1. Introduction
Solid waste management is a global concern, particularly in developing nations, where the availability of sanitary landfills is unavoidable due to rapid population growth and urbanization [
1]. Because of urbanization and rapid growth, the generation of solid waste has also increased dramatically [
2]. Similarly, in the Hargeisa study region, the population and economic growth due to urbanization have increased the amount of municipal waste [
3]. Nonetheless, the estimated number of people residing in Hargeisa is about one million, with an average annual population growth rate of 3.1% [
4]. This means the city has doubled its inhabitants in the past ten years. Accordingly, the city has expanded remarkably in all directions, and many new areas are joining. Despite having the city's fastest rate of population increase, it lacks a landfill to handle the solid waste produced, and open dumping is widely used.
Recent studies [
5,
6], from various African nations, have found that the continent's solid waste management is frequently weak due to poor planning, poor governance, outdated technology, lack of enforcement of current laws, and a lack of financial and economic incentives to encourage environmentally sound development. And this situation makes it worse when it comes to low-income nations [
7]. In a nutshell, both developed and developing countries have faced serious and inevitable problems related to solid waste generation [
8]. The city's growing population and economic activities produced much solid waste in Somaliland. In addition, solid waste generation in the study area was observed at an increasing rate compared to management response.
Moreover, the city's per capita solid waste generation rate is 0.4kg/capita/day [
9]. That figure is expected to rise because of population growth. In addition, only about half of the generated solid waste was collected and dumped at the open dump sites on the city's outskirts.
In contrast, good solid waste disposal design and siting practices can greatly reduce the danger of environmental pollution and public health issues posed by an inefficient system, which is common in poor countries [
2,
10,
11]. Creating a secure place for solid waste disposal is not an easy task. It is time-consuming, costly, and necessitates numerous difficult steps. This, however, necessitates understanding from a variety of fields, including geology, environmental science, urban planning, soil science, and hydrology [
12].
On the other hand, various techniques and approaches have been employed by numerous scholars to identify ideal locations for landfills, particularly in major cities around the globe. Among them, is the Ratio Scale Weighting (RSW) method [
13]. The integration of the fuzzy MCDM method, i.e., fuzzy analytic hierarchy process (FAHP) [
14]. The Decision making trial and evaluation laboratory (DEMATEL) method [
11]. And fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method [
15]. A recent study by [
10], used a GIS-based analysis for sanitary landfill sites in Abuja, Nigeria. However, the aforementioned study did not consider multicriteria decision analysis when using a GIS technique. In addition, [
16], studied landfill location by fuzzy TOPSIS for Istanbul city. In contrast, [
17], has reported that the conventional TOPSIS model has several drawbacks, such as the inability to generate a strong correlation between criteria. Moreover, this may result in uncertainty in obtaining weights. Because of that, accurate results might be found through the integration of GIS with AHP techniques [
18].
As a result, one of the most recently recommended multicriteria decision analysis (MCDA) methodologies; the Analytic hierarchy process (AHP), has been used in selecting potential landfill sites to offer the ideal location with low socioeconomic and environmental repercussions [
19]. In the previous two decades, it has become increasingly popular for this purpose. Furthermore, the versatility of the GIS-based multicriteria evaluation (MCE) technique in managing large amounts of geographical data from many sources makes it an ideal tool for such studies [
20]. However, the experts' opinions on the weighting scale might produce different results [
21]. But so far, because of its simplicity in pair-wise comparisons, consistency in evaluation, and adaptability, the AHP is chosen as the best method from a selection of possible techniques and provides the decision-makers with an accurate solution [
22]. Besides, its many advantages, recent studies have reported the usefulness of integrated AHP and GIS techniques [
23,
24].
In African countries, roughly 95% of solid waste produced from different sources is discarded at the peripheries of cities or in open dumpsites [
25]. In the context of Hargeisa, the City Municipal Council (CMC) has not allotted any land for solid waste processing, and so far, no such facility has been created. Additionally, the city produces more solid waste than its current disposal sites can handle, underscoring the need for a new sanitary landfill. Besides, the existing dumpsite (South Dumping Site -one) is so close to the Airport (4.6 km) it harms aircraft visibility. Thus, the airport authorities are highly concerned about this threat and have complained to the municipal authority to close or shift this site. Therefore, to accommodate the generated solid waste from the municipal, it is compulsory to propose a new trustworthy solid waste landfill site for the city by considering ecological, environmental, and socio-economic considerations.
Various studies on solid waste management have been conducted throughout the country, including [
3], on sustainable waste management in the construction industry and [
26], on constraints for solid waste management in Somaliland. However, this current study is novel for the municipal area of Hargeisa, as this is the first of such kind using advanced GIS techniques with the integration of AHP, restriction analysis, and selection criteria. With this purpose, the current study aims to integrate multicriteria decision analysis and GIS for evaluating the site suitability for the landfill in Hargeisa City and its environs, Somaliland. As a result, it will reduce the cost, time, and no or less environmental and socio-economic impact, leading to the sustainable management of solid waste as part of the sustainable development goals (SDG).
2. Materials and Methods
2.1. Description of the study site
Hargeisa is situated in a valley in the Galgodon (Ogo) highlands and sits at an elevation of 1,334 meters above sea level (4,377 ft.). Figure 1 depicts the latitude and longitude of the city at 9°34′N and 44°4′E, respectively. The area of the city is 56 Sq. Km encompasses eight districts within its administrative boundaries. It represents the capital of Somaliland and its main gateways of trading centers to all regions in Somaliland and neighboring countries. Additionally, the climate of the study region is warm and dry in semiarid conditions. From the historical temperature records, the average maximum and minimum temperatures of the area are determined. Accordingly, the maximum and minimum annual average temperature of the study area is 25.9°C and 23.9°C, respectively. Moreover, Somaliland has a bimodal rainfall distribution with average annual rainfall levels of 400 to 500mm.
Furthermore, there are fewer weather-related hazards in Hargeisa City; there hasn't been any recent seismic activity. However, flooding happens practically every time it rains due to a shortage of storm drains brought on by inadequate urban infrastructure and blocked drains due to haphazardly dumped wastes. Therefore, it is crucial to find new landfill locations for the City of Hargeisa to properly dispose of municipal solid waste (MSW) while taking into account pertinent environmental, social, and economic considerations.
2.2. Current status of municipal solid waste management in Hargeisa
The effects of solid waste on the environment, human health, society, and the economy are becoming a global threat, particularly in low-income nations [
6,
27]. A similar problem was encountered in the study area, where medical waste is mixed with municipal waste, posing a serious threat to the health and environment of the workforce, rag pickers, and the general public.
Solid waste management is a principal function of the Municipal Council. However, municipal authority, primarily responsible for managing solid waste, lacks in-house capabilities and adequate finances for managing solid waste effectively. As a result, the citizens generally dispose of their solid waste on the streets and open spaces around them, creating unhygienic conditions. Moreover, plastic bags were seen littered all around and sticking on the trees and bushes. Likewise, street sweepers only occur around commercial areas and on main streets. However, municipal solid waste management by-laws [
28], made it mandatory to have no solid waste on the streets and to separate solid waste at the source for biodegradable and non-biodegradable solid waste. So far, for various reasons, the municipal authority has not been able to implement these bye-laws. As a result, even the solid waste that has been collected and transferred officially is dumped at the unregulated open dump site. As a result of environmental pollution and other aesthetic effects, it is also unsuitable for achieving the minimum criteria set by environmental protection agencies.
On the other hand, existing dumpsites were not given a scientific appraisal when introduced. Also, the [
9], feasibility study noted the imminent need for a sanitary landfill as well as the closure of current dumpsites. Thus, insufficient solid waste service is anticipated to impact this city's productivity and economic growth negatively. Therefore, it is essential to introduce new, appropriately evaluated alternative disposal sites to meet an exponentially growing population's solid waste disposal needs.
Inadequate storage of solid waste at the source, lack of separation of recyclable solid waste, lack of primary collection of solid waste from the doorstep, irregular street sweeping, inappropriate and unhygienic secondary storage of solid waste, irregular transport of solid waste in open vehicles, lack of treatment of solid waste, and unhygienic solid waste disposal are just a few of the obvious deficiencies in the city's solid waste management [
9].
Therefore, in order to find sustainable solutions to these general problems in Hargeisa, it is vital to research and suggests the appropriate landfill locations. Thus, this study contributes to the provision of pertinent information necessary for choosing suitable solid waste management sites.
