Geoscience Reference
In-Depth Information
TABLE 8.2
Input-Output Variables Used in GEP
Estimating of Pan Evaporation
Symbol
Units
Description
T max
°C
Daily maximum temperature
T min
°C
Daily minimum temperature
T avg
%
Average daily temperature
RH max
%
Maximum daily relative humidity
RH min
%
Minimum daily relative humidity
RH avg
%
Average daily relative humidity
Rs
MJ m −2
Daily solar radiation
U avg
m s −1
Average daily wind speed
E PA N
mm day −1
Daily measured pan evaporation
automatically using a Class A Pan. Further details on our data set are provided in Table 8.2. NLWS
records are used principally because the data that were available for this particular monitoring
station covered a longer period of observation. In addition, E PA N data were logged automatically at
NLWS, reducing the potential error that sometimes occurs with manual logging.
The GEP algorithm implemented in GeneXproTools 4.0 was used to predict E PA N from daily
meteorological data (Beriro et al., 2013). Software settings are shown in Table 8.3. Ten independent
solutions were evolved. The preferred model was selected using testing values for R -squared (0.77),
mean absolute error (1.44), RMSE (2.00) and a one-at-a-time response function sensitivity analysis
of the evolved solutions (Beriro et al., 2013). The goodness-of-fit statistics are summarised in
Table 8.4. The preferred GEP model is shown in Equation 8.5 and scatterplots comparing observed
E PA N with modelled values are provided in Figure 8.6.
Of particular interest in this study is that only 40% of models passed their respective sensitivity
analysis - meaning there was a 60% model redundancy rate (see Section 8.5.4 for a description of
how the sensitivity analysis was undertaken). Model redundancy is therefore a real issue - assuming
that is - that it is important for an evolved model to accurately reflect the conceptual underpin-
nings of its associated natural system. Results show that the performance of the preferred model
compared well to other published studies (Abudu et al., 2011; Moreno et al., 2010) supporting the
conclusion that GEP is able to produce a good predictive model for E PA N :
Ê PAN
=
Sub-ET
1
+
Sub-ET
2
+
Sub-ET
3
(8.5)
2
(
)
2
(
) ()
TRHT
Rs
max
min
max
Sub-ET1
=
A
tan
(8.5a)
(
)
7 488647
.
RH
min
Sub-ET
2 = U
(8.5b)
avg
2
2
(
)
(
)
(
)
(
)
UT
0 286712
8 177459
.
T
avg vg
min
A
Sub-ET
3
=
tan
(8.5c)
RH
+
.
avg
in which Ê PA N represents our calculated estimate of measured E PA N .
 
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