Assessment of Sediment Concentration Through RBNN and SVM FFA in Arid Watershed, India
For scheduling, designing and management of hydraulic structure and hydrologic model prediction of sediment concentration play a crucial role in water resources engineering. Here, prediction of the sediment concentration using SVM FFA and radial basis function network (RBFN) models are done. Parameters like rainfall, temperature, and sediment concentration on a monthly basis from Puintala, Odisha, India watershed are considered. SVM FFA model shows better performance than the RBFN model. Among five input scenarios, S_{t 1} S_{t 2} S_{t 3} S_{t 4} S_{t 5} give most excellent WI value 0.965 and 0.943 for testing and training phases. In case of RBFN, best WI value is 0.8636 and 0.8911 for training and testing phase when 3 0.5 1 architecture is used as an input.
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