consultancy report: expansion of cowan field station

7/30/2019 Consultancy Report: Expansion of Cowan Field Station

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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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References

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consultancy report: expansion of cowan field station

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