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This study investigates the utility of adaptive Radial Basis Function (RBF)  networks for estimating hourly grass reference evapotranspiration (ET0) from  limited weather data. Nineteen days of micrometeorological and lysimeter  data collected at half-hour intervals during 1962-63 and 1966-67 in the  Campbell Tract research site in Davis, California were used in this study.  Ten randomly chosen days (234 patterns) were selected for the RBF networks  training. Two sequentially adaptive RBF networks with different number of  inputs (ANNTR and ANNTHR) and two Penman-Monteith equations with different  canopy resistance values (PM42 and PM70) were tested against hourly  lysimeter data from remaining nine days (200 patterns). The ANNTR requires  only two parameters (air temperature and net radiation) as inputs. Air  temperature, humidity, net radiation and soil heat flux were used as inputs  in the ANNTHR. PM equations use air temperature, humidity, wind speed, net  radiation and soil heat flux density as inputs. The results reveal that  ANNTR and PM42 were generally the best in estimating hourly ET0. The ANNTHR  performed less well, but the results were acceptable for estimating ET0.  These results are of significant practical use because the RBF network with  air temperature and net radiation as inputs could be used to estimate hourly  ET0 at Davis, California

Estimating hourly reference evapotranspiration from limited weather data by sequentially adaptive RBF network