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Predicting the reference evapotranspiration based on tensor decomposition

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Abstract

Most of the available models for reference evapotranspiration (ET0) estimation are based upon only an empirical equation for ET0. Thus, one of the main issues in ET0 estimation is the appropriate integration of time information and different empirical ET0 equations to determine ET0 and boost the precision. The FAO-56 Penman–Monteith, adjusted Hargreaves, Blaney–Criddle, Priestley–Taylor, and Jensen–Haise equations were utilized in this study for estimating ET0 for two stations of Belgrade and Nis in Serbia using collected data for the period of 1980 to 2010. Three-order tensor is used to capture three-way correlations among months, years, and ET0 information. Afterward, the latent correlations among ET0 parameters were found by the multiway analysis to enhance the quality of the prediction. The suggested method is valuable as it takes into account simultaneous relations between elements, boosts the prediction precision, and determines latent associations. Models are compared with respect to coefficient of determination (R 2), mean absolute error (MAE), and root-mean-square error (RMSE). The proposed tensor approach has a R 2 value of greater than 0.9 for all selected ET0 methods at both selected stations, which is acceptable for the ET0 prediction. RMSE is ranged between 0.247 and 0.485 mm day−1 at Nis station and between 0.277 and 0.451 mm day−1 at Belgrade station, while MAE is between 0.140 and 0.337 mm day−1 at Nis and between 0.208 and 0.360 mm day−1 at Belgrade station. The best performances are achieved by Priestley–Taylor model at Nis station (R 2 = 0.985, MAE = 0.140 mm day−1, RMSE = 0.247 mm day−1) and FAO-56 Penman–Monteith model at Belgrade station (MAE = 0.208 mm day−1, RMSE = 0.277 mm day−1, R 2 = 0.975).

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References

  • Acar E, Yener B (2009) Unsupervised multiway data analysis: a literature survey. IEEE Trans Knowl Data Eng 21(1):6–20

    Article  Google Scholar 

  • Allen, R. G., Pereira, L. S., Raes, D., Smith, M., 1998 Crop evapotranspiration. Guidelines for Computing Crop Water Requirements, FAO Irrigation and Drainage Paper 56, Roma, Italy

  • Berry M, Dumais S, O’Brien G (1995) Using linear algebra for intelligent information retrieval. SIAM Rev 37(4):573–595

    Article  Google Scholar 

  • Blaney HF, Criddle WD (1950) Determining water requirements in irrigated areas from climatological and irrigation data. Soil conservation service technical paper 96, soil conservation service. Washington, US Department of Agriculture

    Google Scholar 

  • Bormann H (2011) Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Clim Chang 104:729–753

    Article  Google Scholar 

  • Carroll JD, Chang J (1970) Analysis of individual differences in multidimensional scaling via an n-way generalization of ‘Eckartâ Young’ decomposition. Psychometrika 35:283–319

    Article  Google Scholar 

  • Chaudhury A, Oseledets I, Ramachandran R (2014) A computationally efficient technique for the solution of multi-dimensional PBMs of granulation via tensor decomposition. Comput Chem Eng 61:234–244

    Article  Google Scholar 

  • Cichocki, A., Zdunek, R., Phan, A.H., Amari, S., 2009 Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. John Wiley & Sons, pp. 500. doi:10.1002/9780470747278

  • Cong F, Lin Q-H, Kuang L-D, Gong X-F, Astikainen P, Ristaniemi T (2015) Tensor decomposition of EEG signals: a brief review. J Neurosci Methods 248:59–69

    Article  Google Scholar 

  • Correa FE, Oliveira MDB, Gama J, Corrêa PLP, Rady J (2016) Analyzing the behavior dynamics of grain price indexes using Tucker tensor decomposition and spatio-temporal trajectories. Comput Electron Agric 120:72–78

    Article  Google Scholar 

  • de Almeida ALF, Favier G (2013) Double Khatri–Rao space–time-frequency coding using semi-blind PARAFAC based receiver. IEEE Signal Process Lett 20:471–474

    Article  Google Scholar 

  • Doorenbos J, Pruitt WO (1977) Crop water requirements. FAO irrigation and drainage. Paper no. 24 (rev.). FAO, Rome

