## Abstract Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, an artificial neural network (ANN) model for reference evapotranspiration (ET~0~) calculation was investigated. ANNs were trained
M5 model tree based modelling of reference evapotranspiration
โ Scribed by Mahesh Pal; Surinder Deswal
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 388 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0885-6087
- DOI
- 10.1002/hyp.7266
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โฆ Synopsis
Abstract
This paper investigates the potential of M5 model tree based regression approach to model daily reference evapotranspiration using climatic data of Davis station maintained by California irrigation Management Information System (CIMIS). Four inputs including solar radiation, average air temperature, average relative humidity, and average wind speed whereas reference evapotranspiration calculated using a relation provided by the CIMIS was used as output. To compare the performance of M5 model tree in predicting the reference evapotranspiration, FAOโ56 PenmanโMonteith equation and calibrated HargreavesโSamani relation was used. A comparison of results suggests that M5 model tree approach works well in comparison to both FAOโ56 and calibrated HargreavesโSamani relations. To judge the generalization capability of M5 model tree approach, model created by using the Davis data set was tested with the datasets of four different sites. Results from this part of the study suggest that M5 model tree could successfully be employed in modeling the reference evapotranspiration. Further, sensitivity analysis with M5 model tree approach suggests the suitability of solar radiation, average air temperature, average relative humidity, and average wind speed as input parameters to model the reference evapotranspiration Copyright ยฉ 2009 John Wiley & Sons, Ltd.
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