Geographic Information System (GIS) and
Multicriteria Decision Making (MCDM) for
Optimal Selection of Hydropower Location in
Rogongon, Iligan City
Dick Arnie Q. Badang
Dept. of Electrical Engineering and
Technology, College of Engineering
and Technology
Mindanao State University - Iligan
Institute of Technology (MSU-IIT)
Iligan City, Philippines
dickarnie.badang@g.msuiit.edu.ph
Cherrence F. Sarip
Dept. of Electrical Engineering and
Technology, College of Engineering
and Technology
Mindanao State University - Iligan
Institute of Technology (MSU-IIT)
Iligan City, Philippines
cherrence.sarip@g.msuiit.edu.ph
Abstract—Extending the electrical grid for distant areas
that are inadequately electrified is not financially viable and
certainly not likely to happen in the short to medium term.
Remote areas with potential renewable energy sources are
possible to power a micro-grid that could supply their
unelectrified area. The use of Geographic Information System
(GIS) technology in this study provided an efficient tool in
identifying potential hydropower with effective determination
of the parameters from the actual field measurements and
utilization of the high resolution Digital Elevation Model
(DEM). Similarly, the application of Multi-Criteria Decision
Making (MCDM) methods in the analysis of processed
geospatial data was proven capable in finding the optimal site
location for hydropower in the remote area of Rogongon,
Iligan City. The result suggested that the proposed integration
of GIS-MCDM was successful in providing a robust model for
decision making of renewable energy location.
Keywords—Geographic Information System, Multi-criteria
Decision Making, renewable energy, hydropower
I.
INTRODUCTION
In countries where the energy infrastructure is underdeveloped and few towns are adequately electrified,
extending the grid is often not financially viable, and
certainly not likely to happen in the short to medium term. In
the Philippines, Region X (Northern Mindanao) is composed
of 1,843 barangays which are either partially or completely
energized and a total number of 1,953 sitios remain
unenergized [1]. One of these partially energized barangays
is Barangay Rogongon located in Iligan City. A number of
households in several sitios of the barangay are currently
living without the presence of electricity.
Setting up a micro-grid powered by renewable energy has
become the cheapest way to provide electricity. Aside from
that, renewable energy systems are environmentally friendly
compared to conventional energy systems. They do not
exhaust any natural resource and the inputs they use are
abundant in nature [2]. One of the most pressing issues in
this context relates to where these renewable energy power
plants should be located. Investors prefer sites with good
harvesting conditions (for example, rivers and streams)
leading to a concentration of power plants at those sites with
the best conditions [3]. However, renewable electricity
Anacita P. Tahud
Dept. of Electrical Engineering and
Technology, College of Engineering
and Technology
Mindanao State University - Iligan
Institute of Technology (MSU-IIT)
Iligan City, Philippines
anacita.palma-tahud@g.msuiit.edu.ph
production also has adverse environmental impacts, such as
loss of biodiversity and disturbances to humans, and it is
unlikely that location choices based solely on investors’
interests would result in a spatially optimal allocation from
society’s point of view, wherein the interests of the whole
society are taken into account; this includes not only aspects
of efficiency and social welfare but also distributional or
equity considerations [3].
In recent years, the integration of Geographic
Information System (GIS) and Multi-Criteria Decision
Making (MCDM) has increasingly been a popular tool in
determination for site location of renewable energy. The
integration of GIS-Analytic Hierarchy Process (GIS-AHP)
along with other decision support methods such as –
Technique for Order of Preference by Similarity to Ideal
Solution (TOPSIS), Ordered Weighted Average (OWA),
AHP and Elimination and Choice Expressing Reality
(ELECTRE) are among the widely used methods for site
location [4][5][6][7].Various researches were conducted for
the optimal site selection of hydropower particularly in the
region of Asia and Europe. In the country of Turkey, Aydin
et al. evaluated the potential of the renewable energy
resources considering the economic and environmental
factors through fuzzy weighted average algorithm [8]. In
Zanjan Province-Iran, similar study was conducted by
Farajzadeh et al. applied AHP and Fuzzy TOPSIS technique
combined with GIS in the site selection for wind turbine
installation [9]. Consequently, a research conducted in the
country of Spain, with the peak demand of PV energy,
Sanchez-Lozano carried the study in developing a ranking
for the best location in the installation of PV farms connected
to the power grid using GIS with AHP. In their study, the
combination of GIS, AHP and TOPSIS was used to obtain
the optimal placement of PV power plants in the region of
Cartagena, Spain [10].
