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Consultancy Report:Expansion of Cowan Field
StationGEOS 9016
Kerwin Ferrer (z3444817)
Jayson Bausa (z3429936)
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Executive summary
This Consultancy report was done for the University of New South Wales to help in the
identification of a suitable site for the expansion of the Cowan Field Station. GIS analysis
was used to identify a suitable site that has the following considerations: minimal risk of
pollution (erosion), minimal risk from fire hazard, minimal effect on conservation and
minimal building cost. Four models were generated for each of the considerations. The
erosion model considered how much erosion is expected to happen in each area per annum.
The fire model considered the vegetation cover and how much fire intensity is expected for
each area. The conservation model considers the surrounding creeks, mangroves and
threatened flora and fauna. Lastly, the building model analysed the suitability of areas
depending on their distance from the roads and power supplies. An analysis was done by
combining the output of the different models. After the analysis, it was found out that Site 1
(Figure 1), situated at the eastern side of the fire trails is the best site in terms of area at 14800sq m, it also has a small risk of erosion, moderate fire and solar values and having the best
view. The distance from the main road and power supply lines is also around 2 km. Site
inspection is suggested to further explore the suitability of Site 1. The other sites, Site 2 and 3
can also be inspected to compare with Site 1.
Figure 1: Proposed Sites for the expansion of the Cowan Field Station
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Table of Contents
Executive summary ..................................................................................................................... i
1. Introduction ............................................................................................................................ 1
1.1 Aim .................................................................................................................................. 1
1.2 Location ........................................................................................................................... 1
2. Key data sets .......................................................................................................................... 2
2. 1 The Digital Elevation model (ANUDEM) ...................................................................... 2
2. 2 Accuracy of the firetrails ................................................................................................ 2
3. Analysis.................................................................................................................................. 4
3.1 Erosion model .................................................................................................................. 4
3.2 Fire model ........................................................................................................................ 7
3.3 Conservation model ......................................................................................................... 9
3.4 Building model............................................................................................................... 13
3.5 Combined model ............................................................................................................ 17
3.6 Ranking of Sites ............................................................................................................. 20
4. Recommendations ................................................................................................................ 22
References ................................................................................................................................ 23
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List of Figures
Figure 1: Proposed Sites for the expansion of the Cowan Field Station .................................... i
Figure 2: Location Map of Cowan where the new field station will be built. ........................... 2
Figure 3: Erosion factors that were used in the generation of the Erosion model ..................... 5
Figure 4: Soil Erosion model for Cowan Area .......................................................................... 6
Figure 5: Fire model for Cowan Area ........................................................................................ 8
Figure 6: Fuzzy logic used in the conservation model .............................................................. 9
Figure 7: High Conservation model ......................................................................................... 11
Figure 8: Low Conservation model ......................................................................................... 12
Figure 9: Fuzzy logic used in the building model for each factor. .......................................... 14
Figure 10: High Cost Building Model generated by combining the four factors .................... 15
Figure 11: Low Cost building model generated by combining the four factors ...................... 16
Figure 12: Result of combining the four models ..................................................................... 18
Figure 13: Result of combining the models and limiting results ............................................. 19Figure 14: Viewshed analysis for each site. Higher visible area is better ............................... 21
Figure 15: Proposed site (Site 1) for the expansion of the Cowan Field Station ..................... 21
List of Tables
Table 1: Core data sets that were used in the analyses .............................................................. 3
Table 2: Fuzzy membership limits for the Low Conservation model ..................................... 10Table 3: Fuzzy membership limits for the High Conservation model ..................................... 10
Table 4: Fuzzy membership values for Building model .......................................................... 14
Table 5: Ranking of the three suitable sites ............................................................................. 20
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1. Introduction
1.1 Aim
The aim of this report is the identification of a suitable site for the expansion of the Cowan
Field Station of the University of New South Wales. The University of New South Wales
paid the Meta GIS Inc. to do a consultancy report on the selection of a new site for the
expansion which shall primarily be used for accommodation. Different aspects such as fire,
erosion, conservation and building shall be taken into consideration in the selection process.
The erosion model will identify sites that are not prone to erosion thereby decreasing the
probability of a release of pollutants from septic tanks that will also be part of the building
project. The fire model will assess the risk of bush fires in the area by taking into
consideration the vegetation cover. By this, we will be able to identify a location where fire
hazard is at a minimum. The conservation model shall take into consideration the various
species of flora and fauna in the area and also the surrounding creek and the mangrove areas,so that each of these species/sites will not be affected by the construction/expansion. The
building model will take into consideration the cost of the project by considering sites that are
near the road/fire trails and also near the power supply lines. If possible, the site should also
have a nice view and at the same time a high amount of solar radiation. The minimum area
for the expansion site is 5000 m2.
Geographic Information Systems was used in the analysis of the sites. Four models were
created namely erosion model, fire model, conservation model and building model. The
software ARCGIS v 10.1 by ESRI was used in the analyses.
1.2 Location
The town of Cowan lies approximately 40 kilometres north of Sydney, New South Wales in
Australia (Figure 2). It is bounded by Berowra Creek, Muogamarra Reserve, Pacific Highway
and towns of Berowra Heights. The Cowan Field Station is a reserve site owned by the
University of New South Wales which is usually used for scientific research.
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Figure 2: Location Map of Cowan where the new field station will be built.
2. Key data sets
Several data sets were used in this project. Before using the data sets, it was made sure that
they were projected to GDA 1994 MGA Zone 56 (Table 1).
2. 1 The Digital Elevation model (ANUDEM)
The ANUDEM is one of the main rasters that was used in this analysis. The DEM was used
in calculating the slope values necessary in all the four models. It has a cell size of 10 m and
was derived from the contour, creeks, spot heights and rivers key data sets by using the
ANUDEM algorithm.
2. 2 Accuracy of the firetrails
The data for the firetrails was derived from the Cowan2013waypoints which was surveyed on
24 March 2013 by the GEOS students of UNSW. The Cowan2013waypoints file was
processed by removing soil and fuel load survey data and eliminating points that are
suspicious of having a high error. To be able to use the Cowan2013waypoints in the analysis
of the models a polyline (named firetrails) was traced over the points that would somehow
show an estimate of the firetrails. The firetrails file was then compared to the
fieldstn_firetrails, which contains GPS data collected from previous years. The nearness ofthe firetrails points to the lines in the fieldstn_firetrails was calculated. The root mean square
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error is then calculated by getting the mean of the squares of the near distance values for each
firetrail point and then taking the square root of the answer. The RMSE was calculated as 38
m.
Table 1: Core data sets that were used in the analyses
Data Data type Geometry
Threatened Flora Shape file feature class Point
Threatened Fauna Shape file feature class Point
Mangroves Shape file feature class Polygon
Creeks Coverage feature class Arc (Feature)
River Shape file feature class Polygon
Fire trails Shape file feature class LineContours Shape file feature class Line
Spot heights Shape file feature class Point
Cowan2013waypoints Shape file feature class Point
Infrastructure Shape file feature class Line
Vegetation Shape file feature class Polygon
k_pred_2013 Raster
d_infinity Raster
F_surf_2013 Raster
F_Bark_2013 Raster
F_elev_2013 Raster
NDVI Raster
Study area Shape file feature class Polygon
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3. Analysis
Four models were created to be used in the selection of a suitable site for the Cowan field
station expansion namely erosion, fire, conservation and building. Fuzzy logic was employed
on the models to be able to account for other possible locations that may not necessarily fit
our selection criteria but in a certain degree may be allowed. Fuzzy logic is a type of logicthat does not depend on simple true and false, it recognizes other values not normally
considered in models. (Tarunamulia 2008, p. 23). With the use of fuzzy logic we can give
partial memberships to points depending on their similarity to a certain criteria
(Hatzinikolaou et al. 2003). Membership values range from 0 to 1, where 1 refers to full
membership. The use of fuzzy logic in our analyses will help us in giving intermediate values
to sites that may not fit our ideal criteria but maybe close to it.
