Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India
Vinay Kumar Rayaprol1, Penke Satyannarayana2, Arvind Yadav3, Siva Ranjini4, Uppara Geethika5
1Dr. Penke Satyanarayana*, Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
2Vinay kumar rayaprol, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
3Uppara geethika, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
Siva ranjani, Department of Electronics and Communication4 Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
5Dr. Arvind Yadav, 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: 3701-3705 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7744129219/2020©BEIESP | DOI: 10.35940/ijitee.B7744.019320
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.
Keywords: Artificial Neural Network, Water Discharge; Water level, Suspended Sediment load, Krishna River.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques