Evaluation of Several Classification Methods for
Land Development Constraint Parameters
A. Murni, W. Setiawan, D. Hardianto and B. Kusumoputro
Faculty of Computer Science, University of Indonesia
PO Box 3442, Jakarta 10002, Indonesia Fax: (62 21) 786-3415
E-mail: aniati@cs.ui.ac.id
ABSTRACT
This paper evaluates two classification approaches for land development constraint parameters. The
number of constraint parameters are limited to six parameters which include the quality measures of labor
power, economic growth, natural resources accessibility, degree of environment sustainability, market
orientation, and investment opportunity. The data measurement is in the form of nominal type and the
values range from the lowest quality which has a measure value of 1 to the highest quality which has a
measure value of 5 value. The model of data distribution is uncertain and we have used both unsupervised
and supervised approaches for doing the classification of constraint types. Two methods have been
selected which include a hybrid neural network system and a knowledge-based system. A hybrid system of
self-organizing map and back propagation architecture is used as the neural network classifier and both
non-hierarchical and hierarchical rule-based system is used as the knowledge based system. This paper
evaluates the performance of the two methods based on the table of matching accuracy between the method
used and the expert. The result shows that the hybrid neural network system performs the best with the
correct classification of 92.2%.
1. Introduction
Multistage, multitemporal and multisensor remote sensing data together with census data are
interpreted into thematic maps and used as input data for a geographical information system.
Potential region maps were created using multilayer of land information attributes analysis and
activity development suitability formula.
In regional planning, it is important to identify potential regions which have among others the
following criteria of quality: (i) superior land potential; (ii) dominant factors for triggering
economic growth; (iii) specific prime commodity; and (iv) supported regional environment. The
successfully of regional activity development could be assessed by evaluating the constraint
parameter structure [1-3].
Each region activity development has its own specific effective constraint type. Before assessing
the constraint parameter structure, firstly we have to group the constraint parameters into several
types of constraint. An expert who knows the observed region very well usually has an intuitive
in deciding the number of constraint types. In the absence of an expert knowledge, the number of
constraint types and the assessment of constraint structure become an iterative process. Further
analysis of a potential region map and its related constraint structure has to be done to obtain the
regional planning map [4].
This paper is organized as follows. Section 2 describes the proposed framework for classifying
and assessing the land development constraint types, while Section 3 discusses the methods.
Section 4 shows the experimental results. Finally, a few concluding and closing remarks are in
Section 5.
2. CLASSIFYING AND ASSESSING CONSTRAINT TYPES
The diagram in Fig. 1 illustrates the framework for classifying and assessing constraint types. It
includes the conditions of both the presence and the absence of expert knowledge. In the case of
available expert knowledge, a supervised approach is used. Training samples of each constraint
type can be used as input data for assessing the constraint logical structure. We have used and
expert system tools for both non-hierarchical rule and hierarchical rule approaches for this
purpose.
In the absence of expert knowledge, an unsupervised approach has to be used to evaluate the
number of constraint classes (or types). We have used a hybrid of self-organizing map and back
propagation neural network system for this purpose. After the classes of constraint types is
obtained from the unsupervised process or from the knowledge of an expert, then a sample data
of the constraint types can be derived for constraint structure assessment. A supervised approach
and an expert system tool are used to assess the constraint logical structure based on both the nonhierarchical and hierarchical rules. If the obtained constraint logical structure is acceptable then
the combination of the constraint types and the potential land information can be used for further
regional planning process. In the next Section 3, both the unsupervised and supervised
approaches are discussed.
Multistage, multitemporal, multisensor remote sensing and census data
GIS analysis
Constraint Data
Sample Data of Constraint Classes
Classification / Clustering
Constraint Assessment
Constraint Classes
Constraint Logical Structure
No
Acceptable?
Yes
Potential Region Map
Final Suitability Analysis and Assessment
Regional Planning Map
Fig. 1. A framework for land development constraint assessment.
3. Methods for Classifying and Assessing Constraint Types
This section discusses the unsupervised approach for clustering the land development constraint
types and the supervised approach for assessing the constraint logical structure. The
unsupervised and supervised approaches are used in the hybrid neural network system [5,6]. The
result will give the classified constraint data. From the classified constraint data, we can derive a
sample data to be evaluated based on the trial and error approach with the aid of an expert system
tool to finally find the constraint logical structure. In the trial and error process using the expert
system tool both the non-hierarchical and hierarchical rules are used. And in the presence of an
expert knowledge, the expert can provide the sample data. The experimental results of using the
hybrid neural network system and the knowledge-based system are compared to the expert
knowledge and discussed in Section 4. The following paragraphs discuss the hybrid neural
network system and the knowledge-based system.
An impact matrix approach was used to define the best set of weighting factors for the constraint
parameters in order to increase the discriminating power of the features. Furthermore, using a
hierarchical rule-based approach, the features were used to establish the constraint logic structure.