3.1 Erosion model
An erosion model was created to identify the erodability of the different areas in Cowan. We
will use this model to find suitable places where erosion is minimal, thereby reducing the
possibility of releasing pollutants in the waterways. The formula used for the erosion model
was derived from the Universal Soil Loss equation (USLE) by Wischmeier and Smith (Selby
1993).The soil erosion can be computed by getting the product of the different soil factorssuch as Rainfall erosivity factor (R), Erodibility factor (K), Slope gradient factor (S), slope
length factor (L) and cropping (C) and practise factor (P) (Figure 3).
The formula in calculating the soil erosion model is as follows
=
A Rainfall erosivity factor (R) of 3500 was used in the generation of the erosion model. This
value was taken as the average of the 3000 to 4000 rain factors near Cowan that can be seen
from the Rainfall erosivity map of New South Wales (Figure 2, Rosewell 1993).
The Soil erodibility factor (K) was provided by UNSW which was estimated from the data in
Rosewells (1993) Table 2. The best method of calculating the K factor according to
Rosewell (1993) is through laboratory testing, however, this is costly and would be time
consuming.
The slope gradient factor (S) was an improved version of the USLE formula derived byMoore & Burch (1986) after observing inconsistencies produced by the USLE. It is
calculated by using the formula:
= sin 180 . 0896
1.35
The slope length factor (L) is computed by using the formula below, where the flow
accumulation was derived from the d-infinity algorithm ofTarboton (1997)
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= 22.13
0.4
Figure 3: Erosion factors that were used in the generation of the Erosion model
The cropping factor (C) takes into consideration the effect of vegetation on the resistance of
the soil to erosion by looking at the current land cover, the history on how the soil was usedand the physical properties of the soil (Rosewell 1993). The value for this is usually
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determined by long term data collection, however, we can estimate this by using Table D3
and D4 of Rosewell (1993).
The practise factor (P) considers the effect of soil erosion management practises in the area.
A value of 1 was used which accounts for land areas where the cultivation practise is to plant
crops along the slope.
After calculating all the factors, the product of all of these will then give us our soil erosion
model (Figure 4).
Figure 4: Soil Erosion model for Cowan Area
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3.2 Fire model
The fire model was created to be able to identify the risk of fire hazard in the vicinity of
Cowan. From this we will choose a location/ site where risk of fire is minimal. The fire
assessment model is an approximation of reality in identifying the risks associated with fires
based on the equations derived by Noble et al. (1980) from the McArthur forest fire danger inthe Mark 5 metre. The fire model was produced by taking into consideration the fuel load and
rate of fire spread with respect to slope gradient. Bessie & Johnson (1995) notes that weather
conditions together with fuel load are the main factors that drive fire behaviour. The effect of
weather condition is accounted for in formula for the rate of fire spread. The dominant
vegetation type for the study area is forests and grasslands. The rate of fire spread on forests
can be calculated by using the forest fire danger index derived by Noble et al (1980):
= 1.25 30 + 0.0234 In the formula, F is the fire danger index; D is the drought factor (10); T is the air temperature(37.2C); H is the relative humidity (15%) and V is the mean wind velocity at a height of 10 m
(40 km/hr). Sirakoff (1985) showed that the value for drought factor should not exceed 10
because it usually results in an overestimation of the rate of spread. After getting the value of F,
we can now compute for the Rate of forward spread on level ground (R) which has a formula
(Noble et al 1980): = 0.0012 The value of R for forest after using this formula is 0.0802W, where W is the fuel weight or
also known as fuel load.
