Archives of

1. Introduction
an mathematical machines and methods
be used in environmental management?
In terms of various decision-making criteria,
environmental management requires
methods to optimize decisions and performances.
Most of these decisions are based
on the opinions and experiences of experts
and specialists, and often a wide range of results is seen
in experts’ decisions. This range is derived from different
intuitive judgments of experts [1]. Some methods
have been developed to promote integration in decision-
making, including Multi-Attribute Decision Making
(MADM) methods [2]. The usual way of making
environmental decisions is to classify information and
rank decision criteria [3]. Another method used in environmental
management is the fuzzy inference system,
which is based on the theory of fuzzy sets. Fuzzy logic
was first invented in 1960 by Dr. Lotfizadeh, a professor
of computer science at the University of California,
Berkeley [4]. According to this theory, a fuzzy set on a
source set X is a set of pairs:
A={μA (x)/x:x ϵ X, μA (x) ϵ [0,1]ϵ R}
The function μA (x) of the membership grade of the
fuzzy member, x , is called set A. The membership grade
function can accept real values between 0 and 1 [5]. In
fuzzy logic, imprecise reasoning or ordinary logic is a
particular case of approximate reasoning. Any logical
system can be converted to fuzzy logic. Knowledge is
considered a set of fuzzy or flexible constraints on variables.
The inference is considered the process of disseminating
these constraints, and all problems have a solution
that indicates the degree of desirability (possibility)
[6]. The present study aimed to achieve an ideal solution
to reduce or eliminate oil pollution from the soil. Methods
have been proposed for managing soil oil pollutants,
which fall into three categories: physical, chemical, and
biological [7]. Comprehensive guidelines have been developed
in different countries to deal with oil pollution
in the soil. For example, the management mechanism of
contaminated soils in Japan is as follows:
1. Determining the type of pollution
2. Determining and recording the exact areas of contamination
3. Health risk management
4. Sampling and measuring contaminants
5. Determine the infected areas and areas exposed to
pollution on the map
6. Describe the properties of the land
7. Describe the properties of the climate
8. Determine the area of land required for clearing
9. Including the desired method for cleaning in place
or another place.
10. Description of items with legal and commercial requirements
11. Determining research companies
12. Determining the best method based on residual pollution,
commercial index, and health risk
The mechanism of contaminated soil management in
China is also shown in Figure 1.
The main criteria for selecting the ideal method of controlling
oil pollution in soil are implementation cost, environmental
effects, effects after disposal, use of the soil
after disposal, fit to the soil type, and health effects [8].
The use of models and methods based on numbers reduces
the possibility of error and increases the validity
of the results [9]. The present study investigated the possibility
of using fuzzy logic to decide on an ideal option
for cleaning crude oil-contaminated soils in an oil pump
station. Petroleum products are dangerous and resistant
pollutants and contain compounds that accumulate biologically
in the food chain [10]. Petroleum pollutants, or
products derived from crude oil, include fats, lubricating
oils, gasoline, aromatic hydrocarbons, and halogenated
petroleum hydrocarbons [11]. These materials also contain
a group of heavy metals (Pb, Ni, Hg, Cd, Ba, As, and
Se) that have destructive impacts on the environment.
These compounds are the main factor in changing soil
fertility [12, 13]. On the other hand, with the penetration
of petroleum products into the soil, groundwater contamination
is possible. The extent of this penetration depends
on soil properties (such as porosity and moisture content)
and the nature and quantity of contaminants. After
the petroleum hydrocarbons enter the soil, they compete
with the climate for replacement in the pits [14]. Deciding
on the ideal methods for controlling and eliminating
soil pollution is a complex process that requires extensive
studies of the conditions in the area and knowledge of
common methods of pollution control [15]. To choose a
C
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technique to control or eliminate soil pollution, the following
questions must be answered [16, 17]:
- What is the purpose of isolating or controlling soil
contamination?
- Due to the biological sensitivities of the area (flora and
fauna, the potential for pollution, surface and groundwater,
land use, etc.), is it necessary to eliminate pollution?
(Need to assess the risk of pollution)
- Have the side effects of the technique been determined?
- Are the necessary costs for the practical implementation
of the process determined?
- Has the method’s feasibility been studied in non-laboratory
conditions, considering factors such as the area
and depth of contamination?
- Is there a plan for managing pollutants after removal
from the soil?
- Will the former use be possible after removing the
contaminant from the soil?
