River suspended sediment load prediction using neuro-fuzzy and statistical models: Vamsadhara river basin, India
2018
Nivesh, Shreya | Kumar, Pravendra
The present study deals with the development of Adaptive Neuro-Fuzzy Inference System (ANFIS), Multiple Linear Regression (MLR) and Sediment Rating Curve (SRC) models to estimate the suspended sediment load from Vamsadhara River basin comprising of 7820 km² area situated between Mahanadi and Godavari river basins. Three different inputs or cases using ANFIS, MLR and SRC were employed to find the effect of different inputs on the suspended sediment load. The developed models were trained and tested. Three statistical parameters: Root Mean Square Error (RMSE), coefficient of determination (R₂) and Coefficient of Efficiency (CE) were used to compare the results of the models. Results revealed that the neuro-fuzzy model (RMSE = 44.02 kg sec₋₁, R₂ = 0.99 and CE = 99.06%) is in good agreement with the observed values and present better performance in suspended sediment load prediction in comparison to the traditional models like MLR (RMSE = 188.28 kg sec₋₁, R₂ = 0.828 and CE = 82.82%) and SRC (RMSE = 331.69 kg sec₋₁, R₂ = 0.617 and CE = 56.48%).
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