For the grassland, the formula for F (Noble et al) is:
= 3.35 .089711.04+0.0403 40This can be simplified as = 6.23Since = 0.13 , we can get the value of rate of forward spread for grassland as 0.801W.The value of R calculated above is only applicable to flat surfaces, therefore we will modify the
value by using slope data from the Digital Elevation Model using the formula: = exp (.069 )After getting the R
slopefor both forest and grassland, the fuel weight or fuel load was
calculated by getting the sum of the surface fuel component (f_surf_2013), bark component
(f_bark_2013) and elevated fuel component (f_elev_2013 layers). Fuel Load, is the quantity
of flammable material in a particular area describe by fire management authorities
(Australian Emergency Management 2011). These components were the result of processing
the survey data gathered by students of UNSW last 24 March 2013 and correlated to
topographic and satellite data. The data from the survey done by the students and those from
topographic and satellite were all based from the Overall Fuel Hazard guide by McCarthy et
al. (1999). If we were to make a more accurate fire model, we must do a comprehensive fuel
load survey on the whole area and employ experts in that field.
After getting the fuel load, we can then use the final formula to get the fire intensity model:
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= 18600 Rslope 10003600
110
The constant 18600 is the energy that a eucalypt fire will burn (joules), this value can still be
altered by using different values for each vegetation cover, but in this analysis, we will use this
default value. The last two constants converts the final answer to m/s and kg/m 2 from km/hr
and t/ha. After using this formula, we will be able to generate our fire model (Figure 5).It must be noted that the fire model that we generated is based from empirical or statistical data
and does not take into consideration other physical components that contribute to fire behaviour
(Perry 1998). This means that the fire equations used here may not be applicable to other
places.
Figure 5: Fire model for Cowan Area
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3.3 Conservation model
The conservation model was created to identify sites that should not be built on because of
the presence of endangered animals and plants, creeks and mangroves. Two conservation
models were created namely High Conservation and Low Conservation. The High
Conservation model sets aside a large area for conservation use while the Low model has asmaller area for conservation. The conservation data were taken from four factors, namely;
endangered flora, endangered fauna, creeks and mangroves. Fuzzy logic was employed in
determining the areas that need to be conserved so that partial membership can be given to
places that are not too far from the purely conserved areas. All areas to be used strictly for
conservation are given a value of 1. A value of 0 is given to areas where conservation shall
not be enforced. Monotonic Linear fuzzy logic was used such that a distance of 0 to Max (m)
is given a value of 1-for strict conservation, and distances from Max (m) to Min (m) are given
partial memberships ranging from 1 to 0 (Table 2 and 3). The highest membership values are
given to those that are nearest to the Max (m) distance and it decreases as the Min (m)
distance is approached. Values for sites that are higher than the Min (m) distance are given a
value of 0. The max and min values for the threatened flora, fauna and mangroves were taken
as identical while the creeks had lower values.
Figure 6: Fuzzy logic used in the conservation model
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Table 2: Fuzzy membership limits for the Low Conservation model
Preserved Features Min (m) Max (m)
Threatened Flora 300 100
Threatened Fauna 300 100Mangroves 300 100
Creeks 150 50
Table 3: Fuzzy membership limits for the High Conservation model
Preserved Features Min (m) Max (m)
Threatened Flora 400 200
Threatened Fauna 400 200
Mangroves 400 200
Creeks 200 100
After applying the fuzzy membership to each of the four factors, four raster layers were
produced. The conservation model can then be generated by combining the four factors by
overlaying them and getting the maximum value for each cell. This is done twice to produce
the High Conservation model (Figure 7) and the Low conservation model (Figure 8). TheHigh Conservation Model accounts for higher conservation areas which results from a bigger
radius of conservation given to the four factors. These conservation models show us sites
where construction is not allowed. It should be noted that the Berowra creek was not included
in the conservation model analysis particularly because it is already far away from the fire
trails.