- If it is not possible to access the previous use, can a
new use be assigned to the location?
Among the research that has been done in the field of
applying mathematical methods in environmental management,
we can refer to that conducted by Ameri-Arab
et al. as the application of Multi-Layer Perceptron neural
network in locating municipal solid waste landfills with
emphasis on hydrogeomorphic properties [9] and also
the research by Gonzalez et al. entitled “Ecological management
of sea hills based on climatic data estimated in
artificial intelligence” [16]. This study was done to carefully
review the common methods of cleaning soils con-
Figure 1. Mechanism of contaminated soil management in China
Figure 1- Mechanism of contaminated soil management in China
The main criteria for selecting the ideal method of controlling oil pollution in soil are implementation cost,
environmental effects, effects after disposal, use of the soil after disposal, fit to the soil type, and health effects (8,25).
The use of models and methods based on numbers reduces the possibility of error and increases the validity of the
results (9). The present study investigated the possibility of using fuzzy logic to decide on an ideal option for cleaning
crude oil-contaminated soils in an oil pump station. Petroleum products are dangerous and resistant pollutants and
contain compounds that accumulate biologically in the food chain (10). Petroleum pollutants, or products derived
from crude oil, include fats, lubricating oils, gasoline, aromatic hydrocarbons, and halogenated petroleum
hydrocarbons (11). These materials also contain a group of heavy metals (Pb, Ni, Hg, Cd, Ba, As, and Se) that have
Comprehensive review of soil contaminants with regulations
Health risk assessment at infected sites
Preservation of soil for
agriculture
Prevent further contamination
Infected site protection plan and
soil protection plan
Provide a mechanism applicable
to control and eliminate pollution
Special plan for quick response if
needed
Execute the plan with all the
capacities for control
Optimal effectiveness?
No yes
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taminated with oil derivatives and to express the properties,
advantages, and disadvantages of each method
based on fuzzy logic to find the best method for cleaning
soils contaminated with oil derivatives, based on the criteria
set and appropriate to the climatic and geographical
conditions of southern Iran.
2. Materials and Methods
In this study, the application of a fuzzy inference system
in environmental management decision-making of
crude oil contaminated soils in the area of an oil pumping
station in southwestern Iran in 2019 was investigated.
The study’s statistical population was the soils of an
oil pumping station in southwestern Iran (Figures 2 and
3). This oil pump station provides the required feed for
some of the oil refineries in Iran.
In order to optimally identify the current situation of oil
pollution in the study area, seven samples were collected
in the area of oil reservoirs using the Peterson-Grab
method (dimensions of 10×5×15 cm). The parameter of
Total Petroleum Hydrocarbons (TPHs) and soil texture
of the region were also determined based on the soil texture
triangle. In addition to the mentioned cases, the extent
of pollution, the elapsed time of pollution, climatic,
geographical, biological properties, and the type of soil
used in the study area were also determined. Based on
studies conducted by various authorities, such as Mrozik
[17] and based on the ability to implement different
Figure 2. Position of the studied oil pump station in Iran
Figure 3. Location of the studied oil station pump on Google Map
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methods of controlling oil pollution in the soil for the
study area in southwestern Iran, methods of bio-extraction,
plant stabilization, bio-ventilation, bio-stimulation,
plant evaporation, root decomposition, plant decomposition,
land-farming, bio-sparing, biomass, chemical oxidation,
electro-kinetics, supercritical steam extraction,
stabilization, mechanical handling and burial, combustion,
heating (evaporation with temperature change)
and soil washing were considered [11, 17, 18].
Options were prioritized based on two multi-criteria decision
methods (AHP for criteria prioritization and TOPSIS
for options prioritization) and a fuzzy inference system.
The AHP method prioritized the criteria based on the
pairwise comparison method presented by Saaty [19].
After forming the decision matrix, the criteria are compared
in pairs in this method. Then, the elements of
each level are compared in pairs with their element at a
higher level, and their weight is calculated. By combining
relative weights, the final weight of each option was
determined. All comparisons in the Analytic Hierarchy
Process were performed in pairs. In this comparison,
decision-makers will use oral judgments.
The 6-step method proposed by Lai et al. [20] was used
to prioritize the options in the TOPSIS method. These
steps include: (a) forming the decision table; (b) normalizing
the decision matrix through Euclidean norm; (c)
creating a weightless scale; (d) calculating the positive
ideal and the negative ideal; (e) calculating the distance or
proximity to the positive ideal or the negative ideal, and
(f) the Cli calculation, which indicates the proximity to
the positive ideal and the distance from the negative ideal.
