Estimación de la evapotranspiración de referencia para el Planalto Paulista a través de regresiones múltiples con datos faltantes estimados a través del análisis de componentes principales

Autores/as

DOI:

https://doi.org/10.33448/rsd-v11i8.31120

Palabras clave:

Análisis de componentes principales; Regresión múltiple; Datos perdidos.

Resumen

La evapotranspiración es un fenómeno físico que promueve la compleja transferencia de agua a la atmósfera a través de la relación entre el balance hídrico climatológico, la evaporación de aguas superficiales y la transpiración de los cultivos agrícolas. Es un parámetro importante para optimizar la gestión de los recursos hídricos y la planificación del riego. Obtener mediciones confiables de la evapotranspiración es una tarea compleja, ya que depende de variables no disponibles en algunos lugares. El objetivo de este trabajo fue aplicar la técnica multivariada de Análisis de Componentes Principales para completar los datos faltantes y proponer modelos más simples para estimar la evapotranspiración de referencia para el Planalto Ocidental Paulista, comparándolos con el modelo Penman-Monteith. Se aplicó un procedimiento basado en el Análisis de Componentes Principales para reconstruir la base de datos meteorológica para el período de 2013 a 2017 de 30 estaciones meteorológicas automáticas en el Planalto Ocidental Paulista, ubicado en el noroeste del Estado de São Paulo, Brasil. Posteriormente, se realizó un análisis exploratorio de las variables climáticas para verificar la agrupación de las variables climáticas más relevantes en los procesos físicos de evapotranspiración. Estos clusters fueron la base para la construcción de diferentes modelos de estimación de evapotranspiración de referencia a través de Regresiones Múltiples. Los resultados mostraron el mejor rendimiento de los modelos EToRLM4 (rRMSE = 5,23%) y EToRNLM4 (rRMSE = 6,39%). Los valores de los indicadores estadísticos de la base de validación de RLM4 y RNLM4 indican que ambos modelos de regresión múltiple pueden utilizarse para estimar la evapotranspiración de referencia.

Citas

Adeloye, A. J.; Rustum, R.; Kariyama, I. D (2012). Neural Computing Modeling of the reference crop evapotranspiration. Environmental Modelling e Software, v. 29, pp. 61-73.

Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop evapotranspiration: guidelines for computing crop water requirements. Rome: FAO. 300p. (Irrigation and Drainage Paper, 56).

Allen, R. G.; Pruitt, W. O. (1991). FAO-24 reference evapotranspiration factors. Journal of Irrigation and Drainage Engineering, v. 177, 758 - 773. doi: https://doi.org/10.1061/(ASCE)0733-9437(1991)117:5(758)

Almedeij, J. (2012). Modeling Pan Evaporation for Kuwait by Multiple Linear Regression. The Scientific World Journal, Article ID 574742, 9 pages, doi: 10.1100/2012/574742.

Althoff, D.; Bazame, H. C.; Filgueiras, R. et al. (2018). Heuristic methods applied in reference evapotranspiration modeling. Ciência e Agrotecnologia, v. 42, n. 3, pp. 314-324.

Benavides, J. G., & Lopez Diaz, Y. J. (1970). Formula para el calculo de la evapotranspiracion potencial adaptada al tropico (15º N - 15º S). Agronomia Tropical, Maracay, 20(5), 335-345.

Bilgili, M. (2010). Prediction of soil temperature using regression and artificial neural network models. Meteorology and Atmospheric Physic, v. 110, pp. 59-70. DOI: 10.1007/s00703-010-0104-x

Blaney, H. F.; Criddle, W. D. (1950). Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data. US Dep. of Agr. Tech. Pap. No. 96, p. 48.