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Figure 7: High Conservation model
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Figure 8: Low Conservation model
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3.4 Building model
The building model was created to identify possible sites for the expansion taking into
consideration its nearness to the road/fire trails, the slope/steepness and the nearness to power
supply and high voltage power lines. Fuzzy logic was used in taking into consideration the
above factors. Ideal cases were given a value of 1, which means it is suitable for building.Unacceptable cases were given a value of 0, which means it is not suitable for building. Two
building models were considered, namely High Cost and Low Cost building models, with the
first having higher limits in terms of distance from the road and power supply and the other
has lower distance values (Table 4). The Low Cost model also minimises the effect of the
construction to the surrounding flora and fauna by minimising road development.
The first criteria for the building model is that it is close enough to the road to be able to
lessen costs in terms of transporting materials, limit the effect on the environment (i.e. cutting
of trees) and minimise the costs for the development and construction of roads. The roads
data was derived from the firetrails data set. The road factors were calculated by usingtrapezoidal fuzzy logic on the roads data (Figure 9a & 9b) where distances near the roads (0
to 30 m for High Cost and 0 to 20 m for Low Cost) were excluded so that the possibility of
building too close to the road or on the road itself is minimised. Ideal distances from the road
were 30 to 300 m for the high cost and 20 to 150 m for the low cost and these were given a
value of 1. Distances that were not too close to the road (HC: 20 to 30m, LC: 15 to 20 m) and
not too far (HC: 300 to 400 m, LC: 150 to 200 m) were given partial membership or
conditional values depending on their nearness to ideal distances.
The slope was considered to identify sites that are relatively flat in order to facilitate in the
construction and development of the area by minimising the need to cut and fill and eliminate
sites that are on cliffs and ridges. The slope data was derived from the ANUDEM raster by
calculating the rate of change from one cell to its surrounding cells. Monotonic fuzzy logic
was then used on the slope data (Figure 9c). Slope values of 0 to 10 were considered as
preferable sites and partial membership were given on slopes from 10 to 15.
The high voltage powerlines data was extracted from the infrastructure shapefile. Monotonic
fuzzy logic was used considering the distance from high voltage powerlines (Figure 9d). Sites
that are near the powerlines (less than 200 m) were given a value of 0, for not suitable as
Kroll et al. (2010) identified that magnetic fields emitted by powerlines were associated withchildhood leukemia. The ideal distance from the powerline is 300 m and above, while partial
membership was given for 200 m to 300 m distances (Table 4).
The power supply line data was estimated by extracting the Glendale road feature class from
the infrastructure shapefile. A monotonic fuzzy logic was used on the power supply data. For
High Cost power supply factor the ideal distance is from 0 m to 2500 m, while the Low cost
power supply has an ideal distance of 0 m to 1500 m (Figure 9e & 9f). Partial membership
were given to distances 2500 m to 3000 m for High Cost while the Low Cost has 1500 m to
2000 m range.
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Table 4: Fuzzy membership values for Building model
Factors
High Cost Low Cost
Ideal values Conditional
values
Ideal values Conditional
values
Roads (firetrails) 30 m to 300 m 20 to 30 m 20 m to 150 m 15 to 20 m300 m to 400 m 150 m to 200 m
Slope (in degrees) 0 to 10 10 to 15 0 to 10 10 to 15
High volatage
powerlines
> 300 m 300 to 250 m
from powerline
> 300 m 300 to 250 m
from powerline
Power supply
lines
0 m to 2500 m 2500 to 3000 m 0 to 1000 m 1000 to 1500 m
Figure 9: Fuzzy logic used in the building model for each factor.
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After getting the four factors, the building models can then be generated by combining the
relevant factors. The High Cost factors for power supply and roads will be combined with the
slope factor and powerline factor by getting the minimum value for each cell to generate the
High Cost building model (Figure 10). The same will be done to produce the Low Cost
building model but this time the Low Cost factors for road and power supply will be used
(Figure 11).
