integration of geographical information system and ...article.aascit.org/file/pdf/9760742.pdf ·...

AASCIT Journal of Environment

2017; 2(2): 23-33

http://www.aascit.org/journal/environment

ISSN: 2381-1331 (Print); ISSN: 2381-134X (Online)

Keywords Landfill,

Selection,

Site,

GIS,

MCDA,

Akure

Received: March 9, 2017

Accepted: March 29, 2017

Published: June 13, 2017

Integration of Geographical Information System and Multicriteria Decision Analysis for Landfill Site Selection in Akure, Nigeria

Olajire Olabanji Olatona1, Ojeh Vincent Nduka

2, *

1African Regional Centre for Space Science and Technology Education-English, Obafemi

Awolowo University, Ile-Ife, Nigeria 2Institute of Foundation Studies, Federal University, Otuoke, Yenagoa, Nigeria

Email address [email protected] (Ojeh V. N.) *Corresponding author

Citation Olajire Olabanji Olatona, Ojeh Vincent Nduka. Integration of Geographical Information System

and Multicriteria Decision Analysis for Landfill Site Selection in Akure Nigeria. AASCIT Journal

of Environment. Vol. 2, No. 2, 2017, pp. 23-33.

Abstract The shortage of land for waste disposal is one of the serious and growing potential

problems in most large urban areas. Although there are some efforts to reduce and

recover the waste, disposal in landfills is still the most common method for waste

destination. An inappropriate landfill site may have negative environmental, economic

and ecological impacts. Therefore, it should be selected carefully by considering both

regulations and constraints on other sources. In Nigeria solid wastes are frequently

dumped haphazardly in inappropriate locations. This study identifies suitable sites

appropriate for solid waste landfill in Akure South local government of Ondo State using

the integration of Multi-Criteria Decision Analysis (MCDA) and Geographical

Information System (GIS). GIS was used in integrating various layers of information to

produce a suitability map for solid waste landfill. The process requires the input of

multiple criteria and each of which have some level of importance. Multi-Criteria

Decision Analysis especially Analytical Hierarchy Process was used to identify the

significant role of each criteria in the analysis. Six (6) criteria were considered and were

used as input map layers and they include land use, slope, drainage, road network,

geology and lineament. Each criteria was ranked according to their level of importance

using pairwise comparison method. Weighted overlay tool in ArcGIS was used to

produce the final suitability map. Out of the 329.79km2 area of Akure South Local

government, 45.92km2 (≈14%) of the area was discovered to be most suitable for siting

landfill. The map has four (4) categories; most suitable, suitable, less suitable and not

suitable. At the end of the analysis, potential landfill sites are determined for Akure south

Local government.

1. Introduction

Disposal of waste in landfill is an inevitable part of a solid waste management system.

Most municipal waste generated in Nigeria is disposed in landfills. Nigeria with a

population growth rate of about 2.8% per annum and urban growth rate of about 5.5%

per annum (Imam et al., 2008) generates about 20kg of solid waste per capita every year

(Olaleye and Sangodina, 2000). Also, solid waste generated by an average Nigerian per

day is estimated to be around 0.49kg while commercial centres and households

24 Olajire Olabanji Olatona and Ojeh Vincent Nduka: Integration of Geographical Information System and Multicriteria

Decision Analysis for Landfill Site Selection in Akure, Nigeria

contribute almost 90% of the total waste found in urban

centres (Solomon, 2009).

In Nigeria, open or uncontrolled dumping is largely used

as waste disposal method but the benefit of sanitary landfill

over the open dumping methods cannot be overemphasized

because it is pollution-free thereby eliminating any health or

environmental risk that may result from solid waste disposal

(UN-HABITAT, 2010). The process of solid waste disposal

management mainly consists of collection, processing,

recycling and disposing. At present, waste disposal in most

cities in Nigeria is done in form of landfill disposing.

Landfill siting is a complex process involving the processing

of massive amounts of spatial data.

Geographical Information System (GIS) is a digital

database management system that is ideal for advanced site-

selection studies because it can efficiently store, retrieve,

analyse and display information according to user-defined

specifications (Kao and Lin, 1996, Sener et al., 2006 and

Shamshiry et al., 2011). Multi-Criteria Decision Analysis

(MCDA) is a set of systematic procedures for analysing

complex decision problems. These procedures include

dividing the decision problems into smaller more

understandable parts; analysing each part; and integrating the

parts in a logical manner to produce a meaningful solution

(Malczewski, 1997). The integration of GIS and MCDA

provides a reliable platform for solving the landfill site

selection problem, because GIS provides efficient

manipulation and presentation of the data and MCDA

supplies consistent ranking of the potential landfill areas

based on a variety of criteria.

