
1-1-2021
Abstract
Sedimentation management is one of the primary factors in achieving sustainable development of water resources. However, due to difficulties in conducting in-situ tests, and the complex nature of fine sediments, it remains a challenging task when dealing with issues related to settling velocity. Hence, the machine learning model appears as a suitable tool to predict the settling velocity of fine sediments in water bodies. In this study, three different machine learning-based models, namely, the radial basis function neural network (RBFNN), back propagation neural network (BPNN), and self-organizing feature map (SOFM), were developed with four hydraulic parameters, including the inlet depth, particle size, and the relative x and y particle positions. The five distinct statistical measures, consisting of the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), mean value accounted for (MVAF), and total variance explained (TVE), were used to assess the performance of the models. The SOFM with the 25 × 25 Kohonen map had shown superior results with RMSE of 0.001307, NSE of 0.7170, MAE of 0.000647, MVAF of 101.25%, and TVE of 71.71%. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
Back propagation neural network (BPNN), Fine sediments, Radial basis function neural network (RBFNN), Self-organizing feature map (SOFM), Settling velocity
Divisions
fac_eng
Funders
Ministry of Higher Education (MoHE), Malaysia through the Fundamental Research Grant Scheme project (FRGS/1/2021/WAB07/UTAR/02/1),Universiti Tunku Abdul Rahman Research Fund (IPSR/RMC/UTARRF/2020-C2/C04),Partly supported by the Brunsfield Engineering Sdn. Bhd., Malaysia (Brunsfield 8013/0002 & 8126/0001)
Publication Title
Mathematics
Volume
9
Issue
23
Publisher
MDPI