Bogawski, P.; Bednorz, E. (2014). Comparison and Validation of Selected Evapotranspiration Models for Conditions in Poland (Central Europe). Water Resources Management, v. 28, pp. 5021-5038. doi: https://doi.org/10.1007/s11269-014-0787-8

Cristea, N. C.; Kampf, S. K.; Burges, S. J. (2013). Linear models for estimating annual and growing season reference evapotranspiration using averages of weather variables. International Journal of Climatology, v. 33, pp. 376–387. doi: https://doi.org/10.1002/joc.3430

García-Diego, F.; F. J.; Zarzo, M. (2010). Microclimate monitoring by multivariate statistical control: The renaissance frescoes of the Cathedral of Valencia (Spain). Journal of Cultural Heritage, v. 11, pp. 339-344. doi: https://doi.org/10.1016/j.culher.2009.06.002.

Hargreaves, G. H, Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, v. 1, n. 2, pp. 96-99. doi: http://dx.doi.org/10.13031/2013.26773

Ikudayisi, Akinola & Adeyemo, Josiah. (2016). Effects of Different Meteorological Variables on Reference Evapotranspiration Modeling: Application of Principal Component Analysis. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering. 10. 623-627.

Iqbal, M. (1983). An introduction to solar radiation. New York: Academic Press, 390 p.

Jensen, M. E.; Haise, H. R. (1963). Estimating evapotranspiration from solar radiation. Proceeding of the American Society of Civil Engineers, Journal of Irrigation and Drainage Division, v. 89, p. 15-41.

Josse, J.; Husson, F. (2016). missMDA: A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software, v. 70, pp. 1-31. doi: https://doi.org/10.18637/jss.v070.i01

Josse, J.; Husson, F. (2012). Handling missing values in exploratory multivariate data analysis methods. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2012, 153 (2), pp.79-99. ⟨hal-00811888⟩

Josse, J.; Husson, F.; Pagès, J. (2009). Gestion des donn´ees manquantes en Analyse en Composantes Principales. Journal de la Société Française de Statistique, v. 150, n. 2, pp. 28-51. http://www.numdam.org/item/JSFS_2009__150_2_28_0/

Khanmohammadi, N.; Rezaie, H.; Montaseri, M. et al. (2018). The application of multiple linear regression method in reference evapotranspiration trend calculation. Stochastic Environmental Research and Risk Assessment, v. 32, pp. 661-673. DOI:10.1007/s00477-017-1378-z

Kisi, O; Demir, V. (2016). Evapotranspiration Estimation using Six Different Multi-layer Perceptron Algorithms. Irrigation e Drainage Systems Engineering, v. 5, n. 2, pp. 1-6. https://doi.org/10.1080/02626667.2019.1678750

Kisi, O; Cimen, M. (2011). Evapotranspiration modeling using a wavelet regression model. Irrigation Science, v. 29, pp. 241-252. doi:10.1007/s00271-010-0232-6

Köppen, W.; Geiger, R. (1928). Klimate der Erde. Gotha: Verlag Justus Perthes. Wall-map 150cmx200cm.

Ladlani, I.; Houichi, L.; Djemili, L. et al. (2014). Estimation of Daily Reference Evapotranspiration (ETo) in the North of Algeria Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) Models: A Comparative Study. Arabian. Journal for Science and Engineering, DOI 10.1007/s13369-014-1151-2.

Laaboudi, A.; Brahim, M.; Draoui, B. (2012). Conceptual Reference Evapotranspiration Models for Different Time Steps. Petroleum & Environmental Biotechnology. Volume 3. 10.4172/2157-7463.1000123.

Landeras, G.; Ortiz-Barredo, A.; López, J. J. (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, v. 95, pp. 553-565. doi: 10.1016/j.agwat.2007.12.011

Mahida, H. R.; Patel, V. N. (2015). Impact of Climatological Parameters on Reference Crop Evapotranspiration using Multiple Linear Regression Analysis. SSRG International Journal of Civil Engineering (SSRG-IJCE), v. 2, pp. 21-24. doi:10.14445/23488352/IJCE-V2I1P103

Makkink, G. F. (1957). Testing the Penman formula by means of lysimeters. Journal of the Institution of Water Engineers, v. 11, n. 3, pp. 277-288.

