AN EXPERT SYSTEM FOR DECISION SUPPORT OF THE
TOTAL ALLOCATION OF AGRICULTURE
NON-POINT SOURCE POLLUTION
A Case of Tiaoxi Watershed
Bo Yu
, Qingyu Zhang, Liangqian Fan, Qi Liu and Weili Tian
Institute of Environmental Engineering, Zhejiang University, 866# Yuhangtang Road, Hangzhou, China
Keywords: Expert system, Non-point source, AHP; Decision support system, Waste load allocation.
Abstract: This paper develops an expert system for waste load allocation of agricultural non-point source (ES-
WLAANS) to assist environment administrators in making decision in optimal solution of allocation. ES-
WLAANS includes a decision model at its core which is built based on analytic hierarchy process (AHP)
and a closely related database. Tiaoxi watershed is the case study area. The results showed that ES-
WLAANS can enhance the efficiency of allocation of agriculture NPS pollution for environmental
administrators.
1 INTRODUCTION
One watershed always covers several administrative
regions. Agriculture non-point source pollution of
each region has different impacts on water quality
due to different pollutants emission. Waste load
allocation of agriculture non-point source of river
basin (WLAANS) is a scientific management
method to determine the optimal pollutant abatement
for each region. Therefore, it is important to decide
how to allocate among these regions of a river basin.
There have been some forms of optimization
models to solve the Waste Load Allocation (WLA)
problem. Mostafavi and Afshar (2011) use non-
dominated archiving multi-colony ant algorithm to
develop WLA model. Saadatpour and Afshar (2007)
presented a fuzzy WLA model. Burn et al. (2001)
explored the capabilities of genetic algorithms.
Analytical Hierarchy Process (AHP) is multi-
objective decision-aiding method for pollutants
allocation (Li et al., 2005). However, the complexity
of WLAANS and the synthesis of AHP have many
problems including a mass of time, lots of human
power and financial resources. It is very difficult for
government to estimate pollutants abatement amount.
This paper developed an expert system for waste
load allocation of agricultural non-point source (ES-
WLAANS). We select COD, total nitrogen (TN),
total phosphorous (TP) as pollutants and Tiaoxi
Watershed as case study. ES-WLAANS is
developed with Visual Basic (VB), AHP, Microsoft
Access and Matrix Laboratory (MATLAB). It takes
economic factor, social factor, technical factor and
environmental factor into account to establish the
structure of AHP for decision support system.
2 METHODS
2.1 Study Area
Tiaoxi Watershed belongs to Tai Lake basin that is
located in the northwest of Zhejiang Province,
China. There are 6 regions including Changxin,
Huzhou, Anji, Deqing, Yuhang and Lin’an in Tiaoxi
valley. Ongley et al. (2010) found that the
proportion of NPS pollution to Tai lake increased
remarkably in recent years and algal bloomed in the
Tai Lake in 2008.
2.2 Framework of ES-WLAANS
ES-WLAANS consists of three basic components,
including database, inference engine (analysis
model) and interface. The basic structure of ES-
WLAANS is shown in Fig.1.
405
Yu B., Zhang Q., Fang L., Liu Q. and Tian W..
AN EXPERT SYSTEM FOR DECISION SUPPORT OF THE TOTAL ALLOCATION OF AGRICULTURE NON-POINT SOURCE POLLUTION - A Case of
Tiaoxi Watershed.
DOI: 10.5220/0003579004050408
In Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2011), pages
405-408
ISBN: 978-989-8425-78-2
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Architecture of the ES-WLAANS.
2.3 Modelling and Methodology
2.3.1 Allocation Index System and the
Hierarchic Structure
The first step is building a proper index system for
AHP. To make the allocation more acceptable for
each region, the principles of fairness and efficiency
were emphasized in building index system.
Combining the former researches and the situation
of Tiaoxi Watershed (Xiong et al., 2007), we select
some representative indices: agriculture GDP,
consumer price index, expenditure of science and
education et al for economic factor; medical staff
proportion, population density, number of personnel
in agriculture et al for social factor; TN emission per
cultivated area, COD emission per cultivated area,
TP emission per cultivated area et al for technical
factor; forest cover rate, area of paddy field, area of
irrigable land, area of arid land, area of forest land et
al for environmental factor.
There are 4 layers in the allocation system
objective layer, factor layer, index layer and decision
layer, showed in Fig.2.
2.3.2 Dimensionless Evaluation Factors
There are different units among these indices, so it is
essential to make all factors non-dimensional-
normalized for better comprehensive analysis
(Xiong et al., 2007). There are negative and positive
factors. Positive factors were advantageous to WLA
such as forest cover rate, and negative factors were
disadvantageous to WLA such as emission of
pollutants per cultivated area. Therefore, the positive
factors should be dimensionless with equation (1)
and negative factors should be dimensionless with
equation (2):
Figure 2: Frame of AHP of waste load allocation.
100'
minmax
min
×
=
ii
ii
i
xx
xx
x
(1)
1001
minmax
min
'
×
=
ii
ii
i
xx
xx
x
(2)
where
i is index,
i
x is original value,
maxi
x and
mini
x are the maximum and minimum value.
2.3.3 Indices Weights Estimation
The first layer was broken down into four layers
including economic layer, social layer,
environmental layer and technical layer to establish
the pair-wise comparison matrix. The pair-wise
comparisons are done in terms of which element
dominates the other (Xiong et al., 2007). The matrix
was expressed with A=
(
)
44×
ij
a .
