LANDFILL SITING USING GIS AND FUZZY LOGIC

A. Karkazi*, T. Hatzichristos**, A. Mavropoulos*, B. Emmanouilidou*, Ahmed Elseoud***

*EPEM S.A. Department of Solid and Hazardous Wastes, Greece**Dept. of Geography, National Technical University of Athens, Greece***Egyptian Environmental Affairs Agency

SUMMARY: The construction of landfills is a no alternative option, since a landfill is always necessary independently of the specific waste management system that will be developed. The criteria that must be met to allocate a landfill are various and in many circumstances conflicting. For that reason the result is not univocal, it depends on the criteria and the methodology used together with its restrictions. The suggested methodology utilizes GIS technology for the input, the management and the visualization of the geographic data while fuzzy logic is used for the analysis of the data and the evaluation of the final results. The basic elements of the fuzzy logic methodology as well as its potential in the specific problem are described. A case study took place in one Governorate in Egypt, one of the twenty-seven country administrative units. The results drawn up by fuzzy logic are compared with that of the classical Boolean approach of data analysis.

1. INTRODUCTION

The site selection process is usually one of the most critical steps in the entire decision making cycle of waste management. The direct public involvement, the economic impact in the surroundings of a landfill and the need for combination of technical, social and legislative issues are some typical factors that increase the difficulties for a successful site selection. Recycling, composting, and incineration projects have been implemented as methods of minimizing the use of land disposal of wastes. However, even practices that take advantage of material and energy recovery generate residues that must be disposed on the land.

In many developed countries the site selection process could last five years or more depending on the specific local circumstances. Especially when the site selection is correlated with the design criteria of the facility the process can take up to ten years due to the detailed geological and hydrogeological studies that have to be completed before the final decision. In the case of a large facility with remarkable environmental impacts, a site selection process may cost hundreds thousands dollars. On the other hand, a successful site selection process may reduce the capital and operational cost of a landfill affecting the design of some expensive parts like liners, biogas collection and management systems, leachate collection and management systems and monitoring details (ISWA, 1998

Numerous factors have to be evaluated in order to place a landfill. An adequate landfill should have minimum environmental impacts and social acceptance. Besides, an adequate landfill should be in accordance with the respective regulations (Zyma 1990). A site selection process usually proceeds with an approach of phases. It begins with the use of regional screening techniques to reduce the examined area to a manageable number of discrete search areas. Screening is based on exclusion criteria that have to be defined. Because of this screening, the resulted areas have higher probabilities to contain suitable sites. After the initial screening, the discrete areas have to be evaluated in more detail and the candidate sites will be identified.

Finally, a detailed evaluation of the candidate sites should be implemented, based on a site specific level of analysis and the most suitable site will be selected. The overall site selection process is thus one of increasingly intensive analysis of progressively smaller areas (Buckingham P.L.,1981). It is obvious that the phased approach methodology is widely used forthe inceptive site selection, because of its simplicity and the economy of time and money that provides. Besides that, in most of the cases, especially in the developing countries, the lack of the appropriate data and the requirement for a rapid site selection lead to directions where phased approach is the best available solution.

Up to date several GIS methodologies have been used for the selection of a landfill, as GIS provides the decision maker with a powerful set of tools for the manipulation and analysis of spatial information. Using a Geographic Information System (GIS), it is possible to process a huge amount of spatial data in short time and so the screening is much easier. GIS can help to reduce remarkably the areas that have to be examined on site, although the final decision has to be taken after field studies However, the application of a GIS methodology requires geographic data and software. Therefore, the use of the GIS methodologies is more convenient in large-scale analyses (national level) where one can benefit from the economy of scale.

Several methodologies have been applied up to date for sitting a landfill in combination with GIS, such as, expert systems, raster-based C programs with optimal compactness, multicriteria analysis (Jehng-Jung K. et al., 1996, Muhammad Z. et al., 1996, Hung-Yueh L. et al., 1999 ). Although the data that are used aim to secure high environmental and social standards the results that come up from the above methodologies lead to strict solutions. This is something that does not reflect the reality, as it is impossible to define with 100% certainty the environmental or social criteria used to delineate the boundaries of a candidate site. To overcome these drawbacks, this paper presents a method for the siting of landfills utilizing the tools of modern technology and more specifically fuzzy logic. GIS is necessary, given the fact that offers a powerful set of tools for the input, the maintenance and the presentation of the data, while the use of fuzzy logic is based on the need for appropriately treating environmental phenomena, which are not exact or precise but rather fuzzy. Along these lines it is suggested that GIS can anthropomorphize their analytical abilities through the incorporation of fuzzy logic.