    Google Scholar 

  • Douglas EM, Jacobs JM, Sumner DM, Ray RL (2009) A comparison of models for estimating potential evapotranspiration for Florida land cover types. J Hydrol 373:366–376

    Article  Google Scholar 

  • Favier G, Fernandes CAR, de Almeida ALF (2016) Nested Tucker tensor decomposition with application to MIMO relay systems using tensor space–time coding (TSTC). Signal Process 128:318–331

    Article  Google Scholar 

  • Fisher JB, DeBiase TA, Qi Y, Xu M, Goldstein AH (2005) Evapotranspiration models compared on a Sierra Nevada forest ecosystem. Environ Model Softw 20:783–796

    Article  Google Scholar 

  • Furnas, G., Deerwester, S., Dumais, S., 1988 Information retrieval using a singular value decomposition model of latent semantic structure. In the proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval

  • Guo T, Han L, He L, Yang X (2014) A GA-based feature selection and parameter optimization for linear support higher-order tensor machine. Neurocomputing 144:408–416

    Article  Google Scholar 

  • Harshman, R.A., 1970. Foundations of the PARAFAC procedure: models and conditions for an “explanatory” multi-modal factor analysis. In: UCLA working papers in phonetics, 1–84.

  • Hitchcock FL (1927) The expression of a tensor or a polyadic as a sum of products. J Math Phys 6(1):164–189

    Article  Google Scholar 

  • Jensen ME, Haise HR (1963) Estimating evapotranspiration from solar radiation. J Irrig Drain Eng 93:15–41

    Google Scholar 

  • Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCE manuals and reports on engineering practice no.70. ASCE, New York

    Google Scholar 

  • Katerji N, Rana G (2014) FAO-56 methodology for determining water requirement of irrigated crops: critical examination of the concepts, alternative proposals and validation in Mediterranean region. Theor Appl Climatol 116:515–536

    Article  Google Scholar 

  • Kolda T, Bader B (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

    Article  Google Scholar 

  • Kolda, T. G., Sun, J., 2008 Scalable tensor decompositions for multi-aspect data mining. In the proceeding of the 8th IEEE International Conference on Data Mining (ICDM)

  • Kroonenberg PM (2008) Applied multiway data analysis. Wiley, New York 2008

    Book  Google Scholar 

  • Lathauwer LD, Moor BD, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4):1253–1278

    Article  Google Scholar 

  • Lei J, Liu WY, Liu S, Wang XY (2015) Dynamic imaging method using the low n-rank tensor for electrical capacitance tomography. Flow Meas Instrum 41:104–114

    Article  Google Scholar 

  • Lu J, Sun G, McNulty SG, Amatya DM (2005) A comparison of six potential evapotranspiration methods for regional use in the southeatern United States. J Am Water Resour Assoc 41:621–633

    Article  Google Scholar 

  • Muti D, Bourennane S (2007) Survey on tensor signal algebraic filtering. Signal Process 87(2):237–249

    Article  Google Scholar 

  • Nanopoulos A (2011) Item recommendation in collaborative tagging systems. IEEE Trans Syst Man Cybern Part A: Syst Hum 41(4):760–771

    Article  Google Scholar 

  • Pereira LS, Allen RG, Smith M, Raes D (2015) Crop evapotranspiration estimation with FAO56: past and future. Agric Water Manag 147:4–20

    Article  Google Scholar 

  • Perera KC, Western AW, Nawarathna B, George B (2015) Comparison of hourly and daily reference crop evapotranspiration equations across seasons and climate zones in Australia. Agric Water Manag 148:84–96

    Article  Google Scholar 

  • Popova Z, Kercheva M, Pereira LS (2006) Validation of the FAO methodology for computing ETo with limited data: application to South Bulgaria. Irrig Drain 55:201–215

    Article  Google Scholar 

  • Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  • Qiao Y-N, Yong Q, Di H (2011) Tensor Field Model for higher-order information retrieval. J Syst Softw 84(12):2303–2313

    Article  Google Scholar 

  • Rahimikhoob A, Behbahani MR, Fakheri J (2012) An evaluation of four reference evapotranspiration models in a subtropical climate. Water Resour Manag 26:2867–2881

    Article  Google Scholar 

  • Rivington M, Bellocchi G, Matthews KB, Buchan K (2005) Evaluation of three model estimations of solar radiation at 24 UK stations. Agric For Meteorol 132:228–243

