Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments

• Published in: Mathematics (Basel) Vol. 9; no. 23; p. 3141
• Main Authors: Loh, Wing Son, Chin, Ren Jie, Ling, Lloyd, Lai, Sai Hin, Soo, Eugene Zhen Xiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2021
• Subjects: Algorithms; Artificial neural networks; Back propagation networks; back propagation neural network (BPNN); Feature maps; Field tests; fine sediments; Flow velocity; Fluid mechanics; Hydrology; Machine learning; Mathematics; Neural networks; Neurons; Radial basis function; radial basis function neural network (RBFNN); Root-mean-square errors; Sediment transport; Sedimentation & deposition; Sediments; Self organizing maps; self-organizing feature map (SOFM); Settling velocity; Sustainable development; Water quality; Water resources; Water shortages
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math9233141

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%.