2. Study Area

Akure, the capital city of Ondo State, Nigeria is located on

latitude 7.25°N and longitude 5.20°E (Figure 1). Akure is a

city in South-West Nigeria and capital of Ondo State. Akure

produces cocoa, wood, cassava and palm-oil. The city is

surrounded by extensive tracts of tropical forest reserves and

supports a large timber industry. Akure is connected by road

and water to other Nigerian cities. The city also has an

airport. The city is the site of the Federal University of

Technology Akure, Ondo State founded in 1981. Akure is a

tourist destination and departure point for visitors to the

nearby Ose River.

According to the Koppen classification scheme, Akure lies

within the AF climate type characterized by short dry season

with precipitation not less than 60mm at its driest. This area

is located within the rainforest climate region and is

characterized by average annual temperature is 25°C

temperature near the equator varies little over the course of a

year, so rain forest temperatures are about the same year

round; the average minimum monthly temperature in a rain

forest is about 18°C. Rainforests can average as little as 1.8m

(6 ft.), or as much as 9.0m (30 ft.) of rainfall a year. Every

month, typically more than 100mm (4 in) of rain falls. If a

rainforest does have dry periods, they are usually short and

unpredictable. Though, Akure lies between the tropical rain

forest and guinea savanna type climate belt but is typically

classified under the tropical rainforest belt.

Figure 1. Location map of Akure showing the City centre (1) and Airport (2) sites. Inset (top left and right) are photos of the sites, while bottom left shows

Akure in Nigeria and Nigeria in Africa.

AASCIT Journal of Environment 2017; 2(2): 23-33 25

3. Materials and Methods

3.1. Data Used

The data that was used include the Landsat 8 imagery

for Akure that was downloaded from United States

Geological Survey (USGS) site, Shuttle Radar Topography

Mission (SRTM) imagery, google earth imagery and

scanned geological map for the study area. Image

classification was carried out on the imagery using

supervised classification.

3.2. Spatial Multicriteria Decision Analysis

Decision Analysis is a set of systematic procedures for

analysing complex decision problems. These procedures

include dividing the decision problems into smaller more

understandable parts; analysing each part; and integrating the

parts in a logical manner to produce a meaningful solution

(Malczewski, 1997). In general, MCDA problems involve six

components (Keeney and Raiffa, 1976; Pitz and McKillip,

1984):

a. A goal or a set of goals the decision maker want to

achieve,

b. The decision maker or a group of decision makers

involved in the decision making process with their

preferences with respect to the evaluation criteria,

c. A set of evaluation criteria (objectives and/or physical

attributes)

d. The set of decision alternatives,

e. The set of uncontrollable (independent) variables or

states of nature (decision environment)

f. The set of outcomes or consequences associated with

each alternative attribute pair.

MCDA techniques can be used to identify a single most

preferred option, to rank options, to list a limited number of

options for subsequent detailed evaluation, or to distinguish

acceptable from unacceptable possibilities (Dodgson, 2000).

There are various types of spatial multi-Criteria decision

analysis, they include simple addictive weighting, analytical

hierarchy process, the value/utility function method, the ideal

point method, outranking method, ordered weighted average,

goal programming and compromise programming. The

method adopted for this study is the analytical hierarchy

process.

3.3. Analytical Hierarchy Process (AHP)

The AHP developed by Saaty (1980) is a technique for

analysing and supporting decisions in which multiple and

competing objectives are involved and multiple alternatives

are available. The method is based on three principles:

decomposition, comparative judgment and synthesis of

priorities.

In the AHP, the first step is that a complex decision

problem is decomposed into simpler decision problems to

form a decision hierarchy (Erkut and Moran, 1991). When

developing a hierarchy, the top level is the ultimate goal

of the decision. The hierarchy decreases from the general

to more specific until a level of attributes are reached.

Each level must be linked to the next higher level.

Typically a hierarchical structure includes four levels:

goal, objectives, attributes and alternatives. The

alternatives are represented in GIS database. Each layer

consists of the attribute values assigned to the alternatives

(cell or polygon) which are related to the higher level

elements (attributes).

