Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

Abstract

A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirna and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R2 of 0.92 and a PBIAS of −0.59%. Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal.

Using machine learning to improve predictions and provide insight into fluvial sediment transport

“…Additional analyses of streamflow data could include a time‐since‐last‐event feature that could teach the model sediment source and storage controls to help better account for hysteresis (Gellis, 2013; Smith & Dragovich, 2009). ML is a complex and ever‐changing field of study; additional work could be done exploring other methods including artificial neural networking (ANN) and, more specifically, hybrid wavelet and neural networking (WANN), which produced accurate results in sediment transport prediction studies (Afan et al, 2016; Khan et al, 2021). Lastly, the models could be improved with additional sites and additional samples to better represent sediment transport.…”

“…ML is a complex and ever-changing field of study; additional work could be done exploring other methods including artificial neural networking (ANN) and, more specifically, hybrid wavelet and neural networking (WANN), which produced accurate results in sediment transport prediction studies (Afan et al, 2016;Khan et al, 2021).…”

“…The application of different ML approaches to estimate sediment transport has grown over the past two decades (Afan et al, 2016). ML has multiple benefits over traditional approaches, such as SRCs, with increased prediction accuracy of suspended sediment at specific sites while having the ability to learn complex linear and non-linear relations (Cisty et al, 2021;Francke et al, 2008;Khan et al, 2021;Zounemat-Kermani et al, 2020). Another benefit of ML is the ability to interpret these complex relations with the important features used in the model (Breiman, 2001;Cutler et al, 2007).…”

Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model

“…In recent years, as computing and artificial intelligence technology are developing, the artificial neural network has been widely used in mathematical model building and disaster prediction. Artificial intelligence models can be used to predict and cluster various hydrological problems, such as prediction of rainfall, runoff and sediment, in which the most used models are ANN, SVM/SVR, ANFIS, genetic algorithm (GA), particle swarm algorithm (PSO) and artificial bee colony algorithm (ABC) [8][9][10][11]. Based on the hydrological model of ANN, the flow prediction problem with precipitation as an input showed that the wavelet derived value can provide relevant flow path and hydrological information [12].…”

Evaluation and Validation of Estimated Sediment Yield and Transport Model Developed with Model Tree Technique

“…Bhattacharya et al [18] performed comparative evaluation using an ANN and a model trees method for the estimation of sediment yield. Khan et al [19] employed ANN and wavelet-cum-ANN methods to estimate suspended sediment loads in the Elbe River in Germany. In addition, Nhu et al [20] presented erosion susceptibility mapping through the development of a hybrid model that simultaneously utilized various data mining techniques.…”