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Wavelet-multivariate relevance vector machine hybrid model for forecasting daily evapotranspiration

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Abstract

Evapotranspiration (ET) is one of the main components of the hydrological cycle. It is a complex process driven mainly by weather parameters, and as such, is characterized by high non-linearity and non-stationarity. This paper introduces a methodology combining wavelet multiresolution analysis with a machine learning algorithm, the multivariate relevance vector machine (MVRVM), in order to predict 16 days of future daily reference evapotranspiration (ETo). This methodology lays the ground for forecasting the spatial distribution of ET using Landsat satellite imagery, hence the choice of 16 days, which corresponds with the Landsat overpass cycle. An accurate prediction of daily ETo is needed to improve the management of irrigation schedules as well as the operations of water supply facilities like canals and reservoirs. In this paper, various wavelet decompositions were performed and combined with MVRVM to develop hybrid models to predict ETo over a 16-days period. These models were compared to a MVRVM model, and models accuracy and robustness were evaluated. The addition of 10 days of forecasted air temperature as additional inputs to the forecasting models was also investigated. The results of the wavelet-MVRVM hybrid modeling methodology showed that a reliable forecast of ETo up to 16 days ahead is possible.

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Acknowledgments

Computational, storage and other resources from the Division of Research Computing in the Office of Research (DoRC) and Graduate Studies at Utah State University (USU) are gratefully acknowledged. The authors would like to thank Mr. John Hanks, director of the DoRC at USU, for his help in answering questions concerning cluster computing. The authors would also like to acknowledge the Center of Teaching, Research and Learning at the American University (Washington, DC) for providing access to their High-Performance Computing System (NSF grant BCS-1039497). This research could not have been done without the invaluable support of the Utah Water Research Laboratory (UWRL), and access to the weather data through the Community Environmental Monitoring Program monitored by the Desert Research Institute (DRI) of the Nevada System of Higher Education.

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Authors and Affiliations

  1. Civil and Environmental Engineering Department, College of Engineering, Utah State University, Logan, UT, 84322-4100, USA

    Roula Bachour & Wynn R. Walker

  2. Department of Mathematics and Statistics, American University, Washington, DC, USA

    Inga Maslova

  3. Civil and Environmental Engineering Department, Utah Water Research Laboratory, Utah State Univ, 1600 Canyon Rd., Logan, UT, 84321, USA

    Andres M. Ticlavilca & Mac McKee

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  1. Roula Bachour
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  2. Inga Maslova
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  3. Andres M. Ticlavilca
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  4. Wynn R. Walker
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  5. Mac McKee
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Correspondence to Roula Bachour.

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Bachour, R., Maslova, I., Ticlavilca, A.M. et al. Wavelet-multivariate relevance vector machine hybrid model for forecasting daily evapotranspiration. Stoch Environ Res Risk Assess 30, 103–117 (2016). https://doi.org/10.1007/s00477-015-1039-z

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