Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts

Abstract

Medium range daily reference evapotranspiration (ETo) forecasts are very helpful for farmers or irrigation system operators for improving their irrigation scheduling. We tested four artificial neural networks (ANNs) for ETo forecasting using forecasted temperatures data retrieved from public weather forecasts. Daily meteorological data were collected to train and validate the ANNs against the Penman–Monteith (PM) model. And then, the temperature forecasts for 7-day ahead were entered into the validated ANNs to produce ETo forecast outputs. The forecasting performances of models were evaluated through comparisons between the ETo forecasted by ANNs and ETo calculated by PM from the observed meteorological data. The correlation coefficients between observed and forecasted temperatures for all stations were all greater than 0.91, and the accuracy of the minimum temperature forecast (error within ± 2 °C) ranged from 68.34 to 91.61 %, whereas for the maximum temperature it ranged from 51.78 to 57.44 %. The accuracy of the ETo forecast (error within ± 1.5 mm day−1) ranged from 75.53 to 78.14 %, the average values of the mean absolute error ranged from 0.99 to 1.09 mm day−1, the average values of the root mean square error ranged from 0.87 to 1.36 mm day−1, and the average values of the correlation coefficient ranged from 0.70 to 0.75. The results suggested that ANNs can be considered as a promising ETo forecasting tool. The forecasting performance can be improved by promoting temperature forecast accuracy.