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%.
Highlights
For instance, is a critical problem that is caused by the resuspension of fine sediments deposited within the sediment bed in water [2,3]
The prediction models were developed based on hydraulic feature map (SOFM)
The root mean square error (RMSE) and mean absolute error (MAE) should be close to zero, indicating the minimum error obtained; the Nash–Sutcliffe efficiency (NSE) should be close to 1, indicating the model is better than applying the mean estimator; the mean value accounted for (MVAF) should be close to 100%, indicating the accuracy of the average estimating performance of the model; the total variance explained (TVE) should be close to 100%, indicating the overall dynamics and dispersion accounted by the model
Summary
The movement characteristics of sediments (i.e., coarse and fine sediments) are considered as one of the most complex study areas in the field of hydrology. Shallow riverbeds were one of the main causes of flood-related disasters reported worldwide, with an estimated financial loss of almost USD1800/s between 1990 and 2020 [1]. Sedimentation issues have always been a deep concern for various parties due to the severe impacts that are invited by sediments. For instance, is a critical problem that is caused by the resuspension of fine sediments deposited within the sediment bed in water [2,3]. Siltation could induce a large number of negative impacts on humans and the environment
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