The best decision rules are shown in Fig.2 and are used to in the knowledge-based system. The
same weighted data features are used as input data for the hybrid neural network system
discussed in the next paragraph.
IF
(environment_quality is good)
THEN IF
(natural_resource_accessibility is good)
THEN constraint D
ELSE constraint E
ELSE IF
(labor_quality is good)
THEN IF
(market_orientation is good OR
Economic_growth is good)
THEN constraint B
ELSE constraint A
ELSE not_potential_region.
Fig.2. The constraint logic structure.
The hybrid architectural network of self-organized module with a supervised network is shown in
Fig 3 [6]. The self-organized module performs an iterative process to obtain an optimal number
of neurons. The number of output neurons represents the number of existing clusters. These
output neurons become input neurons for the supervised network module. The output of this
supervised network module has a definite number of output neurons, and these output neurons
represent the number of constraint types. The neural network architecture becomes a hybrid of
SOM and BP network.
V11
X1
W11
Z1
Y1
Xi
Yk
Zj
Wnm
Vnm
Xn
Ym
Zp
SOM
Neuron
Masukan
Neuron Pola atau
Neuron Klaster
Neuron Tersembunyi
BP
Neuron
Keluaran
Fig.3. The hybrid architectural network of self-organized module with a supervised network.
4. EXPERIMENTAL RESULT
There are six constraint parameters used in this experiment. They include the quality measures of
labor power, economic growth, natural resources accessibility, degree of environment
sustainability, market orientation, and investment opportunity. The labor power was measured
based on the regional population and mobility, while the economic growth was estimated based
on the gross margin and regional revenue. The natural resources accessibility was estimated
based on the availability of area for development and the natural resources that support energy,
raw material and water. The degree of environment sustainability was measured based on the
existing natural disaster factors and on the quality of environment. The market orientation was
estimated based on the availability of infrastructure such as road network while the investment
opportunity was measured based on the availability of government, private and community
investments.
The quality measures of the parameters are represented with the number between 1 to 5, where
the lowest quality has a measure of 1 and the highest quality has a measure of 5. The weighting
factors for the labor power, economic growth, natural resources accessibility, degree of
environment sustainability, market orientation, and investment opportunity features are 0.15, 0.1,
0.2, 0.3, 0.1, and 0.15 respectively. The impact matrix of a sample of data is shown in Table 1.
The number of training samples is 777. The classification results using the two methods are
compared to the expert knowledge and represented in the following Table 2 and Table 3.
Table1. The impact matrix of the weighted condition (constraint parameter)
and the corresponding action (constraint type).
Constraint
Type
Labor
Power
Economic
Growth
Resources
Environment
Accessibility
Quality
Market
Orientation
Investment
Opportunity
Total
Score
A
B
C
D
E
Table 1. Impact matrix and weighting factors of constraint parameters.
Sample
Condition
Data
Weight
Labor
Economic
0.3
0.1
Constraint
Accessibility Environment Market Invest
0.2
0.15
0.05
0.2
Types
Score
Wilayah-1
5 1.5
3
0.3
7
1.4
8
1.2
3 0.15 8 1.6
6.15 (A)
Wilayah-2
5 1.5
5
0.5
9
1.8
9
1.35
6 0.3
7 1.4
6.85 (B)
Wilayah-3
5 1.5
4
0.4
8
1.6
10
1.5
6 0.3
7 1.4
6.7 (C)
Wilayah-4
5 1.5
4
0.4
13
2.6
11
1.65
4 0.2
8 1.6
7.95 (D)
Wilayah-5
4 1.2
3
0.3
10
2.0
10
1.5
4 0.2
6 1.2
6.4
Wilayah-6
4 1.2
3
0.3
7
1.4
8
1.2
3 0.15 8 1.6
5.85 (O)
Wilayah-7
4 1.2
3
0.3
7
1.4
9
1.35
6 0.3
5.15 (O)
7 1.4
(E)
Table 2. Correct classification result of the hybrid neural network system.
Constraint type Hybrid Neural Network
Expert
Matching percentage
A
B
C
D
E
119
214
212
55
102
Average matching percentage
119
260
228
58
112
100.0%
82.3%
92.9%
94.8%
91.0%
92.2%
Table 3. Correct classification result of the knowledge-based system.
Constraint type
Knowledge-Based
Expert
Matching percentage
System
A
115
119
96.6%
B
305
260
85.2%
C
86
228
37.7%
D
56
58
96.5%
E
46
112
41.0%
Average matching percentage
71.4%
5. CONCLUSION
Both unsupervised and supervised approaches have been used for classifying and assessing land
development constraint types. Besides an expert knowledge, a hybrid SOM and BP neural
network has been used to assess the number of constraint types. A knowledge-based approach is
used to evaluate the result of constraint logic structure assessment. It can be concluded that for
this application the adaptive-learning hybrid neural network system performs the best over the
knowledge-based system with matching percentage to the expert of 92.2% and 72.8%
respectively.
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