2.3. Current status of municipal solid waste disposal sites in the studied area
The entire solid waste of the city is disposed of at the dumping grounds untreated. The valuable resource is quite often burnt. The City Municipal Council has adopted crude dumping as a method of solid waste disposal. Municipal solid waste is disposed of in only two landfills within the city limits, resulting in nuisance and unsanitary conditions (Figure 2).
Generally, the solid waste disposal sites, including the roadways, are poorly managed. Solid waste is dumped in open spaces haphazardly anywhere. Additionally, the solid waste is neither spread nor covered. The site is full of birds and baboons. Smoke is seen emanating from the heaps of solid waste, posing a serious threat to the human health, environment, and safety of aircraft. Burning solid waste is done to reduce the solid waste volume, resulting in air pollution by releasing pollutants like dioxins. There is no segregation of solid waste. Solid waste contains a lot of plastic, tin, metals, and glass, which can be recycled [
9].
The landfill is located just 4.6 km from the Airport, significantly impacting aircraft movement. The airport authorities are seriously concerned about this threat and have complained to the municipal authority to close or shift this site. Besides, the site is located on the hillside; the leachate/ contaminated water flows down the slope and contaminates the downstream. Furthermore, the strong winds are spreading plastic bags everywhere, significantly affecting the activities and health of the surrounding community [
9].
It is the largest dumpsite in the city. Previously, it was far from the habitation, but today the plotting for housing has been done very much near the site. At both municipal solid waste disposal sites, there has been no digging of pits/holes. Solid waste is being directly dumped and burnt. The solid waste disposed of at the earlier site (old disposal site) near the present site has not been capped. It is understood to dispose of it off to the new disposal site, which is not far away. The same procedure of burning solid waste is adopted at the new location. Once the fire is extinguished, the ashes are moved aside so that new solid waste can be dumped and burned. Besides, since both the old and the new solid waste disposal sites are located on the hilltop, the flow of the contaminated water during rains goes down in the valley towards the streams and drinking water source/wells near the area [
9].
Moreover, although some scavenging is done at the site by rag pickers, tin, glass, and plastic bags are still not picked up. In addition, during strong winds, plastic bags are transported over long distances and staked in the community. Therefore, the sustainability of the environment and public health was more seriously threatened by open burning, leachate leakage, and disturbance from the north open dumping site [
9].
Keeping all the facts mentioned above, this study was designed to identify the best future solid waste landfills for the City of Hargeisa, using GIS-based Analytical Hierarchy Processes.
Figure 1.
Map of the study area.
Figure 1.
Map of the study area.
Figure 2.
Open dumping and burning of solid waste, including medical waste, at the dumpsites a) North dumping site – two b) South dumping site –one.
Figure 2.
Open dumping and burning of solid waste, including medical waste, at the dumpsites a) North dumping site – two b) South dumping site –one.
2.4. Simulation model design
Figure 3 displays the developed methodology applied in this study. Eleven determinant factors were used in the current study, including proximity to existing dump sites, surface water, and river access, boreholes (distance to water wells), airport, and distance from main roads, land use and land cover (LULC), geology, soil type, elevation, and slope.
2.5. Data collection and processing
In order to successfully investigate the entire region and assess the level of suitability for the area, technical and social data were also collected using both primary and secondary data. Additionally, information was gathered from a variety of sources, including the most recent multispectral satellite pictures, cloudless Landsat geo-referenced data, DEM, and professional opinions
(Table 1). Several softwares, including ERDAS Image 2015 and ArcGIS 10.3, were used to do this. According to [
29], one of the most accurate elevation data that is currently freely available is derived from the Shuttle Radar Topographic Mission (SRTM), a Digital Elevation Model (DEM) dataset with 12.5 m spatial resolution. On the Open Street Map website
(https://www.openstreetmap.org), road data were downloaded. A portable global positioning system (GPS) was used to gather data on airports and existing dumpsites in the research area. The geology and soil of the research region were retrieved from the geological, and soil datasets that were received from Somalia Water and Land Information Management (SWALIM)
(https://faoswalim.org). First, using Arc-GIS 10.3 software, all criteria utilized in this study were geo-referenced and transformed into a raster format in order to be ready for categorization and standardization. All criteria were then geo-referenced to zone 38 N of the UTM Projection system. The spatial resolution of 12.5 m was achieved by rasterizing and resampling vector datasets. Secondly, using the spatial analyst tool in ArcGIS 10.3 software, all input datasets were reclassified, ranked, and then standardized into unsuitable, less-suitable, suitable, moderately suitable, and very high suitable zones with their given weights ranging from 1 to 5. By using the AHP technique, where the consistency ratio was assessed, weights were assigned to each thematic dataset. Following the integration of these datasets using the Weighted Linear Combination (WLC) method, a map of the suitability of solid waste landfill sites was created. Finally, using the predetermined eleven influencing parameters, very suitable sites in the study area were identified.
