Agriculture Reference
In-Depth Information
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
affect drought are derived from weather data and satellite data. For ex-
ample, these variables can be average monthly temperature, total monthly
precipitation, and variables based on satellite data during the growing sea-
son. V. K. Boken, in a just concluded analysis, derived 32 variables to de-
velop pattern recognition models to predict drought for selected crop dis-
tricts in Saskatchewan, Canada. Both two-variable and multiple-variable
cases were considered. In the two-variable case, an error-correction (EC)
procedure (Kumar et al., 1998; Duda et al., 2001) was applied and, in
the multiple-variable case, both linear (linear discriminant analysis) and
nonlinear (nearest neighbor analysis) techniques were attempted using SAS
software (SAS Institute Inc., Cary, North Carolina, United States). In the
case of the EC procedure, two variables were selected at a time to ex-
amine the presence of a solution vector to linearly separate drought and
nondrought events. An iterative procedure was applied using a computer
program, but no solution vector was found. This reiterates the complex-
ity involved in the analysis of agricultural drought. To proceed further,
the multiple-variable case was investigated and a subset of significant or
most suitable variables was determined. To find the subset of significant
variables, the STEPDISC procedure of SAS software was used. Using these
significant variables for each crop district, the linear and nonlinear tech-
niques were applied to develop models for classifying an event as drought
or nondrought.
[42],
Line
——
11.
——
Long
PgEn
Linear Discriminant and Nearest Neighbor Analysis
[42],
A function was obtained by applying the linear discriminant technique on
th e subset of variables, which can be used to classify a subset of variables
as drought or nondrought. To develop such a function, the whole data
se t for multiple years was used. The training set was used to develop the
lin ear discriminant function (LDF), and the testing set was used to test
th e classification performance of the LDF. For applying linear discriminant
an alysis, the within-category distribution must be normal. A nonparamet-
ric technique (nearest neighbor) was also attempted. Using this technique
on e can classify a subset of variables as drought or nondrought based on
th e category of the neighboring subsets, and the normality assumption is
no t required. Up to 83% of accuracy to classify or predict drought was
ob tained.
St ochastic or Proababilistic Analysis
In the mathematical modeling of agricultural droughts, most often soil
moisture records are taken as the basis where a time series of the soil mois-
ture contents, X 1 , X 2 , X 3 ,..., X n is truncated at a threshold soil moisture
value, X 0, as shown in figure 4.1. An agricultural drought can be defined on
the basis of some objective, random, probabilistic, or statistical properties
or features:
 
Search WWH ::




Custom Search