International Journal of
Water Resources and Environmental Engineering

  • Abbreviation: Int. J. Water Res. Environ. Eng.
  • Language: English
  • ISSN: 2141-6613
  • DOI: 10.5897/IJWREE
  • Start Year: 2009
  • Published Articles: 345

Full Length Research Paper

Reference evapotranspiration in São Paulo State: Empirical methods and machine learning techniques

Bartolomeu Felix Tangune
  • Bartolomeu Felix Tangune
  • Rural Engineering Departament, Eduardo Mondlane University, Vilankulo, Mozambique.
  • Google Scholar
Joao Francisco Escobedo
  • Joao Francisco Escobedo
  • Rural Engineering Department, FCA/UNESP, Botucatu, São Paulo, Brazil.
  • Google Scholar


  •  Received: 03 March 2018
  •  Accepted: 10 April 2018
  •  Published: 31 May 2018

References

Abtew W (1966). Evapotranspiration measurements and modeling for three wetland systems in South Florida. Journal of the American Water Resources Association, 127(3):140-147.

 

Ahooghalandari M, Khiadani M, Jahromi ME (2016). Developing equations for estimating reference evapotranspiration in Australia. Water Resources Management, 30:3815- 3828.
Crossref

 
 

Amari S, Wu S (1999). Improving support vector machine classifiers by modifying Kernel funtions. Neural Network, 12:783-789.
Crossref

 
 

Allen RG, Pereira L, Raes D, Smith M (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. In: Irrigation and Drainage, Paper No. 56, Food and Agriculture Organization of the United Nations, Rome.

 
 

Benevides JG, Lopez D (1970). Formula para el caculo de la evapotranspiracion potencial adaptada al tropico (15º N - 15º S). Agronomia Tropical, 20(5):335-345.

 
 

Blaney HF, Criddle WD (1950). Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data. USDA. SCS-TP-96, 48 p.

 
 

Cao JF, Li YZ, Zhong XL, Zhao YM (2015). Comparison of four combination methods for reference crop evapotranspiration. Chinese Journal of Agrometeorology, 36(4):428-436.

 
 

Doornbos J, Pruitt WO (1975). Guidelines for predicting crop water requirements. In: Irrigation and Drainage Papper No 24. Food and Agriculture Organization of the United Nations, Rome.

 
 

Hamon WR (1961). Estimating potential evapotranspiration. Journal of the Hydraulics Division Proceedings of the American Society of Civil Engineers, 87:107-120.

 
 

Hargreaves GH, Samani ZA (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1(2):96-99.
Crossref

 
 

Huo Z, Feng S, Kang S, Dai X (2012). Artificial neural network models for reference evapotranspiration in arid area of northwest China. Journal of Arid Environments, 82:81-90.
Crossref

 
 

Irmak S, Irmak A, Allen RG, Jones JW (2003). Solar and net radiation based equations to estimate reference evapotranspiration in humid climates. Journal of Irrigation and Drainage Engineering, 129(5):336-347.
Crossref

 
 

Jensen ME, Haise HR (1963). Estimating evapotranspiration from solar radiation. Procedding of the Journal of Irrigation and Drainage Division: American Society of Civil Engineers, 89:15-41.

 
 

Kisi Ö (2013). Least squares support vector machine for modeling daily reference evapotranspiration. Irrigation Science 31(4):611-619.
Crossref

 
 

Kumar M, Kar IN (2009). Non-linear HVAC computations using least square support vector machines. Energy Conversion and Management, 50:1411-1418.
Crossref

 
 

Kumar M, Rahguwanshi NS, Singh R (2011). Artificial neural networks approach in evapotranspiration modelling: A review. Irrigation Science, 29:11-25.
Crossref

 
 

Landeras G, Barredo AO, Lopez JJ (2008). Comparison of artificial neural network models and empirical and semi– empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management, 95:553-565.
Crossref

 
 

Lin HJ, Yeh JP (2009). Optimal reduction of solutions for support vector machines. Applied Mathematics and Computation 214:329-335.
Crossref

 
 

Liu X, Xu C, Zhong X, LI Y, Yuan X, Cao J (2017). Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement. Agricultural Water Management, 184:145- 155.
Crossref

 
 

Makkink GF (1957). Testing the Penman formula by means of lysimeters. Journal of the Institution of Water Engineers, 11(3):277-288.