Mallikarjuna, P.; Jyothy, S. A.; Reddy, K. C. S. (2013). Daily Reference Evapotranspiration Estimation using Linear Regression and ANN Models. Journal of The Institution of Engineers (India), v. 97, n. 4, pp. 215-221. doi: 10.1007/s40030-013-0030-2

Manikumari, N.; Vinodhini, G. (2016). Regression Models for Predicting Reference Evapotranspiration. In ternational Journal of Engineering Trends and Technology (IJETT), v. 38, n. 3, pp. 134-139. doi:0.14445/22315381/IJETT-V38P224

Marofi, S; Mohammad M, S.; Kourosh, M.; et al. (2011). Investigation of meteorological extreme events over coastal regions of Iran. Theoretical and Applied Climatology. 103. 401-412. DOI: 10.1007/s00704-010-0298-3.

Martí, P.; Gasque, M. (2010). Ancillary data supply strategies for improvement of temperature-based ETo ANN models. Agricultural Water Management, v. 97, pp. 939-955. doi: https://doi.org/10.1016/j.agwat.2010.02.002

Martí, P.; González-altozano, M. (2011). Reference evapotranspiration estimation without local climatic data. Irrigation Science, v. 29, pp. 479-495. doi: 10.1007/s00271-010-0243-3

Martí, P.; Zarzo, M. (2012). Multivariate statistical monitoring of ETo: A new approach for estimation in nearby locations using geographical inputs. Agricultural and Forest Meteorology, v. 152, pp. 125 - 134. doi: 10.1016/j.agrformet.2011.08.008

Melo Prado, B. Q.; Fernandes, H. R.; Araújo, T. G. et al. (2016). Avaliação de variáveis climatológicas da cidade de Uberlândia (MG) por meio da análise de componentes principais. Engenharia Sanitária e Ambiental, v. 21, n. 21, pp. 407 - 413. doi:https://doi.org/10.1590/s1413-41522016147040.

Mohan, S.; Arumugam, N. (1996). Relative Importance of Meteorological Variables in Evapotranspiration: Factor Analysis Approach. Water Resources Management, v. 10, pp. 1 - 20. doi: https://doi.org/10.1007/BF00698808

Oracle Corporation. (2019). Chapter 1 General Information. MySQL 5.7 Reference Manual, 2019. Disponível em: https://dev.mysql.com/doc/refman/5.7/en/introduction.html. Acesso em: 08 abr. 2019.

Ozbayoglu, G.; Ozbayoglu, M. E. (2006). A new approach for the prediction of ash fusion temperatures: a case study using Turkish lignites. Fuel, v. 85, pp. 545–552. doi: 10.1016/j.fuel.2004.12.020

Penman, H. L. (1948). Natural evaporation from open water, bare soil, and grass. Proceedings of the Royal Society, London, v. 193, n. 1, p. 120-146. doi: https://doi.org/10.1098/rspa.1948.0037.

Priestley, C. H. B., Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, v. 100, n. 2, pp. 81-92. doi: http://dx.doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2

R Core Team. (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Rolim, G. S.; Camargo, M. B. P.; Lania, D. G.; et al. (2007). Classificação climática de Köppen e de Thornthwaite e sua aplicabilidade na determinação de zonas agroclimáticas para o estado de São Paulo. Bragantia, Campinas, v. 66, n. 4, p. 711-720. doi: https://doi.org/10.1590/S0006-87052007000400022.

Santos, C. M.; Souza, J. L.; Ferreira Júnior, R. A. et al. (2014). On modeling global solar irradiation using air temperature for Alagoas State, Northeastern Brazil. Energy, v. 71, 338-398. doi: 10.1016/j.energy.2014.04.116

Shiri, J.; Nazemi, A. H.; Sadraddini, A. A.; et al. (2014). Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Eletronics in Agriculture, v. 108, pp. 230-241.