Figure 10: High Cost Building Model generated by combining the four factors
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Figure 11: Low Cost building model generated by combining the four factors
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3.5 Combined model
Combining the different models will give us the suitable sites that fit our selection criteria. In
this combination, we are going to use the fire model, erosion model, the Low Cost building
model and the High conservation model. In can be noted that the High Cost Building model
and the Low Cost building model were generated as backup in case their counterparts werenot able to produce suitable sites. The first step is to convert the values of the fire and erosion
model into membership values where one (1) equates to places where it is suitable to build
and zero (0) is for not suitable. Fuzzy membership was used on both the erosion and fire
models. For erosion, the model that was used in the combination is the erosion model with
3500 rain factor. The ideal range for a suitable site is 0 to 20 t/ha/yr while giving partial
membership for values of 20 to 50 t/ha/yr. For the fire model, the ideal range is 0 to 2000
kW/m and giving partial membership to values 2000 to 3000 kW/m. The converted fire and
erosion models can then be overlayed to the building model by getting the minimum or the
smaller value for each cell from the three models. We will call this model as the constrained
model. After getting the constrained model, the high conservation model is then subtracted
from it which gives us the combined model with values ranging from -1 to 1(Figure 12).
Cells with values of 0 to -1 are those that have higher conservation values, therefore these are
not suitable for building. Cells that have a value above 0 are those that have higher
constrained model values; therefore this is where we will look up sites which may be suitable
for the expansion. We limited the results by getting only sites that have values higher than .8
and having an area greater than 5000 m2. After eliminating sites that have values less than .8
and having smaller areas, three sites were found to fit our selection criteria (Figure 13).
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Figure 12: Result of combining the four models
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Figure 13: Result of combining the models and limiting results
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3.6 Ranking of Sites
The three sites that were generated by the combination of the four models were ranked
according to several criteria. Ordinal ranking was employed, giving a rank of 1 to the site that
is the best for each criteria and 3 to the lowest. The values for erosion, fire, conservation, and
building for each site were extracted from the four models (Table 5). The site with thesmallest value for erosion, fire and conservation was given the highest rank while the largest
value for building was given the highest rank. The area of each site was also considered, with
the bigger areas given higher ranking values. A view shed analysis was also used to identify
which site has a nice view (Figure 14). The site with the biggest visible area was given the
highest ranking. Area of solar radiation was derived from the surface of the DEM and the
value for solar radiation for each site was extracted from it. The site with the highest solar
radiation value was given highest ranking. The last criterion that was used to rank the sites is
the distance of each from the freeway. The distance from the freeway is the distance that you
need to travel through the firetrails.
Table 5: Ranking of the three suitable sites
CriteriaValues Ranking
Site 1 Site 2 Site 3 Site 1 Site 2 Site 3
Area (m2) 14800 10500 6500 1 2 3
Erosion (t/ha/yr) 2.5 4 4 1 2 2
Fire (kW/m) 1470 1490 1280 2 3 1
Conservation 0.2 0.2 0.2 1 1 1
Building 0.8 0.95 0.9 3 1 2
Solar (kW/m2) 617600 618100 606800 2 1 3
View (m2) 70000 45000 55000 1 3 2
Distance from freeway (m) 2300 2500 900 2 3 1
Mean 1.625 2 1.875
After evaluating each with the use of several criteria, it was found out that Site 1, which is
situated at the east part of the fire trails, is the best site for the expansion of the Cowan FieldStation (Figure 15). Even though it was lowest ranked in terms of building, it was the site
with the biggest area, smallest erosion value, and the one with the best view (in terms of
area).
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Figure 14: Viewshed analysis for each site. Higher visible area is better
Figure 15: Proposed site (Site 1) for the expansion of the Cowan Field Station
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4. Recommendations
After using the four models in our analysis, it was found out that Site 1 (Figure 15)
was the most preferable site for the Expansion of the Cowan Field Station. In terms of cost, it
has a building model value of .8 which is the lowest among the three considered sites,
however it is the biggest in terms of area, has the least erosion value, a moderate fire andsolar value and it has the best view. The distance of Site 1 from the main road and the power
supply is still acceptable. Further site inspection can also be done to confirm the suitability of
Site 1 and the other sites can also be inspected to compare them with Site 1.
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