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Estimating hourly reference evapotranspiration from limited weather data by sequentially adaptive RBF network
Univeristy of Niš, Faculty of Civil Engineering and Architecture

emailslavisa.trajkovic@gaf.ni.ac.rs
Project:
Development of Hydro-information system for monitoring and early drought warning (MESTD - 37003)

Keywords: evapotranspiration; neural networks; temperature; net radiation; lysimeters
Abstract
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.
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About

article language: English
document type: unclassified
DOI: 10.2298/FUACE1103473T
published in SCIndeks: 30/05/2012

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