=
44434241
34333231
24232221
14131211
aaaa
aaaa
aaaa
aaaa
A
(3)
We can assign the value of element in the matrix
as follows: if social factor is more important than
economic factor, the value of
ij
a can be assigned
greater than 1. At the same time, the value that is
less than 1 means less important than another and if
the value of 1 means that the two factors have equal
importance. This study selects MATLAB to
calculate the eigenvalue and eigenvector of the pair-
wise comparison matrix. Members of the
SIMULTECH 2011 - 1st International Conference on Simulation and Modeling Methodologies, Technologies and
Applications
406
eigenvector represent corresponding weights of the
indices. Weights of indices in the lower lay can also
be obtained with the same method.
2.3.4 Rate of Decision Layer Estimation
Based on the weight of each index, we can calculate
the synthetic evaluation value of each unit by the
following formulas.
4321
'
VVVVV
i
+++=
(4)
=
××=
n
k
kkjj
wuWV
1
(5)
where
i is the unit to be allocated,
j
and k are
index type in the first layer and lower layer,
'
V is the
synthetic evaluation value,
V
is the evaluation value
of the first layer,
W and w are the weight of the
index in the first layer and lower layer,
u is the
value of the index in the lower layer.
Finally, we can calculate the rate of WLA for
each unit based on the synthetic value (equation (6)) :
=
×=
n
k
k
i
ii
V
V
LPT
1
'
'
(6)
where
i is the unit to be allocated,
T
is the amount
of pollutant to be reduced,
P
is the total amount of
pollutant,
L is the total environmental capacity
added by all the units.
2.4 Database
Microsoft Office Access has two distinguishing
characteristics what are Microsoft Jet Database
Engine and Graphical User Interface which render it
suitable for developing the database.
2.5 Implementation of ES-WLAANS
ES-WLAANS is implemented by VB which is an
advanced programming language characterized by
visualization, object-oriented and event-driven (Liu
and Lu, 2010). Owing to the complexity of model
calculation for AHP, this system selects MATLAB
to support the calculation. Though MATLAB has
the advantage of data process and graph plot, it is
weak in functional interface. Fortunately, Visual
Basic can call dynamic link library compiled by
MATLAB so that the system easily integrates
advantages of AHP and MATLAB (Zhou et al.,
2004).
3 APPLICATION OF ES-WLAANS
3.1 Selecting Index for AHP
According to the present situation of Tiaoxi
Watershed, we select 18 indices in the lower layer
shown in Fig.3.
Figure 3: Frame of AHP of waste load allocation.
3.2 Data Input Interface
As showed from GUI of data input in the Fig.4,
users can input the pair-wise comparison matrix and
kinds of data such as pollutants emission, local
environmental capacity and data of each index.
Figure 4: The GUI of data input.
4 RESULTS AND DISCUSSION
Fig.5 shows the result estimated with ES-WLAANS.
From figure 5, we can see that Changxing has the
worst result which requires reduction of 9575.2 ton
of COD, 4056 ton of TN and 223.45 ton of TP
followed by Anji which needs reduction of 6005.2
ton of COD, 2406.2 ton of TN and 85 ton of TP. As
for Deqing, the required reduction of TP is 87.81 ton
just less than Changxing, but COD, 251.5 ton, and
TN, 623 ton, are the second least except Lin’an
which needn’t be reduced anymore. Besides,
AN EXPERT SYSTEM FOR DECISION SUPPORT OF THE TOTAL ALLOCATION OF AGRICULTURE
NON-POINT SOURCE POLLUTION - A Case of Tiaoxi Watershed
407
Allo catio n of Agric ulture NP S Pollution F or Each Area
0
2000
4000
6000
8000
10000
12000
Wuxing Deqing Changxing Anji Yuhang Linan
Survey Regions
Amount of allocation of COD and T
N
(ton/a)
0
50
100
150
200
250
Amount of allocation of TP (ton/a
)
COD
TN
TP
Figure 5: Result of allocation for each area.
Wuxing and Yuhang have the similar condition that
reduction of TN for Wuxing is 767.6 ton and
reduction of TN for Yuhang is 926.9 ton. COD and
TP in both Wuxing and Yuhang needn’t be reduced
like Lin’an.
5 CONCLUSIONS
ES-WLAANS is a modelling expert decision
support system that assists decision-makers in local
environmental administration selecting an optimal
solution for reduction proportion of pollutants from
agriculture NPS among survey regions. Compared
with other model or system, it has own feature
which are showed in the table.1.
Table 1: Comparison with other decision support systems.
System/
Model
Manual/
computerized
Index
number
Expert
ES-WLAANS C Flexible Not
Delphi-AHP M 18 Need
Input-output
analysis
(Ni et al., 2001)
M 21 Need
Linear
programming
(Deng et al.,
2010)
M 6 Need
The characteristic of ES-WLAANS is listed as
following:
1. It can provide user-friendly interface for
operators without special training such as mastering
MATLAB or AHP.
2. When ES-WLAANS is applied in other
watershed, users can select any index they need.
3. ES-WLAANS can give pollutants abatement
scenarios of agriculture non-point source in each
administrative region.
ACKNOWLEDGEMENTS
This work was supported by grant number
2008ZX07101-006. The special thanks are sent to
the viewers for my paper.
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Applications
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