Abstract
Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS–BA, ANFIS–WA, MFNN–BA, and MFNN–WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash–Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS–BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0. 75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS–BA had more reliable performance compared to other models. Thus, the ANFIS–BA model has high potential for predicting SSL.
Highlights
Through basin management, different strategies have been applied to decrease sediment volume.Sedimentation processes and soil erosion are important in the fields of hydrology and hydraulics [1].Sediment load relies on rainfall and runoff, as these hydrological variables can change the sedimentationAppl
The results indicated that the genetic programming (GP) model outperformed the support vector machine (SVM) and artificial neural network (ANN) models
The results revealed that the SVM model outperformed the ANN and linear regression models
Summary
Different strategies have been applied to decrease sediment volume. Soft computing models have been widely applied to simulate hydrological variables [5]. The investigated literature reviews emphasize that the preparation of soft computing models has unknown parameters [7,8,9] These studies show that the training of adaptive neuro fuzzy system (ANFIS) parameters is an important challenge during the simulation process. Different studies attempted to use new training strategies that are more accurate and easier than the traditional methods for obtaining model parameters. To improve ANFIS and MFNN model efficiency by applying new optimization algorithms These algorithms are used to obtain the best ANFIS and MFNN structures and parameters; To predict monthly sediment load by applying improved ANFIS and MFNN models; To examine the uncertainty of the predictions; and.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
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.
