A spatial neural fuzzy network for estimating pan evaporation at ungauged sites

Abstract

Evaporation is an essential reference to the management of water resources. In this study, a hybrid model that integrates a spatial neural fuzzy network with the kringing method is developed to estimate pan evaporation at ungauged sites. The adaptive network-based fuzzy inference system (ANFIS) can extract the nonlinear relationship of observations, while kriging is an excellent geostatistical interpolator. Three-year daily data collected from nineteen meteorological stations covering the whole of Taiwan are used to train and test the constructed model. The pan evaporation (<i>E</i>_pan) at ungauged sites can be obtained through summing up the outputs of the spatially weighted ANFIS and the residuals adjusted by kriging. Results indicate that the proposed AK model (hybriding ANFIS and kriging) can effectively improve the accuracy of <i>E</i>_pan estimation as compared with that of empirical formula. This hybrid model demonstrates its reliability in estimating the spatial distribution of <i>E</i>_pan and consequently provides precise <i>E</i>_pan estimation by taking geographical features into consideration.