Data fuzzification
The main necessity in designing a fuzzy expert system
is to select high-performance membership functions for
linguistic variables and to define fuzzy input sets. In
fuzzification, the input indices to the fuzzy variable are
done by specifying the index classes in each class. The
resulting function is called the Input Membership Function.
Thus, by defining the membership functions, the
membership grade of each point in the set can be determined.
The membership grade of each point is a mapping
of it in the fuzzy set, in the specified range (between zero
and one), and based on the shape of the defined membership
function. Thus, the membership grade ambiguity of
a value is expressed quantitatively (mathematically). Dif-
Figure 4 Fuzzy inference system [5]
Input Output
Figure 3- Fuzzy inference system (5)
Mamdani fuzzy inference system is preferred to other existing simulators due to its general acceptability and ease of
use (3). Fuzzy set output membership functions in Mamdani fuzzy inference must be de-fuzzy. This method increases
the efficiency of the de-fuzzy process by reducing the required calculations. Minimum-Maximum, Maximum-
Minimum-, Minimum-Minimum, and Maximum-Maximum are the most important methods used in Mamdani fuzzy
simulation, and the maximum-minimum combination is commonly used (5). A combination of maximum-minimum
operators has been used in the Mamdani fuzzy simulation used in this research. Figure 4 shows Mamdani fuzzy
inference system simulation.
Figure 4- Mamdani fuzzy inference system simulation (5)
This image compacts all operations from fuzzification to defuzzification. The flow of information starts from the left,
and after processing each rule, the outputs on the right are combined, and the final output is generated. Fuzzy inference
system decision-making is based on the rules applied by experts. The rules consist of two parts (if-then) and in (if),
the input is defined. The triangular function was also used to determine the membership function. Fuzzy rules were
compiled in the system rules database in MATLAB software. Finally, the fuzzy inference system was implemented
by presenting the fuzzy connection model created between the variables.
Results
Fuzzy rules
Defuzzification
Fuzzy inference Fuzzification
Figure 5. Mamdani fuzzy inference system simulation [5]
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ferent types of membership functions have been used.
These include triangular, trapezoidal, Gaussian, and sigmoid
functions. In this research, a triangular function
was selected according to the type of fuzzification. After
determining the input and output indices, the fuzzy rules
must be explained. These rules are explained based on
the results. Finally, defuzzification is done by displaying
the fuzzy connection between the variables. In this study,
Mamdani fuzzy inference system was used. It should be
noted that all steps of the fuzzy inference system were
performed using the MATLAB software version R2018a.
The implementation process of the Mamdani fuzzy inference
system is shown in Figure 4.
Mamdani fuzzy inference system is preferred to other
existing simulators due to its general acceptability and
ease of use [3]. Fuzzy set output membership functions in
Mamdani fuzzy inference must be de-fuzzy. This method
increases the efficiency of the de-fuzzy process by reducing
the required calculations. Minimum-Maximum,
Maximum-Minimum-, Minimum-Minimum, and Maximum-
Maximum are the most important methods used
in Mamdani fuzzy simulation, and the maximum-minimum
combination is commonly used [5]. A combination
of maximum-minimum operators has been used in the
Mamdani fuzzy simulation used in this research. Figure
5 shows Mamdani fuzzy inference system simulation.
This image compacts all operations from fuzzification
to defuzzification. The flow of information starts
from the left, and after processing each rule, the outputs
on the right are combined, and the final output is generated.