In general, this study focuses on the collaboration of GIS
as a means of processing and analyzing data and the
implementation of AHP-TOPSIS in the derivation of criteria
weights and the ranking of potential sites. Specifically, the
objectives of this study are as follows:
•
To determine the hydropower sites and their
estimated technical potential;
Dept. of Science & Technology (DOST) and Office of the Vice
Chancellor for Research & Extension (OVCRE), MSU-IIT.
978-1-5386-7767-4/18/$31.00 ©2018 IEEE
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•
To determine the weights of the selected criteria
based on judgments by experts;
•
To rank the suitable areas according to the selected
criteria; and
•
To create a thematic map for hydropower sites
classified according to their ranking value.
This study is part of the “Techno-Economic Feasibility
Study of Micro-Grid in a Remote Community” Project, a
research project of MSU-IIT funded by the Department of
Science and Technology (DOST). The information derived
from this study is helpful to policy makers and concerned
agencies in hydro energy resource management, planning
and development. Furthermore, this could provide a current
and reliable assessment for possible investors and hence
greatly benefit the community involved.
II.
METHODOLOGY
A. The Study Area
Rogongon, one of the 44 barangays in Iligan City,
Philippines with total land area of 35,555 hectares (355.55
km2) represents about 44% of the total land area of the city.
It is located northwestern part of the city and is bounded on
the north by portion of Province of Misamis Oriental and
Cagayan de Oro City; on the east by Province of Bukidnon;
on the south by Barangay Kalilangan and Panoroganan; and
on the west by Barangay Digkila-an.
Majority of the inhabitants in Brgy. Rogongon are
Indigenous People (IP), specifically “Higaunons”. The
ancestral domain area is endowed with rich water resources
and has promising hydropower potential [11]. However,
since it is situated in the remotest part of Iligan, majority of
the area still has no access to electricity, which is a major
hindrance to its economic development.
The study area is composed of selected sitios in the
barangay. These sitios and their corresponding number of
households are listed in Table 1 while their locations are
shown in Fig. 1.
TABLE I.
SELECTED SITIOS (IN BRGY. ROGONGON, ILIGAN CITY)
WITHOUT ACCESS TO ELECTRICITY
Sitio
Malagsum
Gabunan
Libandayan
Languisan
Salingsing
Number of households
25
20
18
15
25
Fig. 1. Study Area in Rogongon, Iligan City
B. Development of Hydropower Potential Sites
There were 696 identified hydropower potential sites
along the stream networks of Brgy. Rogongon [11]. Some of
these hydropower potential sites are found along Icog River
and Malagsum River, which are near the target sitios. For
data processing of hydropower installation, these sites were
validated and other necessary data were obtained by
conducting an actual flow measurement in the study area.
Equation (1) was used to derive the technical potential of
each potential site [12].
ܲ ൌ ߟ௧ ߟ௚ ߩ݃ܳ‫ܪ‬
(1)
where,
P = Power Output (kW)
ߟ௧ = Turbine efficiency (85%)
ߟ௚ = Generator efficiency (95%)
= Density of water (1000 kg/m3)
g = Acceleration due to gravity (9.81 m/s2)
Q = Plant discharge (m3/s)
H = Effective head (m); with head loss = 10%
Moreover, the project cost can be derived by determining
the turbine classification, head and flow rate of the potential
river stream. Particularly for this study, the turbine type for
all six potential sites lies in the Kaplan class. The sum of
electromechanical cost, ‫ܥ‬ாெ in (2), and turbine cost, ‫ܥ‬௄௔௣௟௔௡
in (3), was used to estimate the project cost of each
alternative sites [13].