    Article  Google Scholar 

  • Sidiropoulos ND, Giannakis GB, Bro R (2000) Blind PARAFAC receivers for DSCDMA systems. IEEE Trans Signal Process 48:810–823

    Article  Google Scholar 

  • Smilde A, Bro R, Geladi P (2004) Multi-way analysis with applications in the chemical sciences. Wiley, New York

    Book  Google Scholar 

  • Sun M, Van Hamme H (2013) Joint training of non-negative Tucker decomposition and discrete density hidden Markov models. Comput Speech Lang 27(4):969–988

    Article  Google Scholar 

  • Sun, J., Shen, D., Zeng, H., Yang, Q., Lu, Y., Chen, Z., 2005 Cubesvd: A novel approach to personalized Web search. In the proceedings of the 14th international conference on World Wide Web

  • Sun, J., Tao, D., Faloutsos, C., 2006 Beyond streams and graphs: dynamic tensor analysis. In the proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining

  • Tabari H (2010) Evaluation of reference crop evapotranspiration equations in various climates. Water Resour Manag 24:2311–2337

    Article  Google Scholar 

  • Tabari H, Grismer ME, Trajkovic S (2013) Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrig Sci 31(2):107–117

    Article  Google Scholar 

  • Tan H, Cheng B, Wang W, Zhang Y-J, Ran B (2014) Tensor completion via a multi-linear low-n-rank factorization model. Neurocomputing 133:161–169

    Article  Google Scholar 

  • Trajkovic S (2007) Hargreaves versus Penman–Monteith under humid conditions. J Irrig Drain Eng 133(1):38–42

    Article  Google Scholar 

  • Trajkovic S, Kolakovic S (2009) Wind-adjusted Turc equation for estimating reference evapotranspiration. Hydrol Res 40(1):45–52

    Google Scholar 

  • Trajkovic S, Stankovic M, Todorovic B (2000) Estimation of FAO Blaney-Criddle b factor by RBF network. J Irrig Drain Eng 126(4):268–271

    Article  Google Scholar 

  • Tucker LR (1966) Some mathematical notes on three-mode factor analysis. Psychometrika 31(3):279–311

    Article  Google Scholar 

  • Utset A, Farre I, Martinez-Cob A, Cavero J (2004) Comparing Penman–Monteith and Priestley–Taylor approaches as referenceevapotranspiration inputs for modeling maize water-use under Mediterranean conditions. Agric Water Manag 66(3):205–219

    Article  Google Scholar 

  • Valiantzas JD (2013) Simplified forms for the standardized FAO-56 Penman-Monteith reference evapotranspiration using limited weather data. J Hydrol 505:13–23

    Article  Google Scholar 

  • Vanderlinden K, Giraldez JV, Van Meirvenne M (2004) Assessing reference evapotranspiration by the Hargreaves method in southern Spain. J Irrig Drain Eng 130(3):184–191

    Article  Google Scholar 

  • Wang H, Ahuja N (2008) A tensor approximation approach to dimensionality reduction. Int J Comput Vis 76(3):217–229

    Article  Google Scholar 

  • Wang L, Bai J, Wu J, Jeon G (2015) Hyperspectral image compression based on lapped transform and Tucker decomposition. Signal Process Image Commun 36:63–69

    Article  Google Scholar 

  • Wu Q, Zhang L, Cichocki A (2014) Multifactor sparse feature extraction using Convolutive Nonnegative Tucker Decomposition. Neurocomputing 129:17–24

    Article  Google Scholar 

  • Xystrakis F, Matzarakis A (2010) Evaluation of 13 empirical reference potential evapotranspiration equations on the island of Crete in southern Greece. J Irrig Drain Eng 137:211–222

    Article  Google Scholar 

Download references

Acknowledgments

The study was supported by ICT COST Action IC1408 Computationally-intensive methods for the robust analysis of non-standard data (CRoNoS) and the Ministry of Education, Science and Technological Development, Republic of Serbia (Grant No. TR37003).

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Correspondence to Dalibor Petković.

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Misaghian, N., Shamshirband, S., Petković, D. et al. Predicting the reference evapotranspiration based on tensor decomposition. Theor Appl Climatol 130, 1099–1109 (2017). https://doi.org/10.1007/s00704-016-1943-2

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