Abstract
The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R2 value of 0.85 and 0.74 during the training and testing period, respectively.
Highlights
Eroded sediment originating from drainage basins due to hydrometeorological processes like rainfall, snow melt, and ice melting, etc., is transported in the form of suspended loads and bed loads [1,2,3]
Sediment deposition in rivers and reservoirs is a very serious challenge worldwide. It leads to rapid depletion of water storage capacities which affects the supply of irrigation as well as power generation
To accomplish objective,this weobjective, investigated the input such flows affecting of sediment yields
Summary
Eroded sediment originating from drainage basins due to hydrometeorological processes like rainfall, snow melt, and ice melting, etc., is transported in the form of suspended loads and bed loads [1,2,3]. Sediment deposition in rivers and reservoirs is a very serious challenge worldwide It leads to rapid depletion of water storage capacities which affects the supply of irrigation as well as power generation. The results of ANN and ANFIS were compared by [26,27] for the prediction of sediment yield In these studies, researchers found that ANFIS models show a higher accuracy than the ANN and SRC models. Researchers found that ANFIS models show a higher accuracy than the ANN and SRC models It was found in studies by [28,29] that gene expression algorithms are better than ANN and ANFIS models for predictions of sediment loads
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.