‫ܥ‬ாெ ൌ ͳʹǡ ͲͲͲ ൈ ቀ
௞ௐ ଴Ǥହ଺
ு బǤమ
ቁ
‹͉
‫ܥ‬௄௔௣௟௔௡ ൌ ͳͷǡ ͲͲͲ ൈ ሺܳ ൈ ‫ܪ‬ሻ଴Ǥ଺଼ ‹͉
(2)
(3)
C. Multi-criteria Decision Making
The optimal selection of renewable energy sites generally
composed of various and conflicting criteria. The application
of MCDM became a necessity to ease the site selection for
small scale renewable energy applications [14]. Among the
decision-making methods, AHP was chosen as the suitable
approach for the weighing of criteria with the combination of
TOPSIS for the determination of alternative’s scores and
ranking.
AHP, popularized by Thomas L. Saaty in 1970, with a
unique characteristic of weighing criteria through
hierarchical structuring of decision making problems is the
best course of action when combining multiple inputs from
several persons to consolidate the outcome [15]. In this
study, five experts in the field of renewable energy were
chosen to rate the relationship of each criteria based on their
own viewpoint using the Saaty scale shown in Table II.
In addition, TOPSIS originally developed by Hwang and
Yoon in 1981, with its robustness and simplicity of ranking
alternatives, was used in determining the optimal site
location and its corresponding score based on its evaluation
[16].
TOPSIS is a ranking method that determines
optimality by the geometric distance of the alternative from
the ideal solutions. The most suitable location was chosen as
the closest value from the positive ideal solution and the
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farthest to the negative solution. The combination of the two
decision making methods provides a very clear distinction
between the criteria and alternatives involved.
TABLE II.
Intensity of
Importance
SAATY’S SCALE OF PREFERENCE
Definition
1
Equal Importance
2
Weak or slightly
importance
3
Moderate importance
4
Moderate plus
5
Strong importance
6
Strong plus
Very strong or
demonstrated importance
7
8
Very, very strong
9
Extreme Importance
Explanation
Two indicators
contribute equal to the
objective
Experience and
judgment slightly favour
one over another
Experience and
judgment strongly
favour one over another
Experience and
judgment strongly
favour one over another;
its dominance
demonstrated in practice
The evidence favouring
one over another is of
the highest possible
order of affirmation
Fig. 2. Malagsum River Potential Sites’ Intakes and Outtakes.
D. Visualization of the Potential Sites’ Ranking and Scores
Finally, thematic maps were created in ArcGIS 10.2.2 for
hydropower potentials sites with their corresponding ranking
added in the attribute table. The ranking values were based
on the results obtained from the ranking done by TOPSIS.
The features are then displayed based on their values for a
particular attribute to deliver a specific message (ranking) by
displaying unique values.
III.
RESULTS AND DISCUSSION
A. Hydropower Potential Sites Capacity and Location
In determining the hydropower potential of the sites,
previous study results were validated and certain measures
and processes were made to establish the parameters needed
in the calculation. Site surveys were thus conducted in
Malagsum River and Icog River.
For the determination of the average river velocity, three
locations for each river were considered for actual
measurement to complement with the available hydrologic
data. The intake point and outtake point for each potential
site are shown in Fig. 2 and Fig.3 while the corresponding
coordinates are shown in Table III. Elevation data were
determined using the Synthetic Aperture Radar (SAR)-DEM
from Phil-LIDAR.
For each river, three possible sites for run-of-river
hydropower development were identified. The calculated
capacities are shown in Table IV. All sites belong to the
micro-hydropower classification (< 100 kW).
