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Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China

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Academic year: 2019

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Figure 1. points and grids of ACCESS1.0, ACCESS1.3, HadGEM2-CC and HadGEM2-ES; (Figure1.  (a) and (b) Location of the Yingluoxia (YLX) Watershed; (c) mean monthly rainfall andtemperature at the Yeniugou weather station from 1961 to 2013; (d) the global cli
Table 1. Summary of global climate models from CMIP5 simulation set.
Figure 2.Schematic diagram for the modelling process followed to generate future referenceevapotranspiration (ET SchematicevapotranspirationFigure 2.    diagram  for  the  modelling  process  followed  to  generate  future  reference  (ET0) projections. 0) projections.
Figure 3.   Scatter plots for the historical ET0 (1961–2005) calculated by the Penman–Monteith equation with the observed meteorological data downscaled by theFigureextreme-learning machine (ELM) and support vector regression (SVR) approaches derived for t
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