Table 1.
Dataset used in the study.
Table 1.
Dataset used in the study.
No. |
Map layer |
Data source |
1 |
Base Map |
Map of the area (1:50,000) Satellite images from LANDSAT-8 (12.5 m) |
2 |
Dumpsite locations |
GPS handheld data collection with google earth verifications |
3 |
Well data |
SWALIM https://faoswalim.org |
4 |
LULC |
LANDSAT-8 satellite imagery (12.5) with google earth verifications |
5 |
Road map |
Open Street Map |
6 |
Slope Map |
ASTER-DEM (12.5) http://earthexplorer.usgs.gov/; |
7 |
Elevation |
ASTER-DEM (12.5) http://earthexplorer.usgs.gov/; |
8 |
River |
Google Earth pro |
9 |
Airport |
GPS handheld data collection with google earth verifications |
10 |
Geological Structure |
SWALIM https://faoswalim.org |
13 |
Soil information |
FAO-SWALIM Organization funded by EU https://faoswalim.org |
Figure 3.
The methodological framework in the study.
Figure 3.
The methodological framework in the study.
2.6. Application of GIS-based multicriteria decision analysis in landfill sites selection
2.6.1. Analytic hierarchy process
Using Eqs. 1 to 7, AHP has been determined. The multicriteria decision analysis (MCDA) technique known as AHP was developed by [
30,
31]. In this study, the AHP-entropy technique was employed to analyze data from a questionnaire survey. Therefore, specialists with in-depth knowledge and experience in choosing solid waste dump sites were invited to take part in the survey. Additionally, after normalizing the matrix value total and dividing it by numerous criteria, the weights for each criterion were determined. Due to the ability to statistically evaluate the judgment's accuracy, this technique becomes more dependable [
29].
Moreover, the fundamental steps for applying the AHP approach are as follows [
32].
Step 1 –Compare the factors: With nine levels of intensity scale in
Table 3,
the pair-wise matrix was constructed using the perspectives of the experts [
16]. Which are shown in
Table 4. In addition, the pair-wise comparison matrix calculation, as calculated by the following equation:
Where C11 represents the ith row's (first row) and jth column's (first column's) respective values in this comparison matrix.
Step-2: Complete the matrix: The matrix's values were added independently for each column [
33]. Additionally, the column sums of the pair-wise matrices are given by the following equation:
Step-3: Matrix Normalization: The normalization for each column value can then be expressed using the following equations, as shown in
Table 5.
Step-4: weight determination: After normalization, the row sum in the normalization matrix was divided by the total number of criteria [
14]. The following is how the priority vector's criteria weights are calculated:
Step-5: Calculate the Consistency Ratio (C.R.) because only the consistency ratio (C.R.) value may be used to evaluate the judgment value's trustworthiness. As a result, when the C.R. value was less than 0.10 (10%), the comparison matrix was consistent, as indicated by [
30].
Step- 5A: Lambda ( λ) max: The average value of each consistency vector was used to calculate the principal eigenvector (
λmax). Following is the equation that was used to obtain the principal eigenvalue (
λmax) [
18].
Step 5B: The consistency Index (CI) was chosen to assess the degree of a matrix's departure from consistency. The value of
𝜆max was highlighted as being necessary for the discussion of the consistency ratio calculation [
34]. The Consistency Index (CI) was calculated as follows:
Where 𝜆max is the maximum eigenvalue and n represents the number of criteria.
Step 5C: Random Index (R.I.) The only factor affecting the random index is how many elements were compared.
Table 2 below displays the random index values for the consistency index.
Table 2.
values of random index values for the consistency index [
30].
