Author(s): Qamar Sultana, M. Gopalnaik
Linked Author(s): QAMAR SULTANA
Keywords: Reservoir sedimentation, trap efficiency, artificial neural network
Abstract: The storage reservoirs built across rivers and streams, undergo deposition of sediment. This deposition which takes place progressively in time reduces the active capacity of the reservoir to provide the outputs of water through passage of time. A major portion of the silt that is carried along with the river water settles down in the reservoir, which causes the reduction in its storage capacity, which in turn reduces the benefits from the reservoir projects which were constructed with a huge investment. Thus reservoir sedimentation has become a major problem all over the world. There are a number of methods for estimation of reservoir sedimentation but all these methods differ in terms of their complexity, inputs and other requirements. In the simplest way, the fraction of sediment deposited in the reservoir can be determined through the knowledge of its trap efficiency. In this study, the empirical formulae are used for estimating the trap efficiency of Sriramsagar reservoir which is located on Godavari River in Nizambad district of Telangana State, in India. The observed trap efficiency is calculated using empirical equation suggested by Heinemann and compared with the Brune, Brown method which uses the capacity-inflow ratio for medium sediment depending upon the size of the reservoir and also compared with Gill method which is used for estimation of medium sediment. An attempt has also been made to develop the Artificial Neural Network Model (ANN) to simulate the trap efficiency of the reservoir using the Matlab software. It is observed from the values of several performance statistical indicators that the ANN model predicted the trap efficiency of the reservoir with better accuracy and less effort than that of the conventional method. Based on the simulation results it concludes that the developed ANN model has more advantages over the conventional methods
Year: 2017