Shiri, J.; Sadraddini, A. A.; Nazemi, A. H. et al. (2015). Independent testing for assessing the calibration of the Hargreaves-Samani equation: New heuristic alternatives for Iran. Computers and Eletronics in Agriculture, v. 117, pp. 70-80. doi: 10.1016/j.compag.2015.07.010

Silva, I. N.; Spatti, D. H.; Flausino, R. A. (2016a). Redes Neurais Artificiais para engenharia e ciências aplicadas – fundamentos teóricos e aspectos práticos. 2nd ed. Artiliber 431 p.

Silva, H. J. F.; Santos, M. S.; Cabral júnior, J. B. et al. (2016b). Modeling of reference evapotranspiration by multiple linear regression. Journal of Hyperspectral Remote Sensing, v. 6, n. 1, pp. 44-58. doi: https://doi.org/10.5935/2237-2202.20160005%20

Silva, M. B. P.; Escobedo, J. F.; Santos, C. M. et al. (2017). Performance of the Angstrom-Prescott Model (A-P) and SVM and ANN techniques to estimate the daily Global Solar Irradiation in Botucatu/SP/Brazil. Journal of Atmospheric and Solar–Terrestrial Physics, v. 160, pp. 11-23. doi: 10.1016/j.jastp.2017.04.001

Snyder, R. L. (1992). Equation for evaporation pan to evapotranspiration conversions. Journal of Irrigation and Drainage Engineering, v. 118, n. 6, pp. 977-980. doi: http://dx.doi.org/10.1061/(ASCE)0733-9437(1992)118:6(977)

Sriram, A. V.; Rashmi, C. N. (2014). Estimation of Potential Evapotranspiration by Multiple Linear Regression Method. IOSR Journal of Mechanical and Civil Engineering, v. 11, pp. 65-70. doi: 10.9790/1684-11246570

Tabari, H.; Grismer, M. E. (2013). Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Science, v. 31, pp. 107-117. doi:10.1007/s00271-011-0295-z

Tabari, H.; Kisi, O.; Ezani, A. et al. (2012). SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hidrology, v. 444-445, pp. 78-89. doi : 10.1016/j.jhydrol.2012.04.007

Tangune', B. F.; Escobedo, J. F. (2018). Reference evapotranspiration in São Paulo State: Empirical methods and machine learning techniques. International Journal of Water Resources and Environmental Engineering, v. 10, n. 4, pp. 33-44.

Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review, v. 55–94, pp. 38.

Xu, J.; Peng, S.; Wang, W. et al. (2013). Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data. Archives of Agronomy and Soil Science, v. 59, n. 11, pp. 1487-1501. doi: 10.1080/03650340.2012.727400

Ye, X. A.; Li, X.; Liu, J. et al. (2014). Variation of reference evapotranspiration and its contributing climatic factors in the Poyang Lake catchment, China. Hydrological Processes, v. 28, pp. 6151-6162. doi: https://doi.org/10.1002/hyp.10117

Publicado

25/06/2022

Cómo citar

SILVA, M. B. P. da; SOUZA, V. C. de .; CREMASCO, C. P. .; CALÇA, M. V. C.; SANTOS, C. M. dos; CREMASCO, C. P.; GABRIEL FILHO , L. R. A.; RODRIGUES, S. A.; ESCOBEDO, J. F. Estimación de la evapotranspiración de referencia para el Planalto Paulista a través de regresiones múltiples con datos faltantes estimados a través del análisis de componentes principales. Research, Society and Development, [S. l.], v. 11, n. 8, p. e43211831120, 2022. DOI: 10.33448/rsd-v11i8.31120. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/31120. Acesso em: 6 jul. 2024.

Número

Sección

Ciencias Exactas y de la Tierra