Fuzzy inference system decision-making is based
on the rules applied by experts. The rules consist of two
parts (if-then) and in (if), the input is defined. The triangular
function was also used to determine the member-
Table 1. Soil properties of oil pump station area (Source: present research)
Sample Parameter Soil 1 Soil 2 Soil 3 Soil 4 Soil 5 Soil 6 Soil 7
pH 7.04 7.35 7.23 7. 34 7.35 7.50 6.85
TDS 3.75 2.41 1.78 1.73 1.41 1.41 0.9586
Porosity 48.81 51.09 50.36 48.89 55.31 53.25 65.55
EC (μs /s / cm) 95.93 75.09 68.04 63.54 44.33 44.89 69.76
OC (%) 8.75 5.752 2.35 2.31 1.378 2.78 1.284
TPH (ppm) 1001 656 689 705 603 530 412
HPC (microbial population) 1 x 102 0.9 x 102 0.0/7 x 102 0/7 x 102 0.6 x 102 0.4 x 102 0.4 x 102
Apparent weight (g/cm3) 1.40 1.49 1.38 1.32 1.31 1.40 1.38
Actual weight (g/cm3) 2.63 2.64 2.74 2.70 2.74 2.74 2.73
Table 2. Results of prioritizing the main criteria of methods to deal with oil pollution in soil
Option Name Weight
Achieve prior use for soil 0.273573
Execution cost 0.172331
Environmental effects 0.157001
The fit of the method with the soil type 0.141608
Effects on the environment after disposal 0.139351
Health effects on humans 0.116135
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ship function. Fuzzy rules were compiled in the system
rules database in MATLAB software. Finally, the fuzzy
inference system was implemented by presenting the
fuzzy connection model created between the variables.
3. Results
Since the methods of dealing with oil pollution in the soil
depend to some extent on soil properties, the physicochemical
and biological parameters of the soil in the area of this
oil pump station were measured, the results of which are
Figure 6. Creating initial input and output indices in the MATLAB software
Figure 7. Defined membership functions and fuzzy mapping
Figure 8. Building a database of rules in MATLAB software for the present study
Shafie et al. Fuzzy Inference System in Comparing it with Multi-criteria Decision-Making Techniques. Arch Hyg Sci. 2022; 11(1):31-44.
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Table 3. Decision matrix for prioritizing options in TOPSIS method
Health Effects
on Humans
Effects on the
Environment
After Disposal
The Fit of the
Method With
Soil Type
Environmental
Effects
Cost of Implementation
Achieve Prior
Matrix Use for Soil
Land-farming 6 5 7 7 7 8
Biological extraction 7 7 9 8 8 9
Bio-ventilation 6 8 8 7 8 8
Plant stabilization 7 7 8 8 8 8
Root decomposition 5 8 8 8 8 8
Plant decomposition 6 5 8 8 8 8
Plant evaporation 7 7 7 8 7 8
Bio-stimulation 7 7 8 8 8 7
Bio-sparing 6 3 7 8 7 6
Biological masses 2 5 6 7 6 4
Soil washing 4 2 2 4 2 2
Chemical oxidation 6 3 3 4 3 2
stabilization 1 4 3 7 3 7
Combustion 1 6 1 5 1 1
2 1 4 5 4 4
Heating (evaporation
with temperature
change)
Supercritical steam 3 4 4 5 4 4
extraction
Electro-kinetics 5 2 3 6 3 5
Mechanical handling 1 2 4 9 4 6
and burial
Criterion type Positive Positive Positive Positive Positive Positive
Standard weight 0.273573 0.172331 0.157001 0.141608 0.139351 0.116135
Figure 9. The fuzzy connection created between input and output indices
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shown in Table 1. Pollution to total petroleum hydrocarbons
was at a significant level. The status of other parameters
in the soil of the study area was also determined.
In order to prioritize the identified options to determine
the ideal method for dealing with oil pollution in the soil,
multi-criteria decision-making techniques were used.
Its purpose is to compare the decision-making results of
the fuzzy inference system with the prioritization done
by ten experts (five faculty members in the Department
of Environment and five environmental experts in the
National Iranian Oil Company). For this purpose, the
main criteria were prioritized by the AHP multi-criteria
decision-making technique in the first step. Accordingly,
achieving prior use for soil weighing 0.273573 was determined
as the most important criterion for prioritizing
and determining the ideal method to deal with oil
pollution. Execution cost with a weight of 0.172331,
environmental effects with a weight of 0.157001, fit to
the soil type with a weight of 0.141608, effects on the
environment after disposal with a weight of 0.139351,
and health effects on humans with a weight of 0.116135
were prioritized as other criteria. Also, the biological
extraction method with a weight of 0.079977 has been
determined as the most desirable method to deal with
oil pollution in the soil in the area of this oil center. The
results of prioritizing the main criteria of methods to deal
with oil pollution in the soil based on the AHP method
are shown in Table 2. Then the multi-criteria decisionmaking
technique (Topsis) was used to prioritize the options.
The decision matrix for prioritizing options is presented
in Table 3. Also, the calculation of proximity to
the positive and negative ideal solution and the ranking
of options in the TOPSIS method is presented in Table 4.