. 2. SUGGESTED METHODOLOGY

2.1 Using GIS for the selection of landfills

The idea of GIS as a box of tools for handling geographical data is useful. Like most toolboxes, however, the list of tools provided by GIS although impressive is not complete. For example in most GIS packages spatial analytical functionality, lies mainly in the ability to perform deterministic overlay and buffer functions (Carver J.S. 1991). Such abilities whilst ideal for performing spatial searches based on nominally mapped criteria, are of limited use when multiple criteria and targets, such as in the case of landfills selection, are applied. The integration of GIS with analytical techniques will be a valuable addition in GIS toolbox. As Fotheringham

and Rogerson (1994) note “progress in this area is inevitable and future developments will continue to place increasing emphasis upon the analytical capabilities of GIS”.

The fuzzy approach is most suited to applications where decision criteria are not rigid, where the boundary between two regions is gradual. Inexact boundaries or class overlap appear to be more the rule than the exception in geographical problems (Openshaw S., 1997).

2.2. The fuzzy logic approach

Classic Boolean logic is binary, that is a certain element is true or false, an object belongs to a set or it doesn’t. Fuzzy logic, introduced by Zadeh in 1965 permits the notion of nuance. Apart from being true, a proposition may be anything from almost true to hardly true (Kosko B, 1991). In comparison with the Boolean sets, a fuzzy set does not have sharply defined boundaries. The notion of a fuzzy set provides a convenient way of dealing with problems in which the source of imprecision is the absence of sharply defined criteria of class membership rather than the presence of random variables.

As mentioned, a significant fact about statistical logic is the defect that each point of a set U is unequivocally grouped with other members of its group and thus bears no similarity to members of other groups. One way to characterize an individual point’s similarity to all the groups is to represent the similarity a point shares with each group with a function (termed the membership function) whose values (called memberships) are between 0 < m < 1. Each point will have a membership in every group, memberships close to unity signify a high degree of similarity between the point and a group while memberships close to zero imply little similarity between the point and that group. Additionally the sum of the memberships for each point must be unity.

The complement of A is NOT A . Although in Boolean logic A and not A are unique, in fuzzy logic the following equation is true:

.

Fuzzy degrees are not the same as probability percentages. Probabilities measure whether something will occur or not. Fuzziness measures the degree to which something occurs or some condition exists. Crisp sets are a subset to fuzzy sets. Only when an object belongs 100% to a group fuzzy sets are identical to crisp sets.

In order to solve a problem with a knowledge-based fuzzy system it is necessary to describe and process the influencing factors in Fuzzy terms and provide the result of this processing in a usable form. The basic elements of a knowledge-based Fuzzy system are:

1. Fuzzification2. Knowledge base

3. Processing4. Defuzzification

These elements are described in detail, in the following paragraphs.Several types of membership functions can be utilized (Burrough, 1996). The membership

function reflects the knowledge for the specific object or event. Every continuous math function can be approximated by a fuzzy set. For example the criterion distance from a road” can be approximated from the membership function in Figure 1.

D is ta n c e (m )

M e m b e r s h ip s

S m a l l G re a t

“Figure 1. Membership function for the “distance from a road”

1. The assignment of a membership function to every variable of the problem is called fuzzification process. During this process crisp subsets are transformed to linguistic subsets such as small or great distance (Fig. 1). The concept of the linguistic variable illustrates particularly clearly how fuzzy sets can form the bridge between linguistic expression and numerical information.

2. The second step in the Fuzzy systems methodological approach is the definition of the rules which connect the input with the output. These rules are based on the form “if …then and”. The knowledge in a problem-solving area can be represented by a number of rules. The task of rules definition is usually accomplished by experts with general knowledge on the specific field. There is no need for assigning weights in the criteria used. The weights are indirectly taken in account through the rules defined. For example, if the output set “suitability: is comprised by two subsets called :poor” and “appropriate”, the rules could be:

If the distance is small then suitability is poorIf the distance is great then suitability is appropriate

3. The next step is the processing of the rules. This step is also called inference. It comprises of the three stages, aggregation, implication and accumulation. Aggregation provides the degree of fulfillment for the entire rule concerned. All the Boolean algebra operations (like intersection, union, negation, etc) can be easily extended to fuzzy set operations (Bezdek, 1981) and they can be used in this stage. In implication the degree of fulfillment of the conclusion is determined. Accumulation brings together the individual results of the variables used details for this process can be found in Bezdek (1981).