Table 2.
values of random index values for the consistency index [
30].
n |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
RI |
0.00 |
0.00 |
0.52 |
0.89 |
1.11 |
1.25 |
1.35 |
1.40 |
1.45 |
1.49 |
1.51 |
1.54 |
1.56 |
1.57 |
1.58 |
Step-5D: Consistency Ratio (C.R): Comparing the CI with the Random Index resulted in the development of the final consistency ratio [
30]. Shown in
Table 6
The current study's C.R. is 0.05 and is less than 0.10. If the obtained C.R is higher than this threshold, the judicial response in the pair-wise comparison matrix is regarded as inconsistent, and the process needs to be redone [
29]. It, therefore, suggests that the weights assigned were appropriate. Additionally, the model accurately reflected the degree of reality present in the research area, demonstrating the method's efficacy in locating and mapping landfill-prone locations.
Table 3.
the nine-point weighing scale for pair-wise comparisons [
30].
Table 3.
the nine-point weighing scale for pair-wise comparisons [
30].
Intensity of importance |
Description |
Suitability class |
1 |
Equal importance |
Low suitability |
2 |
Equal to moderate importance |
Very low suitability |
3 |
Moderate importance |
Low suitability |
4 |
Moderate to strong importance |
Moderately low suitability |
5 |
Strong importance |
Moderately suitability |
6 |
Strong to very strong importance |
Moderate high suitability |
7 |
Very strong importance |
High suitability |
8 |
Very to extremely strong importance |
Very high suitability |
9 |
Extreme importance |
Highest suitability |
Table 4.
Pair-wise comparison matrix for selected landfill controlling factors.
Table 4.
Pair-wise comparison matrix for selected landfill controlling factors.
Factors |
(C1) |
(C2) |
(C3) |
(C4) |
(C5) |
(C6) |
(C7) |
(C8) |
(C9) |
(C10) |
(C11) |
LULC (C1) |
1 |
2 |
2 |
2 |
3 |
3 |
3 |
2 |
3 |
4 |
4 |
Habitations (C2) |
0.50 |
1 |
1 |
3 |
2 |
3 |
3 |
1 |
4 |
5 |
5 |
Water bodies (C3) |
0.50 |
1.00 |
1 |
2 |
2 |
3 |
3 |
2 |
4 |
4 |
6 |
Airport (C4) |
0.50 |
.33 |
.50 |
1 |
2 |
2 |
3 |
2 |
3 |
4 |
5 |
Elevation (C5) |
0.33 |
.50 |
.50 |
.50 |
1 |
2 |
3 |
3 |
4 |
4 |
4 |
Roads (C6) |
0.33 |
.33 |
.33 |
.50 |
.50 |
1 |
2 |
2 |
3 |
4 |
5 |
Slope (C7) |
0.33 |
0.33 |
0.33 |
0.33 |
0.33 |
0.50 |
1 |
1 |
2 |
3 |
4 |
Lithology (C8) |
0.50 |
1.00 |
0.50 |
0.50 |
0.33 |
0.50 |
1.00 |
1 |
2 |
3 |
4 |
Soil types (C9) |
0.33 |
0.25 |
0.25 |
0.33 |
0.25 |
0.33 |
0.50 |
0.50 |
1 |
2 |
3 |
Borehole (C10) |
0.25 |
0.20 |
0.25 |
0.25 |
0.25 |
0.25 |
0.33 |
0.33 |
0.5 |
1 |
2 |
Existing Dumpsites (C11) |
0.25 |
0.25 |
0.16 |
0.20 |
0.25 |
0.20 |
0.25 |
0.25 |
.33 |
0.50 |
1 |
Sum |
4.83 |
7.15 |
6.83 |
10.6 |
11.9 |
15.8 |
20.1 |
15.1 |
27 |
34.5 |
43 |
Table 5.
Normalized pair-wise comparison matrix and calculated criteria weight for each parameter.
Table 5.
Normalized pair-wise comparison matrix and calculated criteria weight for each parameter.
Factors |
(C1) |
(C2) |
(C3) |
(C4) |
(C5) |
(C6) |
(C7) |
(C8) |
(C9) |
(C10) |
(C11) |
Sum |
CriteriaWeights |
Criteria weight (%) |
(C1) |
0.21 |
0.28 |
0.29 |
0.19 |
0.25 |
0.19 |
0.15 |
0.13 |
0.11 |
0.11 |
0.09 |
2.01 |
0.18 |
18 |
(C2) |
0.10 |
0.14 |
0.15 |
0.28 |
0.17 |
0.19 |
0.15 |
0.07 |
0.15 |
0.14 |
0.12 |
1.66 |
0.15 |
15 |
(C3) |
0.10 |
0.14 |
0.15 |
0.19 |
0.17 |
0.19 |
0.15 |
0.13 |
0.15 |
0.12 |
0.12 |
1.62 |
0.15 |
15 |
(C4) |
0.10 |
0.05 |
0.07 |
0.09 |
0.17 |
0.13 |
0.15 |
0.13 |
0.15 |
0.12 |
0.12 |
1.24 |
0.11 |
11 |
(C5) |
0.07 |
0.07 |
0.07 |
0.05 |
0.08 |
0.13 |
0.15 |
0.20 |
0.15 |
0.12 |
0.09 |
1.18 |
0.11 |
11 |
(C6) |
0.07 |
0.05 |
0.05 |
0.05 |
0.04 |
0.06 |
0.10 |
0.13 |
0.11 |
0.12 |
0.12 |
0.89 |
0.08 |
8 |
(C7) |
0.07 |
0.05 |
0.05 |
0.03 |
0.03 |
0.03 |
0.05 |
0.07 |
0.07 |
0.09 |
0.09 |
0.63 |
0.06 |
6 |
(C8) |
0.10 |
0.14 |
0.07 |
0.05 |
0.03 |
0.03 |
0.05 |
0.07 |
0.07 |
0.09 |
0.09 |
0.79 |
0.07 |
7 |
(C9) |
0.07 |
0.04 |
0.04 |
0.03 |
0.02 |
0.02 |
0.03 |
0.03 |
0.04 |
0.06 |
0.02 |
0.44 |
0.04 |
4 |
(C10) |
0.05 |
0.03 |
0.04 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.03 |
0.05 |
0.31 |
0.03 |
3 |
(C11) |
0.05 |
0.03 |
0.02 |
0.02 |
0.02 |
0.01 |
0.01 |
0.07 |
0.01 |
0.01 |
0.02 |
0.22 |
0.02 |
2 |
|
|
|
|
|
|
|
|
|
|
|
|
11 |
1 |
100 |
Table 6.
Calculating the consistency of pair-wise comparison (C.R = 0.05).
Table 6.
Calculating the consistency of pair-wise comparison (C.R = 0.05).
Factors |
(C1) |
(C2) |
(C3) |
(C4) |
(C5) |
(C6) |
(C7) |
(C8) |
(C9) |
(C10) |
(C11) |
(C1) |
0.18 |
0.30 |
0.30 |
0.22 |
0.32 |
0.24 |
0.17 |
0.14 |
0.12 |
0.11 |
0.09 |
(C2) |
0.09 |
0.15 |
0.15 |
0.34 |
0.21 |
0.24 |
0.17 |
0.07 |
0.12 |
0.14 |
0.11 |
(C3) |
0.09 |
0.15 |
0.15 |
0.23 |
0.21 |
0.24 |
0.17 |
0.14 |
0.12 |
0.11 |
0.13 |
(C4) |
0.09 |
0.05 |
0.07 |
0.11 |
0.21 |
0.16 |
0.17 |
0.14 |
0.12 |
0.11 |
0.11 |
(C5) |
0.06 |
0.08 |
0.07 |
0.06 |
0.11 |
0.16 |
0.17 |
0.22 |
0.12 |
0.11 |
0.08 |
(C6) |
0.06 |
0.05 |
0.04 |
0.06 |
0.05 |
0.08 |
0.11 |
0.14 |
0.12 |
0.11 |
0.11 |
(C7) |
0.06 |
0.05 |
0.04 |
0.04 |
0.04 |
0.04 |
0.06 |
0.07 |
0.02 |
0.08 |
0.09 |
(C8) |
0.09 |
0.15 |
0.07 |
0.06 |
0.04 |
0.04 |
0.06 |
0.07 |
0.02 |
0.08 |
0.09 |
(C9) |
0.06 |
0.04 |
0.03 |
0.04 |
0.03 |
0.03 |
0.03 |
0.04 |
0.04 |
0.06 |
0.06 |
(C10) |
0.05 |
0.03 |
0.03 |
0.03 |
0.03 |
0.02 |
0.02 |
0.02 |
0.02 |
0.03 |
0.04 |
(C11) |
0.05 |
0.03 |
0.02 |
0.02 |
0.03 |
0.02 |
0.01 |
0.02 |
0.01 |
0.01 |
0.02 |
2.6.2. Applications in GIS
2.6.2.1. Normalization of selected criteria
The datasets have distinct categorization units and need to be rasterized before they can be combined into a single measuring unit for further analysis. Moreover, the weighted criteria were divided into sub-classes and listed on a common preference scale from 1 (least liked) to 5 (most favored) [
35]. In order to normalize datasets, an integer value between 1 and 5 was assigned to each utilizing the reclassifying tool in the ArcGIS10.3 program. As a result, it is a helpful tool for spatial decision-making
(Table 7).
Table 7.
suitability score [
35].
Table 7.
suitability score [
35].
Score |
Suitability |
1 |
Unsuitable |
2 |
Less Suitable |
3 |
Suitable |
4 |
Moderately Suitable |
5 |
Highly Suitable |
However, solving landfill selection issues has been made easier with the incorporation of GIS and the AHP approach [
36].
2.6.2.2. Criteria restriction mapping
In order to create a binary mask layer with the values 0 and 1, all gathered restricted layers were merged using a raster calculator tool in the spatial analysis [
37]. As a result, a value of 0 for the unsuitable region and a value of 1 for the suitable area was assigned to the restricted and non-restricted areas, as illustrated in
Figure 4. The exclusionary regions for waste disposal sites that were not included in the suitability mapping are shown in
Table 8.
Table 8.
Exclusionary criteria for landfill sites of the study area.
Table 8.
Exclusionary criteria for landfill sites of the study area.
Criteria |
Parameters*
|
Suitability Score |
Ranks |
Area in Hectors |
Slope |
0-30% |
Suitable |
1 |
30240.7 |
>30% |
Unsuitable |
0 |
301.547 |
habitations |
0-3000 |
Unsuitable |
0 |
18824.8 |
>3000 |
Suitable |
1 |
11732 |
Water bodies |
0-250 |
Unsuitable |
0 |
1530.23 |
>250 |
Suitable |
1 |
29026.6 |
Airport |
0-4000 |
Unsuitable |
0 |
4226.44 |
>4000 |
Suitable |
1 |
26328.4 |
Existing dumpsites |
0-500 |
Unsuitable |
0 |
56.0313 |
>500 |
Suitable |
1 |
30500.8 |
Road |
0-300 |
Unsuitable |
0 |
7536.2 |
>300 |
Suitable |
1 |
23020.6 |
wells |
0-500 |
Unsuitable |
0 |
444.469 |
>500 |
Suitable |
1 |
30112.3 |
Figure 4.
Restriction buffer analysis used for (a) road, (b) Airport, (c) slope, (d) habitation, (e)river, (f) boreholes, and (g) dumpsite.
Figure 4.
Restriction buffer analysis used for (a) road, (b) Airport, (c) slope, (d) habitation, (e)river, (f) boreholes, and (g) dumpsite.
Table 9.
Description of criteria and sub-criteria of the input layer.
Table 9.
Description of criteria and sub-criteria of the input layer.
Criteria |
Sub-Criteria |
Ranking |
Area |
Level Suitability |
(hector) |
Percentage (%) |
Elevation |
< 1195 |
5 |
1728.94 |
5.65 |
Highly Suitable |
|
1195-1245 |
4 |
7152.09 |
23.40 |
Moderately Suitable |
|
1245-1295 |
3 |
10336.80 |
33.82 |
Suitable |
|
1295-1345 |
2 |
8537.31 |
27.93 |
Less Suitable |
|
>1345 |
1 |
2800.88 |
9.16 |
Unsuitable |
Distance from water bodies |
<250 |
1 |
1530.23 |
5.0 |
Unsuitable |
|
250-550 |
2 |
1369.28 |
4.48 |
Less Suitable |
|
550-750 |
3 |
1281.84 |
4.19 |
Suitable |
|
750-1000 |
4 |
1242.2 |
4.06 |
Moderately Suitable |
|
>1000 |
5 |
25133.2 |
82.25 |
Highly Suitable |
Distance from the built-up area |
<3000 |
1 |
18824.8 |
61.60 |
Unsuitable |
|
3000 - 4000 |
2 |
3735.34 |
12.22 |
Less Suitable |
|
4000 - 5000 |
3 |
2917.48 |
9.54 |
Suitable |
|
5000 - 6000 |
4 |
2185.16 |
7.15 |
Moderately Suitable |
|
>6000 |
5 |
2894.06 |
9.47 |
Highly Suitable |
Slope |
<20 |
5 |
29077.1 |
95.20 |
Highly Suitable |
|
21-30 |
4 |
1163.59 |
3.80 |
Moderately Suitable |
|
31-40 |
3 |
234.422 |
0.76 |
Suitable |
|
41-51 |
2 |
56.5 |
0.18 |
Less Suitable |
|
>51 |
1 |
10.625 |
0.03 |
Unsuitable |
LULC |
Water bodies |
1 |
6.09375 |
0.01 |
Unsuitable |
|
Built-up areas |
2 |
4412.47 |
14.44 |
Less Suitable |
|
Agricultural areas |
3 |
324.34 |
1.21 |
Suitable |
|
Shrubs |
4 |
5349.94 |
17.51 |
Moderately Suitable |
|
bare land |
5 |
20432.5 |
66.89 |
Highly Suitable |
Geology |
Auradu Limestone (Ea) |
1 |
273.797 |
0.89 |
Unsuitable |
|
Sands, silt, and gravels (Q) |
5 |
19672 |
64.37 |
Highly Suitable |
|
Yesomma Sandstones (Ky) |
3 |
10611 |
34.72 |
Suitable |
Distance from an Existing Dumping ground |
<500 |
1 |
157.03 |
0.51 |
Unsuitable |
|
500-1000 |
2 |
470.50 |
1.53 |
Less Suitable |
|
1000-1500 |
3 |
779.23 |
2.55 |
Suitable |
|
1500-2000 |
4 |
928.57 |
3.03 |
Moderately Suitable |
|
>2000 |
5 |
28221.5 |
92.35 |
Highly Suitable |
Distance from Main Road |
<750 |
5 |
14456 |
47.30 |
Highly Suitable |
|
750-1500 |
4 |
7884.61 |
25.80 |
Moderately Suitable |
|
1500-2250 |
3 |
4077.53 |
13.34 |
Suitable |
|
2250-3000 |
2 |
2096.19 |
6.85 |
Less Suitable |
|
>3000 |
1 |
2042.5 |
6.68 |
Unsuitable |
Distance from wells (GW protections) |
<500 |
1 |
444.46 |
1.45 |
Unsuitable |
|
500 – 1000 |
2 |
1099.91 |
3.59 |
Less Suitable |
|
1000 – 1500 |
3 |
1723.19 |
5.63 |
Suitable |
|
1500 – 4500 |
4 |
16640.1 |
54.45 |
Moderately Suitable |
|
>4500 |
5 |
10649.1 |
34.85 |
Highly Suitable |
Soil type |
Calcaric Camisoles |
3 |
26746 |
87.52 |
Suitable |
|
Chronic Cambisols |
3 |
107.047 |
0.35 |
Suitable |
|
Eutric Leptosols |
1 |
3703.78 |
12.12 |
Unsuitable |
Distance from Airport |
<4000 |
1 |
4229.7 |
32.31 |
Unsuitable |
|
4000-5000 |
2 |
2030.78 |
15.51 |
Less Suitable |
|
5000-6000 |
3 |
2080.31 |
15.89 |
Suitable |
|
6000-7000 |
4 |
2234 |
17.07 |
Moderately Suitable |
|
>7000 |
5 |
2511.34 |
19.19 |
Highly Suitable |
4. Conclusions
The weighting of all criteria was assessed using a developed analytical hierarchy process (AHP) with a consistency ratio of 0.05. Additionally, the model accurately reflected the degree of reality present in the research area, demonstrating the method's efficacy in locating and mapping landfill-prone locations. The study determined the viability of three potential landfill locations in the wasteland.
Furthermore, the land uses and land cover (LULC) criterion is the most significant one, with a relative weight of 0.1829, followed by habitations, with a relative weight of 0.1506. This was shown by the pair-wise comparison used to determine the priority between the criteria that were chosen. The research region is unsuitable for landfill siting in around 68.96% (21060.9 ha), but only 24.36% (7441.53 ha) had low appropriateness and about 6.68% had high suitability. In this instance, the government, local authorities, and city planners might refer to and follow the findings of this site selection analysis for subsequent development. Moreover, the approaches used in this study can make an important contribution to the scientific community working on the study and design of solid waste disposal in Somaliland and elsewhere.