Application of artificial neural network in estimating monthly time series reference evapotranspiration with minimum and maximum temperatures

作者：Jahanbani Heerbod^*; El Shafie Ahmed Hussein

来源：Paddy and Water Environment, 2011, 9(2): 207-220.

DOI：10.1007/s10333-010-0219-1

• Evapotranspiration
• Rich and poor data
• Feed forward backpropagation

There are various methods for computing reference evapotranspiration (ET(o)) using meteorological data. However, such models tend to perform well for predicting ET(o) close to the mean, but do not keep accurate performance with extreme observations. It is recognized that the Penman-Monteith (PM) model has the best performance when rich data is available to calculate the ET(o), which is not frequently available to a certain extent. In case of poor data, such as prediction of futuristic ET(o) while investigating climate change effect, although there are models other than PM like Hargreaves-Samani (HGS), the universal sustainability of these models are not quit proved. Accordingly, the calculation of ET(o) still required numerous research to reach accurate estimation of ET(o) specially when there is lacking for data to utilize PM method. Recently, methods based on artificial intelligence (AI) have been suggested to provide reliable prediction model for several application in engineering. This manuscript employed artificial neural network (ANN) for predicting daily ET(o) at Rasht city located northern part of Iran using minimum and maximum daily temperatures collected from 1975 to 1988 of the region. A comprehensive data analysis utilizing the daily time series, minimum and maximum temperatures and solar radiation (T (min), T (max) and R (s)), as input pattern to predict daily ET(o) at the current month and for the following month is proposed. The employed ANN model was feed forward backpropagation (FFBP) type with Bayesian regulation backpropagation. The mean square error, mean absolute error, mean absolute relative error and regression coefficient are the statistical performance indices used to evaluate the model accuracy. The results showed that the proposed ANN model could successfully be used to predict daily ET(o) using only maximum and minimum temperatures with significant level of accuracy. In addition, results show that the proposed ANN model outperforms HGS method.

• 出版日期2011-6