Support Vector Regression to Downscaling Climate Big Data: An Application for Precipitation and Temperature Future Projection Assessment

  • Stalin Jimenez
  • Alex AvilesEmail author
  • Luciano Galán
  • Andrés Flores
  • Carlos Matovelle
  • Cristian Vintimilla
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1099)


The techniques for downscaling climatic variables are essential to support tools for water resources planning and management in a climate change context in the entire world. Support vector machines (SVM) through regression approach (SVR), constitute an artificial intelligence method to downscaling climatic variables. Since that statistical downscaling based on regression methodologies is susceptible to the predictor variables, the aim of this study was exploring a big database of predictor variables to achieve the best performance of a statistical downscaling model using SVR to predict precipitation and temperature future projections. Data from regional climate models of Ecuador and information of three meteorological stations was used to apply this approach in the Tomebamba river sub-basin, located in southern Ecuadorian Andean region. The results show that the downscaling model has a better performance with the climatic averages. The precipitation extremes do not estimate in a good manner, but the model achieves an effective behavior with the temperature extremes values. These results could serve to improve water balance projections in the future for formulating suitable measures for climate change decision-making.


Artificial intelligence SVR Statistical downscaling Climate change Climate big data Andean basin 



The authors would like to thank the MAE and INAMHI for the information provided.


This research was funded by the Ecuadorian Corporation for the Development of Research and Academia (CEDIA) and the University of Cuenca through its Research Department (DIUC) via project “Evaluación de los efectos de las actividades socioeconómicas en el cambio del uso del suelo y del cambio climático en las amenazas a inundaciones y sequías en la cuenca del río Tomebamba”.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Stalin Jimenez
    • 1
  • Alex Aviles
    • 1
    Email author
  • Luciano Galán
    • 1
  • Andrés Flores
    • 1
  • Carlos Matovelle
    • 2
  • Cristian Vintimilla
    • 3
  1. 1.Carrera de Ingeniería Ambiental, Facultad de Ciencias QuímicasUniversidad de CuencaCuencaEcuador
  2. 2.Carrera de Ingeniería AmbientalUniversidad Católica de CuencaCuencaEcuador
  3. 3.Carrera de Ingeniería CivilUniversidad Católica de CuencaAzoguesEcuador

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