抽象的

Predicting Reservoir Water Level Using Artificial Neural Network

Shilpi Rani, Dr. Falguni Parekh

Water resources management mainly deals with hydrological forecasting. In hydrological forecast mainly forecast of water level of reservoirs is done which is useful for various purposes. Forecasting techniques may be different as per the purpose of system, data available for the system and physical characteristics. Uncertainty is there in hydrological parameters, and to deal with thisa proper forecasting method is needed. This paper presents an Artificial Neural Network (ANN) approach for forecasting of reservoir water level using ten daily data of inflow, water level and release. For developing the ANN models, three alternative networks i.e. Cascade, Elman and Feedforward back propagation were evaluated. A total of 23 years of hydrological data were used to train and validate the networks. This paper investigates the best model to forecast water level. The developed models are trained and validated on the collected data of Sukhi Reservoir project, located in Gujarat State, India.Based on these results, it can be concluded that amongst the three methods used for this study, ANN using Feed Forward Backpropagation is an appropriate predictor for real-time Water Level forecasting of Sukhi Reservoir Project.