Geology Reference
In-Depth Information
Table 6.1 Entropyanalysis results on the daily rainfall runoff data from the Brue catchment
Scenario
Transinformation Conditional
entropy
1
10000000 7.1891 3.9742 0.2489 3.4638
2
01000000 7.3091 3.9741 0.1289 3.4639
3
00100000 6.4596 3.4639 0.4682 3.4639
4
00010000 7.3806 3.9741 0.0574 3.4639
5
00001000 6.7028 3.4638 0.2249 3.4639
6
00000100 7.3981 3.9738 0.0395 3.4638
7
00000010 6.7585 3.4639 0.1692 3.4638
8
00000001 6.7905 3.4638 0.1372 3.4639
Note Mask indicates different combinations of the input effects (inclusion and exclusion indicated
by 1 or 0 in the mask). From left to right The Present value of rainfall information P(t), (t
Mask
Joint
entropy
Marginal
entropy
1) step
−
of rainfall information (P(t
1)), (t
1) step of runoff information (Q(t
1)), (t
2) step of
−
−
−
−
rainfall information (P(t
2)), (t
2) step of runoff information (Q(t
1)), (t
3) step of rainfall
−
−
−
−
information (P(t
3)), (t
3) step of runoff information (Q(t
3)), (t
4) step of runoff
−
−
−
−
information (Q(t
4))
−
for modelling is shown in the Fig.
6.6
. The test has shown that a maximum
transinformation to a value 0.869 is at around 1,040 data points. At the same time
the GT identi
cient for making a reliable model.
The variation of different attributes like marginal entropy conditional entropy and
joint entropy variation with the number of data is shown in Fig.
6.7
. The
ed that 1,056 data points are suf
gure
shows that the higher value information is associated with joint entropy followed by
marginal and conditional entropy values. All three curves in Fig.
6.7
show the same
trend throughout the data points.