Once decomposition is completed, cardinal rankings for

objectives and alternatives are required. This is done by using

pairwise comparisons which reduces the complexity of

decision making since two components are considered at a

time. It involves 3 steps: (1) development of a comparison

matrix at each level of hierarchy (2) computation of weights

for each element of the hierarchy and (3) estimation of

consistency ratio.

The final step is to combine the relative weights of the

levels obtained in the above step to produce composite

weights. This is done by means of a sequence of

multiplications of the matrices of relative weights at each

level of the hierarchy. The weighted overlay tool in ArcGIS

was used to achieve this and the final output map are

classified into not suitable, suitable and most suitable sites.

4. Result and Discussions

4.1. Land-Use

The supervised image classification carried out resulted

in the land use map of Akure. From the imagery, five land

use types were generated which include the built up areas,

bare surfaces, light vegetation (which would include farm

lands, pasture, grass lands, derived savannah, and light

forest), dense forest and rocky areas. The suitability map

for land-use was then generated by reclassify the land-use

imagery.

Table 1. Land use type by Ranking.

Land-use Types Ranking

Built up 0

Bare Surface 1

Light Vegetation 5

Dense Vegetation 3

Rocks 0



Figure 2. Land-use Maps of Akure.

4.2. Slope

The slope map was generated from Digital Elevation

Model (DEM) using the Shuttle Radar Topography Mission

(SRTM) imagery. The distribution of slope values in the

study area ranges between 0 and 60°. There are many

suggestions about slope in various literature. As stated by

Allen (2002) and Oweis et al. (1990) and applied in this

study, areas with slopes greater than 15° should be avoided

for a waste dump site.

Table 2. Slope Degree and Ranking.

Slope Ranking

0-5 5

5.1-10 3

10.1-15 1

> 15 0


Figure 3. Slope map of Akure.

4.3. Road Network

The road network was digitised from google earth

imagery. The road contains minor and major roads. By

considering suggested values from past literatures, the

distance from road and related rankings are determined for

the road network.

Table 3. Road Distance and Ranking.

Distance to Road Rankings

0-250 4

251-500 2

> 500 0



Figure 4. Road network class in Akure.

4.4. Drainage

The drainage pattern was generated from the Shuttle Radar

Topography Mission (SRTM) Imagery using hydrology

analysis tool in ArcGIS. The necessary buffer distances to

drainage are determined as 250m, 500m and above 500m.

The buffer distances were created for the drainage and

divided into three classes.

Table 4. Distance to drainage and Ranking.

Distance to Drainage Ranking

0-250 0

251-500 2

>500 4


Figure 5. The drainage pattern classes.

4.5. Lineament

Lineament is one of the most important criterion for site

selection. As contaminants can be strongly influenced by

fracturing or by an interconnected series of solutions

openings, these may provide pathways for easier flow (Lee

2003; Sener et al., 2005). Lineaments analyses of basin were

made using Shuttle Radar Topography Mission (SRTM)

satellite images. Safe distance information are compiled from

related literature. According to the calculations, the areas that

are 0–250 m away from the lineaments have the lowest

weight values, but those > 500 m from the lineaments have

the highest weight values.



Figure 6. Lineament Distances/Classes.

4.6. Geology

There are two types of geological formation in Akure. The

migmatite gneiss and granite gneiss are the geology types

present in Akure. They are not easily affected by weathering

but are rough on the surface (Ademeso, 2009). The geology

types where ranked according to their suitability for waste

dump site selection.

Table 5. Geology class and ranking.

Geology Type Ranking

Granite Gneiss 2

Migmatite Gneiss 4


Figure 7. Geology Maps/Class of Akure.

4.7. Selected Sites

The various factor for identifying a suitable location for

waste dump site were inputted into the GIS environment. The

maps for the factors were reclassified to make them suitable

for the overlay analysis. Weighted overlay was performed on

the various factor and the final result for the selected sites

was generated. The weighted overlay was performed on the

factors based on the importance ranking generated from oral

interview to experts. Out of the 329.79km2 area of Akure

South Local government, 45.92km2 area was discovered to

be most suitable for siting landfill.



Figure 8. Landfill Suitability map of Akure.

Table 6. Landfill suitability map and ranking.

CRITERIA RANKING

Settlement 26

Lineament 21

Drainage 21

Geology 16

Slope 11

Road 5

5. Conclusion

The study examined the integration of geographical

information system and multicriteria decision analysis for

landfill site selection in Akure South Local Government. The

study demonstrates that GIS and MCDA are veritable tool for

the selection of appropriate landfill locations which is needed

for sustainable urban waste management.

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