Post on 10-Mar-2018
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
R² = 0.0042
0
5
10
15
20
25
0 5 10 15 20 25
R² = 0.01
0
2
4
6
8
10
12
0 2 4 6 8 10 12
?
yb
yg
g
ygb
yybbgg
ybg
CC
CCQ
QQQ
CQCQCQ
QQQ
Liu et al. (2008)
1321 fff1
3
1
32
1
21
1
1 tCfCfCfC 2
3
2
32
2
21
2
1 tCfCfCfC
))(())((
))(())((2
3
2
1
1
3
1
2
2
3
2
2
1
3
1
1
2
3
21
3
1
2
2
3
2
2
1
3
1
1CCCCCCCC
CCCCCCCCf tt
11
3
1
2
1
3
1
1
1
3
1
2
1
3
1
2 fCC
CC
CC
CCf t
213 1 fff
•
PCA Matlab PCA R (ade4) PCA R (prcomp)
[COEFF,SCORE] = princomp(X) [COEFF,SCORE,latent] = princomp(X) [COEFF,SCORE,latent,tsquare] = princomp(X) [...] = princomp(X,'econ')
data2<-read.table("data2.txt",header=T) attach(data2) names(data2) pca_data2<-dudi.pca(data2,scannf=T) pca_data2 pca_data2$li pca_data2$co s.corcircle(pca_data2$co) par(mfrow=c(2,2)) s.corcircle(pca_data2$co) pca_data2$eig
data2<-read.table("data2.txt",header=T) attach(data2) names(data2) prcomp(data2) summary(prcomp(data2, scale = TRUE))
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
100.0 150.0 200.0 250.0
Dis
char
ge(m
3 /se
c)
Calender day
Glacier melt
Snow melt
Ground water
◦ −5,+5 1.221
0, 300 150 ◦ −5,+5 2.823
0, 1 0.032
◦ 0, 10 4.825 ◦ 0, 10 3.319
0, 500 300
NSE = 1 −
Xiobs − Xi
sim 2ni=1
Xiobs − Xi
mean 2ni=1
PBIAS = Xi
obs − Xisim × 100n
i=1
Xiobsn
i=0
SR =RMSE
STDEVobs=
Xiobs − Xi
sim 2ni=1
Xiobs − Xi
mean 2ni=1
Xiobs m3s−1
Xisim m3s−1
Ximean
≤
•
•
•
•