Modeling of reference evapotranspiration by multiple linear regression

Silva, Helder J Farias; Santos, Marconio Silva dos; Cabral, Jorio Bezerra; Spyrides, Maria Helena Constantino

doi:10.5935/2237-2202.20160005

Cited by 11 publications

(7 citation statements)

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“…The proportion of 20% of the dataset applied in this study for model validation is widely used and approved in many investigations. However, other studies, such as Almedeij [25] and Silval et al [13] employed smaller percentages 17% and 15% respectively. The relationship between the observed and predicted evaporation regression model is presented (see Figure 3), suggesting that there is a strong correlation between observed and predicted values.…”

Section: Mlr Model Validationmentioning

confidence: 93%

“…Potential evaporation is the potential or ability of the atmosphere to remove water from a surface if there is no limit to the water availability [11,12]. Potential evaporation is the most commonly used variable, while actual evaporation is the amount of water removed by evaporation from that surface [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…The use of formulas or mathematical models that can predict evaporation from available climate data is therefore assertive and can provide more precise results than the calculated evaporation [18][19][20][21]. These models had been widely used by several researchers to estimate evaporation based on meteorological parameters like [1,9,13,19,20,[22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Develop Evaporation Model Using Multiple Linear Regression in the Western Desert of Iraq –Horan Valley

Mohammed¹,

Said²,

Kamel³

et al. 2022

IJDNE

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Evaporation is influenced by several meteorological parameters, evaporation data are usually difficult to obtain compared to rainfall data, especially in arid regions. Developing a monthly evaporation prediction model in arid regions in terms of available meteorological data is a significant step. The data used in this study for modeling are monthly measurements to cover substantial continuity over a period of 18 years between January 2000 and December 2017. Stepwise and backward multiple linear regression techniques were used with a new procedure of variable selection to select the best model. Temperature, wind speed, relative humidity and sunshine hours were used as a independent variables in the multiple linear regression (MLR) technique to establish the best prediction of the evaporation model. To examine the MLR evaporation developed model in the current study, MLR results were compared with the most common evaporation models commonly used in arid regions such as Kharufa and Khosla methods. The results of performance indicators shows that the R2 values are approximately 0.937, 0.90 and 0.85 for MLR evaporation developed model, Kharufa and Khosla methods, respectively. Moreover, the values of the error measures, namely RMSE and NAE for MLR evaporation developed model were 36.3 and 0.123, Kharufa model 71.22 and 0.241 and Khosla model was and 173.7 and 0.581 respectively. Based on the foregoing, the results of the MLR developed evaporation model in the current study outperforms in all performance indicators and proves to be better than the Kharufa and Khosla models.

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Section: Mlr Model Validationmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Develop Evaporation Model Using Multiple Linear Regression in the Western Desert of Iraq –Horan Valley

Mohammed¹,

Said²,

Kamel³

et al. 2022

IJDNE

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show abstract

“…The important advantage of LR models is that it has the representation of the explicit equations, which can be easily interpreted. Mahida and Patel (2015) and Silva et al (2016) developed MLR models using different meteorological parameters as inputs for estimation of ET0 and found satisfactory performance for prediction of ET0 by comparing with the P-M method. Hence, as per the simplicity and precise application of the LR technique, it has been examined as a benchmark ET0 modeling method in this study.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variation of reference evapotranspiration using different estimation techniques for Western Maharashtra, India