Results showed that Malagsum River has greater
hydropower potential compared to Icog River. The combined
hydropower potential in these sites is enough to provide the
energy demand in the unelectrified sitios. Also, during the
actual survey, it was found out that although Malagsum has a
difficult terrain with cascading waterfalls, it is the nearest
river to the community, making it a good candidate as
hydropower source for the study area.
Fig. 3. Icog River Potential Sites’ Intakes and Outtakes.
TABLE III.
SITE INTAKE AND OUTTAKE COORDINATES
Intake
Outtake
Site
No.
Latitude
Longitude
Latitude
Longitude
1
8°12'49.5"N
124°26'47.5"E
8°12'48.0"N
124°26'44.6"E
2
8°12'43.3"N
124°26'41.7"E
8°12'40.1"N
124°26'41.3"E
3
8°12'38.8"N
124°26'41.8"E
8°12'35.9"N
124°26'42.2"E
4
8°14'8.8"N
124°28'4.5"E
8°14'8.0"N
124°28'7.6"E
5
8°14'6.8"N
124°28'9.4"E
8°14'6.1"N
124°28'12.4"E
6
8°14'6.7"N
124°28'10.2"E
8°14'5.8"N
124°28'13.20E
TABLE IV.
Site
No.
1
2
3
4
5
6
River
Malagsum
Malagsum
Malagsum
Icog
Icog
Icog
TECHNICAL POTENTIAL OF POTENTIAL SITES
Average River
Discharge (m3/s)
0.43
0.43
0.43
0.15
0.15
0.15
Gross
Head (m)
4.41
28.23
27.60
25.27
17.85
15.07
Capacity
(kW)
15.14
96.85
94.70
29.38
20.76
17.53
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B. Established Criteria and Weights
During the conduct of the study, rating sheets were given
to five experts in the field. The extracted data were then used
for determining the weights of each criterion considered in
the study. After processing the criteria scores rated by the
experts using the AHP algorithm programmed with
MATLAB 2016a software, the critical ratio index (CRI) was
determined. Out of 5 experts, only 4 experts had successfully
reached the allowable tolerance (CRI < 0.1) for minimum
error in weighing of criteria.
The weights for hydropower site selection are presented
in Table V. Electricity generation, with fifty percentage of
the overall weight, was chosen as the most important criteria
followed by Socio-economics (0.3780) and lastly
Engineering and Economics (0.1181).
TABLE V.
MAIN CRITERIA AND SUB-CRITERIA WEIGHTS FOR
HYDROPOWER SITE SELECTION
Main
Criteria
Electricity
Generation
(A)
(0.5039)
Subcriteria
A1
A2
A3
B1
Engineering
and
Economics
(B)
(0.1181)
B2
B3
B4
Socioeconomics
(C)
(0.3780)
C1
C2
Site Capacity
(0.58333)
Load Demand
Supplied (0.12286)
Length of
Transmission Lines
(0.29381)
Project Cost
(0.46106)
Difficulty in
Transportation of
Materials (0.24723)
Accessibility to
Project Site
(0.21779)
Obstruction in the
Site (0.07392)
Social Conflict
(0.67708)
Water Resource
Problem (0.32292)
Type
Overall
Weight
Quantitative
0.29392
Quantitative
0.0619
Quantitative
0.14804
Quantitative
0.05448
Qualitative
0.02921
Quantitative
0.02573
Qualitative
0.00873
Qualitative
0.25592
Qualitative
0.12206
C. Potential Sites Ranking
The potential sites were ranked according to their
capacity and it turned out that Malagsum River has a greater
potential with the exemption of site 1 compared to the
capacity generation along the Icog River. The Malagsum
River is also situated near the major roads of Rogongon
compared to the Icog River. The project cost is directly
related to the capacity of the site (i.e., the greater the
potential, the higher the cost). The obstruction to site was
evaluated by the researchers who visited the site and the
result shows an obstruction index ranging between 2 to 3 for
the six potential sites.
