Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments
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. Hen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/23/3141 |
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| author | Wing Son Loh Ren Jie Chin Lloyd Ling Sai Hin Lai Eugene Zhen Xiang Soo |
| author_facet | Wing Son Loh Ren Jie Chin Lloyd Ling Sai Hin Lai Eugene Zhen Xiang Soo |
| author_sort | Wing Son Loh |
| collection | DOAJ |
| description | 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 <i>x</i> and <i>y</i> 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%. |
| first_indexed | 2024-03-10T04:48:46Z |
| format | Article |
| id | doaj.art-cf67a22862e6449db9354d58e45c7999 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T04:48:46Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-cf67a22862e6449db9354d58e45c79992023-11-23T02:46:39ZengMDPI AGMathematics2227-73902021-12-01923314110.3390/math9233141Application of Machine Learning Model for the Prediction of Settling Velocity of Fine SedimentsWing Son Loh0Ren Jie Chin1Lloyd Ling2Sai Hin Lai3Eugene Zhen Xiang Soo4Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaDepartment of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaDepartment of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, MalaysiaSedimentation 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 <i>x</i> and <i>y</i> 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%.https://www.mdpi.com/2227-7390/9/23/3141back propagation neural network (BPNN)fine sedimentsradial basis function neural network (RBFNN)self-organizing feature map (SOFM)settling velocity |
| spellingShingle | Wing Son Loh Ren Jie Chin Lloyd Ling Sai Hin Lai Eugene Zhen Xiang Soo Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments Mathematics back propagation neural network (BPNN) fine sediments radial basis function neural network (RBFNN) self-organizing feature map (SOFM) settling velocity |
| title | Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments |
| title_full | Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments |
| title_fullStr | Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments |
| title_full_unstemmed | Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments |
| title_short | Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments |
| title_sort | application of machine learning model for the prediction of settling velocity of fine sediments |
| topic | back propagation neural network (BPNN) fine sediments radial basis function neural network (RBFNN) self-organizing feature map (SOFM) settling velocity |
| url | https://www.mdpi.com/2227-7390/9/23/3141 |
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