FORECASTING OF EVAPORATION FROM HEMREN RESERVOIR BY USING ARTIFICIAL NEURAL NETWORK

Abstract

The evaporation is one of the basic components of the hydrologic cycle and is essential for studies such as water balance, irrigation system design, and water resource management and it is requires knowledge of the values of many climatic variables. In order to estimate the evaporation, direct measurement methods or physical and empirical models can be used. Using direct methods require installing meteorological stations and instruments for measuring evaporation. Installing such instruments in various areas requires specific facilities and cost which is hard to be employed. Accordingly, this paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from Hemren reservoir by using daily temperature, relative humidity, wind velocity, sunshine hours, and evaporation data in Hemren meteorological station. Also, this study outlines a procedure to evaluate the effects of input variables on the output variable using the weight connections of ANN models. The Lev. Marqn. Back Prog. (LMBP) has been utilized to construct the ANN models. For the development ANN model, different networks with different numbers of neurons and layers were evaluated. Mean Squared Error (MSE) and the Correlation Coefficient (R2) were employed to evaluate the accuracy of the proposed model. The study shows that the best model for estimation of evaporation is ANN (4-10-1), it have MSE equal to 0.112711 and the correlation coefficient (R2) equal to 0.999540.