criteria and subcriteria were assigned with different weights
using AHP by selected experts and with the use of TOPSIS,
the hydropower potential sites were ranked according to their
criteria values. TOPSIS method was used as the MCDM tool
to rank the potential sites of hydropower with its simplicity
and comprehensibility in giving the relationship of the site
distance from the ideal solution. The Attributes for potential
sites of hydropower together with the AHP weighing result
were used as inputs for the TOPSIS algorithm. The result of
the analysis is shown in Table VI with the corresponding
ranking of the potential sites and distance from the ideal
solution.
The site that is nearest to the Positive Ideal solution and
farthest from the Negative Ideal Solution was chosen as the
best site for developing hydropower in the area. Site 2 along
the Malagsum River was considered as the optimal site with
an ideal distance of 0.888. Site 3 as well can be considered a
good spot for developing hydro project with little difference
compared to Site 2. However, Site 1 and Site 4-6 has lesser
scores and may not be appropriate when choosing the site
location. The major reason for a huge gap in the ideal
distance was due to a higher weight imposed for electricity
generation which the other site lacks.
TABLE VI.
TOPSIS RANKING RESULTS FOR HYDROPOWER SITES
Site
No.
1
Positive Ideal
Distance
Negative Ideal
Distance
Ideal Distance
Rank
0.1692454529
0.03002159549
0.1506601102
4
2
0.0214070522
0.17055576806
0.8884833416
1
3
0.0217783636
0.16573301965
0.8838557782
2
4
0.1407976308
0.03524892627
0.2002250248
3
5
0.1588516142
0.02447651766
0.1335120660
5
6
0.1655682587
0.02214287674
0.1179625102
6
D. Creation of Thematic Maps
The ranking of hydropower potential sites results from
the process made by the MCDA is utilized in deriving
thematic maps using ArcGIS 10.
Fig. 4 shows the classified six (6) hydropower potential
sites along the streams of Malagsum and Icog using their
unique ranking values.
The social conflict for all alternatives was scored 1 for all
locations since the residence in the community approved any
projects in the said area. Both the social conflict and water
resource problem were rated by the Indigenous People (IP)
leaders of the community. According to the IP leaders,
Malagsum River is more prone to flashfloods causing huge
turbulence of water during extreme weather conditions rather
than the Icog River.
With the aid of Multi-Criteria Decision Analysis
(MCDA) implemented in MATLAB 2016a, all selected main
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Fig. 4. Map of hydropower potential sites classified according to their
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IV. SUMMARY AND CONCLUSIONS
The use of GIS technology in this study indeed provided
an efficient identification of the site capacities of the
hydropower sites by effective determination of the parameter
used such as head with the utilization of the high resolution
DEM.
The application of MCDM methods in the analysis of
processed geospatial data was proven capable in finding the
optimal site location for hydropower in remote area of
Rogongon, Iligan City. Two MCDM methods were proposed
in this study: AHP for the determination of weights and
TOPSIS for the ranking of potential sites. The
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hydropower in the area. The unique characteristics of
TOPSIS in calculating the distance from the ideal solutions
presents a clear distinction between the alternatives. In
conclusion, the proposed integration of GIS-MCDM was
successful in providing a robust model for decision making
in renewable energy location.
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ACKNOWLEDGMENT
The authors thank the following: Department of Science
and Technology (DOST) and Philippine Development –
Shell (PhilDev-Shell) for scholarships granted, the IP leaders
of Brgy. Rogongon and the “Techno-Economic Feasibility
Study of Micro-Grid in a Remote Community” Project of
MSU-IIT, COET-DEET Faculty, dear parents – Lalaine E.
Badang and Asterion E. Badang Jr.; Porsosa F. Sarip and
Sammy D. Sarip.
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