Based on the multi-criteria decision-making technique
results, bio-extraction with a coefficient of the proximity
of 0.9335 was determined as the best method to deal
Table 4. Calculating the proximity to the positive and negative ideal solution and the ranking of options
Result Proximity Coefficient
Biological extraction 0.9335
Plant stabilization 0.901
Bio-ventilation 0.887
Bio-stimulation 0.8724
Plant evaporation 0.854
Root decomposition 0.8233
Plant decomposition 0.779
Land-farming 0.7488
Bio-sparing 0.6352
Biological masses 0.4697
Chemical oxidation 0.4219
Electro-kinetics 0.3993
Supercritical steam extraction 0.3841
Stabilization 0.3492
Mechanical handling and burial 0.3159
Combustion 0.277
Heating (evaporation with temperature change) 0.2727
Soil washing 0.2646
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Table 5. Fuzzy indices, classes, and their fuzzy equivalents
Input Indices Class Fuzzy Equivalent Data Mapping
Cost of implementation
Less than $ 10 (square meters) Very little 20 - 0
$ 10 - $ 30 Low 35 - 15
$ 30 - $ 60 Medium 50 - 30
$ 60 - $ 100 Much 65 - 45
More than $ 100 Very much 100 - 60
Environmental effects
- Low 0.4 - 0
- Medium 0.9 - 0.1
- Much 1- 0.6
Effects on the environment after
disposal
- Low 0.4 - 0
- Medium 0.9 - 0.1
- Much 1 - 0 . 6
Achieve prior use for soil
- Suitable 0.4 - 0
- Acceptable 0.9 - 0.1
- Inappropriate 1 - 0.6
The fit of the method with the
soil type
- Fit 0.4 - 0
- Low fit 0.9 - 0.1
- Non-fit 1 - 0.6
Health effects on humans
- Low 0.4 - 0
- Medium 0.9 - 0.1
- Much 1 - 0.6
The desirability of the method
- Completely N0n-Optimal 0.15 - 0
- N0n-Optimal 0.3 - 0.1
- Very low Optimal 0.4- 0.2
- Moderate Optimal 0.55 - 0.35
- Relatively Optimal 0.65 - 0.45
- Optimal 0.8 - 0.6
- Absolutely Optimal 0.1 - 0.75
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with oil pollution in the soil of this oil pump station. Other
biological methods, such as plant stabilization, bioventilation,
and bio-stimulation were selected as ideal
to deal with soil oil pollution by considering proximity
coefficients 0.901.
Prioritize options using a fuzzy inference system
The first step in a fuzzy inference system is to define
the input and output indices and their fuzzy equivalents.
Fuzzy indices, classes, and their equivalents are shown
in Table 5 and Figures 6 and 7. Index classes are determined
based on the intuitive method.
Inference
The most important part of a fuzzy inference system is
building the rule base. The purpose of writing these rules
is to define different and varied statements obtained by
combining different probabilities defined for each index
(inputs and outputs). Conditional sentences define these
statements (if ..... then ....). The criterion for decisionmaking
is based on if-then, here, “if” is called first, and
“then” is called result.
The number of rules required for a fuzzy inference system
depends on the number of classes in each index and
is calculated by the following equation:
I = K1 ×… × K2×Kn
In this equation, I indicates the number of rules, n is
the number of indexes, and k is the number of classes of
each index. Given that six input indices and four classes
are defined in each index, 4096 fuzzy rules were designed
using MATLAB software (Figure 8).
Defuzzification
Table 6. Comparison of fuzzy inference system results with multi-criteria decision-making methods
Membership Grade
Method Topsis AHP Membership in Fuzzy Set (Non-Fuzzy Map)
Land-farming 0.7488 0.055385 Completely desirable 0.71
Biological extraction 0.9335 0.079977 Completely desirable 0.63
Bio-ventilation 0.887 0.070111 Completely desirable 0.62
Plant stabilization 0.901 0.074451 Completely desirable 0.25
Root decomposition 0.8233 0.063032 Absolutely desirable 0.08
Plant decomposition 0.779 0.066342 Desirable 0.8
Plant evaporation 0.854 0.068363 Desirable 0.3
Bio-stimulation 0.8724 0.070629 Relatively desirable 1
Bio-sparing 0.6352 0.05466 Relatively desirable 0.74
Biological masses 0.4697 0.052088 Relatively desirable 0.36
Soil washing 0.2646 0.039912 Moderate desirability 0.85
Chemical oxidation 0.4219 0.04368 Relatively low desirability 0.32
Stabilization 0.3492 0.042779 Relatively low desirability 0.45
Combustion 0.277 0.045516 Relatively low desirability 0.6
Heating (evaporation with tem- 0.2727 0.042824 Undesirable 0.17
perature change)
Supercritical steam extraction 0.3841 0.042683 Undesirable 0.62
Electro-kinetics 0.3993 0.044492 Completely undesirable 0.46
Mechanical handling and burial 0.3159 0.043075 Completely undesirable 0.69
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The final result of the inference process is a fuzzy output.
