Reservoir Evaporation Prediction Using Data-Driven Techniques

Abstract

Evaporation in reservoirs plays a prominent role in water resources planning, operation, and management because a considerable amount of water is lost through evaporation, especially in large reservoirs. Estimating evaporation from surface water usually requires ample data that are not easily measurable. At present, in India, reservoir evaporation is estimated from the pan evaporation and average water spread area. Because reservoir evaporation exhibits a nonlinear relationship with the reservoir storage and other meteorological parameters, accurate prediction of evaporation by the conventional method is a cumbersome process. Recently evolved data-driven techniques will excel in nonlinear processes modeling. In this study, reservoir evaporation is predicted using three different data-driven techniques—artificial neural network (ANN), model tree (MT), and genetic programming (GP)—by time-series modeling. The daily Koyna reservoir evaporation prediction models are developed using 49 years of daily evaporation data. Approximately 70% of the data set is used for training the model, and the remaining 30% is used for testing. From this study, all of the data-driven techniques predicted the reservoir evaporation very accurately, with better performance and a correlation of approximately 0.99. This shows that if the input data series exhibits a good pattern with less noise, the data-driven techniques result in better performances. Among the data-driven techniques used in this study, GP predicts the reservoir evaporation slightly better than ANN and MT models.