 
 

Melo GL, Fernandes ALT (2012). Evaluation of empirical methods to estimate reference evapotranspiration in Uberaba. Enginharia Agrícola, 32(5):875-888.
Crossref

 
 

Meyer D, Leisch F, Hornik K (2003). The support vector machine under test. Neurocomputing, 55:169-186.
Crossref

 
 

Nourani V, Fard MS (2012). Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advances in Engineering Software, 47(1):127-146.
Crossref

 
 

Parasuraman K, Elshorbagy A, Carey SK (2004). Modeling the dynamics of the evapotranspiration using genetic programming. Hydrological Sciences Journal, 52:563 -578.
Crossref

 
 

Raghavendra NS, Deka PC (2014). Support vector machine applications in the field of hydrology: a review. Applied Soft Computing, 19:372-386.
Crossref

 
 

Samaras DA, Reif A, Theodoropoulos K (2014). Evaluation of radiation based reference evapotranspiration models under different Mediterranean climates in Central Greece. Water Resources Management, 28:207-225.
Crossref

 
 

Souza AP, Mota LL, Zamadei T, Martim CC, Almeida FT, Paulino J (2013). Classificação climática e balanço hídrico climatológico no estado de Mato Grosso. Nativa, 1(1):34-43.
Crossref

 
 

Subedi A, Chavez, JL, Andales AA (2013). Preliminary performance evaluation of the Penman–Monteith evapotranspiration equation in southeastern Colorado. Hydrology Days, 970:84-90.

 
 

Tabari H (2010). Evaluation of reference crop evapotransoiration equations in various climates. Water Resources Management, 24(10):2311-2337.
Crossref

 
 

Tabari H, Kisi O, Ezani A, Hosseinzadeh Talaee P (2012). SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi– arid highland environment. Journal of Hydrology, 444-445:78-89.
Crossref

 
 

Tabari H, Martinez C, Ezani A, Hosseinzadeh Talaee P (2013). Applicability of support vector machines and adptative neurofuzzy inference system for modeling potato crop evapotranspiration. Irrigation Science, 31:575-588.
Crossref

 
 

Tezel G, Buyukyildiz M (2016). Monthly evaporation forecasting using artificial neural networks and support vector machines. Theoretical and Applied Climatology, 124:69- 80.
Crossref

 
 

Todorovic M, Karic B, Pereira LS (2013). Reference evapotranspiration estimate with limited weather data across a range of Mediterranean climates. Journal of Hydrology, 48:166 -176.
Crossref

 
 

Traore S, LuoY, Fipps G (2016). Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages. Agricultural Water Management, 163:363-379.
Crossref

 
 

Valipour M (2015). Temperature analysis of reference evapotranspiration models. Meteorological Applications, 22:385-394.
Crossref

 
 

Vicente Serrano SM, Molina Azorin C, Lorenzo Sanchez A, Revuelto J, Moreno López JI, Hidalgo González JC, Tejeda Moran E, Espejo F (2014). Reference evapotranspiration variability and trends in Spain, 1961-2011. Global and Planetary Change, 121:26-40.
Crossref

 
 

Wen X, Si J, He Z, Wu J, Shao H, Yu H (2015). Support vector machine based models for modelling daily reference evapotranspiration with limited climatic data in extreme arid Regions. Water Resources Management, 29:3195-3209.
Crossref

 
 

Witten IH, Franki E, Hall MA (2011). Data Mining: Practical Machine Learning Tools and Techniques. 3rd edition 630 p.

 
 

Xu J, Peng S, Ding J, Wei Q, Yu Y (2013). Evaluation and calibration of simple methods for daily reference evapotranspiration estimation in humid East China. Archives of Agronomy and Soil Science, 59(6):845-858.
Crossref

 
 

Zhai L, Feng Q, Li Q, Xu C (2010). Comparison and modification of equations for calculating evapotranspiration (ET) with data from GANSU Province, Northwest China. Irrigation and drainage, 59:477-490.
Crossref