Monthly Suspended Sediment Load Estimation Using Artificial Neural Network and Traditional Models in Krishna River Basin, India

  • et al.
N/ACitations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.

Cite

CITATION STYLE

APA

Yadav*, A., Krishna, A. R., … Satyannarayana, P. (2020). Monthly Suspended Sediment Load Estimation Using Artificial Neural Network and Traditional Models in Krishna River Basin, India. International Journal of Innovative Technology and Exploring Engineering, 9(3), 613–618. https://doi.org/10.35940/ijitee.b7743.019320

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free