Estimation of Suspended Sediment Yield using Artificial Neural Network Model
Manikanta Pajjuri1, Arvind Yadav2, Kondapanani Lakshmi Tanuja3, Pendurathi Nagarjuna4, Penke Satyannarayana5
1Arvind Yadav*, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
2Manikanta. Pajjuri, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
3Kondapanani Lakshmi Tanuja, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
4Pendurathi Nagarjuna, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
5Penke Satyannarayana,, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 3249-3253 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8877019320/2020©BEIESP | DOI: 10.35940/ijitee.C8877.019320
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Abstract: Estimation of the suspended sediment yield is important for the planning and management of water resources and protection of the environment. Environmental change influences sediment generation and the transport and the consequent sediment load in river. In this study, artificial intelligence-based technique like the artificial neural network (ANN) is proposed for sediment yield estimation in the Godavari river basin, India. The ANN is one of the appropriate data-mining techniques that help model the complex phenomenon of sedimentation. In this study the prediction of the suspended sediment load is done using the ANN techniques by using the water discharge and water level data from 1970 to 2015 as inputs at Polavaram gauge station in Godavari river basin, India. The results demonstrate that the ANN shows a satisfactory performance based on the root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE) and correlation coefficient (r) error statistics and provided more accurate results.
Keywords: Suspended Sediment Yield, Neural, Network, Water Discharge, Godavari river.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques