Evaluation of reservoir sedimentation using data driven techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Garg:2013:ASC,

• author = "Vaibhav Garg and V. Jothiprakash",
• title = "Evaluation of reservoir sedimentation using data driven techniques",
• journal = "Applied Soft Computing",
• year = "2013",
• volume = "13",
• number = "8",
• pages = "3567--3581",
• keywords = "genetic algorithms, genetic programming, Reservoir sedimentation, Soft computing techniques, Artificial neural networks, Model trees",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2013.04.019",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494613001439",
• abstract = "The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets.",

}

Genetic Programming entries for Vaibhav Garg V Jothiprakash

Citations