Khedkar¹,

Mane²

2022

Preprint

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The Spatio-temporal variability of reference evapotranspiration (ET0) was analysed using different estimation techniques with limited data over western Maharashtra, India. Sixteen ET0 models were compared with FAO 56 Penman-Monteith (P-M) method, out of that eight climate-based methods viz., Soil Conservation Service Blaney-Criddle, Thornthwaite, Hargreaves-Samani, Pan evaporation, Jensen-Haise, Priestly-Taylor, Turc, and Radiation were selected, and four linear regression (LR) and four artificial neural networks (ANN) models were developed. The LR and ANN models viz., Model1 (evaporation), Model2 (maximum and minimum temperature), Model3 (maximum and minimum temperature, bright sunshine hours), Model4 (maximum and minimum temperature, maximum and minimum humidity, bright sunshine hours). Resulting LR and ANN models showed satisfactory performance and can be accepted to predict ET0 values. The inverse distance weighting technique used for mapping, and the Mann-Kendall and linear regression methods are used for analysing trends of ET0. The map of P-M method showed that the values of ET0 increase from central part towards south-east and relatively more ards north-east of study area. Among climate based methods, it was observed that none of methods shown close in spatial distribution pattern of ET0 with P-M method, whereas the maps of LR and ANN models showed close approach to P-M method. It was observed that there was decreasing trend of ET0 using P-M method for all stations as well as the more complex methods which require temperature, radiation and humidity data showed decreasing trends of ET0 for most of the stations Whereas the methods which requiring only temperature data showed no or increasing trends of ET0. Hence, it was concluded that trends of ET0 depends on type of meteorological data used for calculation under current global scenario.

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“…O conhecimento do regime hídrico em uma bacia hidrográfica é fundamental nos estudos hidrológicos que servem como base para projetos de diferentes usos de água, sendo indispensável à gestão sustentável dos recursos hídricos (Santos et al, 2009). Na tentativa dessa gestão sustentável muitos pesquisadores no mundo têm utilizado diferentes tecnologias (Schirmbeck, 2017;Anjos et al, 2016;Oliveira et al, 2017) e metodologias (Silva et al, 2016;Cavalcante et al, 2017) para auxiliar na gestão dos recursos hídricos.…”

Section: Introductionunclassified

Efeitos da precipitação na vazão da bacia hidrográfica do rio Pajeú-PE

França¹,

Diaz²,

Reis³

et al. 2019

Rev. Bras. Geog. Fis.

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Em estudos hidrológicos se torna importante a obtenção de dados através de estações meteorológicas. Porém, em certas localidades, esses dados possuem muitas falhas, o que pode comprometer diversos estudos. Com isso, o uso de estatística para o preenchimento de falhas e análise de séries climáticas e hidrológicas são fundamentais para a interpretação dessas séries. Assim, este trabalho tem como objetivo aplicar métodos estatísticos no preenchimento de falhas para análise da espacialização das vazões. Foram utilizados dados de precipitação e vazão para algumas localidades da bacia hidrográfica do rio Pajeú dos anos de 1994 a 2013 para a regressão linear e de 1964 a 2014 para a vazão específica. Como forma de preenchimento de falhas, foi utilizado o método de Ponderação Regional para a precipitação e o método Auto Regressivo de Médias Moveis (ARMA) para a vazão. Para analisar se a série tem relação estatisticamente foi utilizado o método de Regressão Linear e para analisar os dados anualmente foi calculada a vazão específica de cada estação. Os resultados demonstram que não há relação entre precipitação e vazão durante o período analisado, onde o melhor valor obtido de r foi 0.0315 para a estação Ilha Grande no município de floresta e a maior vazão específica foi para a estação de Afogados da Ingazeira. Com a análise de regressão linear entre a vazão e a precipitação, foi demonstrado que para essa bacia hidrográfica essas variáveis não possuem significância estatística.

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Modeling of reference evapotranspiration by multiple linear regression

Cited by 11 publications

References 0 publications

Develop Evaporation Model Using Multiple Linear Regression in the Western Desert of Iraq –Horan Valley

Develop Evaporation Model Using Multiple Linear Regression in the Western Desert of Iraq –Horan Valley

Spatio-temporal variation of reference evapotranspiration using different estimation techniques for Western Maharashtra, India

Efeitos da precipitação na vazão da bacia hidrográfica do rio Pajeú-PE

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