Reservoir Trap Efficiency Using Artificial Neural Networks

Abstract

The developmental activities, undergoing within the vicinity of reservoir watershed, result in discharge of large volumes of sediment into the reservoir, which in turn affects biodiversity of the reservoir and reservoir useful life period. These necessitate the reservoir sedimentation studies. The volume of sediment deposited or trapped in a reservoir can easily be quantified by the simple knowledge of its trap efficiency (Te), which is been estimated by various conventional empirical approaches till date. In the present study empirical methods proposed by Brown and Brune have been modified and adopted to estimate Te of Pong Reservoir (Beas Dam) on the Beas River in Kangra district of Himachal Pradesh, India. The major contribution of the study is incorporation of reservoir age in an empirical equation. It is found that the present study estimation of the Te is much better than any other conventional methods. Further, an attempt has been made to estimate ‘Te’ using Artificial Neural Networks (ANN). The study shows that ANN can successfully be applied to estimate not only the trend but also the magnitude of the Te better than the conventional models.