It is necessary to return the output from the fuzzy
state to a crisp value. This part of the inference process,
known as defuzzification, is a unit that acts as a function
of a fuzzy set to a crisp value. Many different methods
have been developed for defuzzification of the inference
process output, such as the center of gravity, the center of
sets, height, the center of the largest surface, and maximum
mean, which used the center of gravity in this study
method. The fuzzy connection created between the input
and output indices is shown in Figure 9.
The results of the prioritization of options in the fuzzy
inference system have differences with multi-criteria decision-
making techniques, which is probably due to differences
in the definition of fuzzy rules. In this method, landfarming
with a membership grade of 0.071 to a completely
desirable set has been determined as the best method to
deal with oil pollution in the soil. The fuzzy inference engine
acts as a calculator and, based on fuzzy rules, determines
the membership of the options to each set (Table 6).
4. Discussion
Decision-making in environmental management based
on the collected information is one of the problems of
environmental engineers [21, 22]. Various methods have
been developed to quantify information and facilitate
decision-making. However, it is difficult for machines to
make decisions for reasons, such as multiple criteria, such
as economic costs, social outcomes, efficiency, time, etc.,
to reach the stage where machines (mathematical methods)
can be the optimal output and management decision
in the field of environment. Further research should be
done applying mathematical methods, such as fuzzy logic
in environmental management. Currently, the main problem
in applying mathematical methods to environmental
management decisions is that most of the criteria cannot
be easily condensed and quantified in a single unit [21].
For example, in the cost criterion of implementing each
method of cleaning oil pollution in the soil, the calculation
of the results of previous research has been the basis.
However, based on economic and even political factors,
the cost of implementation in different countries or even
different regions can be different. On the other hand, accurate
calculation of implementation costs for each method
requires separate research. In other criteria, determining
the level of desirability is based only on expert opinions,
and it is often not possible to rank based on validated data.
Ma et al. [21] also mentioned the impossibility of creating
data density in environmental decision-making as one
of the most important drawbacks of these methods in environmental
management. Jamshidi et al. [22] proposed
using a combination of standardized data and environmental
measurements to construct fuzzy rules for environmental
decisions, such as waste disposal. The results
of using fuzzy inference systems in decision-making in
this research and its overlap with other methods used in
research (AHP and TOPSIS) in prioritizing options for
management decisions showed that fuzzy inference engine
could be used as an intelligent decision system used.
Balioti et al. [23] described the combination of multicriteria
decision-making techniques with fuzzy logic as
a more effective way to achieve optimal environmental
decision-making. The interplay of goals between environmental
ethics, economics, and other criteria has made
applying these methods difficult. Therefore, this research
proposes to create a database to facilitate the possibility
of quantifying data [24-27].
5. Conclusion
The environment is one of the areas that has not yet benefited
much from mathematical methods in decision-making.
It is suggested to use fuzzy logic in decision-making
for other environmental areas and, if the desired results are
obtained, create comprehensive systems in national and
international organizations to achieve the ideal decision
in changing conditions. This measure can significantly reduce
the rate of human error in environmental decisions.
Ethical Considerations
Compliance with ethical guidelines
There were no ethical considerations to be considered
in this research.
Funding
This research did not receive any grant from funding
agencies in the public, commercial, or non-profit sectors.
Authors' contributions
All authors equally contributed to preparing this article.
Conflict of interest
The authors declared no conflict of interest.
Acknowledgments
This article is extracted from the doctoral dissertation in
environmental sciences at the Islamic Azad University of
Ahvaz with the code 1064822972878280000162421137.
We express our sincere gratitude to the officials of the
Faculty of Agriculture and Natural Resources who helped
us carry out and improve the quality of this research.
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