Vol. 8, Issue 2 (2019)
Assessment of soft computing and statistical approaches for suspended sediment load estimation: Vamsadhara river basin, India
Shreya Nivesh, Pravendra Kumar, Bhagwat Saran, Pragati N Sawant and Ramesh Verma
The present study was undertaken to estimate the suspended sediment load from the Vamsadhara river basin comprising of 7820 km2 area, situated between Mahanadi and Godavari river basins. Three daily input data groups or cases were employed using Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Fuzzy Logic (FL), Multiple Linear Regression (MLR) and Sediment Rating Curve (SRC) to find the effect of different inputs on the suspended sediment load. Input 1 consists of Pt, Qt, Qt−1, St−1 as inputs to the model to predict St. Input 2 consists of Pt-1, Qt, Qt-1, St-1 and Input 3 consist of Pt-1, Qt, Qt-2, St-1. The developed models were trained and tested. Three statistical parameters: root mean square error (RMSE), correlation coefficient (r) and coefficient of efficiency (CE) were used to compare the results of the models. Based on the performance analysis results revealed that the ANFIS model (RMSE-44.02 kg/sec, r-0.995 and CE-99.06%) outperformed other soft computing and conventional models. It can be concluded that the ANFIS models are more preferable and can be applied successfully for the estimation of the suspended sediment concentration for the study area.
How to cite this article:
Shreya Nivesh, Pravendra Kumar, Bhagwat Saran, Pragati N Sawant, Ramesh Verma. Assessment of soft computing and statistical approaches for suspended sediment load estimation: Vamsadhara river basin, India. Pharma Innovation 2019;8(2):693-702.