Open AccessArtificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India
Author(s) -
Penke Satyanarayana,
Vinay Kumar,
Uppara Geethika,
Shree Ranjani,
Arvind Yadav
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7744.019320
Subject(s) - sediment , sedimentation , structural basin , artificial neural network , environmental science , hydrology (agriculture) , bed load , sediment control , drainage basin , flood control , flood myth , sediment transport , geology , computer science , geotechnical engineering , geography , geomorphology , machine learning , cartography , archaeology
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.