4. The result of rules processing can be transformed back into a linguistic expression or a crisp value. This second process is called defuzzification and there are several methods to achieve it (Bezdek C.J., 1981).e.g. fuzzy results: 73% poor suitability, 37% appropriate suitability defuzzified: poor suitability.

3. CASE STUDY

The case study presented here, is based on the project “Action plan for the site location and the development of design operation & environmental impact assessment methods of solid waste sanitary landfills in Egypt Governorates” granted by the European Union within the framework of Life Third Countries Program. According the priorities of Egyptian strategic plan regarding the environment, the construction of at least one large landfill to each Governorate is a primary goal among others, for the next five years. The implementation of this goal will provide an economically feasible, environmentally sound and technically appropriate solution for the waste disposal. The study area was Cairo, one of the twenty-seven country administrative units, and has been used as a paradigm for this paper. The appropriate geographic layers (scale 1: 250.000) related to environmental criteria were utilized. A major task was the utilisation of the available data with all the restrictions that this includes, since in developing countries like Egypt the gathering and the availability of data is the difficult part of any project. The available geographic information which was originally stored in vector format was converted to raster format, in order to apply the proposed approach. The pixel size decided upon was 100 x 100 meters. The hardware platform was a PC with a Pentium III processor with an HP Design jet Plotter. As for the software, the packages MSOFFICE, ARC/INFO 7.2.1, ARCVIEW3.2, DATA ENGINE 2.0, in a WIN98 environment, were used.

3.1 Fuzzy logic analysis

The geographic layers used corresponding to environmental criteria are the following:

1. Primary roads network2. Secondary roads network3. Faults4. Ports – Airports5. Streams6. Canals

7. Nile 8. Protected areas9. Urban areas10. Agricultural land11. Stream valley12. Geology-Hydrogeology

The membership function for every criterion, as well as their linguistic expression, based on the experts knowledge, are illustrated in Table 1

Table 1 – Criteria used – linguistic expressions – membership functions

Criterion Linguistic expression

Membership function

Primary roads network

Distance: Short(x), Long(x)

Secondary roads network

Distance: Short(x), Long(x)

FaultsDistance: Short(x), Long(x)

Airports – PortsDistance: Short(x), Long(x)

Streams Distance:

Short(x), Long(x)

Stream ValleysDistance: Short(x), Long(x)

CanalsDistance: Short(x), Long(x)

NileDistance: Short(x), Long(x)

Protected areasDistance: Short(x), Long(x)

Urban areasDistance: Short(x), Long(x)

Agricultural landAgri. landLow(x), High(x)

Geology- Hydrogeology

Geo-HydroLow(x), High(x)

Figures 2 and 3 illustrate in graphics format two of the previous membership functions that have been developed.

Figure 2. Membership function of the criterion “Airports-Ports”

Figure 3. Membership function of the criterion “Nile”

In order to define completely the fuzzy system at this stage, another variable must be set. This variable concerns the fuzzy output. The output variable is the suitability of sites, comprised of two subsets, poor suitability and appropriate suitability. According to this, at the end of the entire process, each site will be assigned with a value, from 0 to 100, for the two suitability subsets, poor and appropriate.

For this reason, it is necessary to define the rules that connect the input values of the criteria through their membership functions with the output subclasses. Some of the rules used to determine the suitability of the sites for the establishment of a landfill are the following. 1. If distances from Primary road network and Secondary road network are Long and distances

from Protected areas and Urban areas are Long and Agricultural land is Low Then the suitability is Appropriate with 80% certainty

2. If distances from Protected areas and Urban areas are Long and Agricultural land is Low and Geology-Hydrogeology is High Then the suitability is Appropriate with 85% certainty

3. If distances from Primary road network and Secondary road network are Long and distances from Faults and Streams and Stream Valleys are Long Then the suitability is Appropriate with 45% certainty

With the same structure a set of twenty rules have formulated. The next stage, inference, or processing of the rules, was carried out by using the following

operators: Minimum for Aggregation, Algebraic Product for Implication and Maximum for Accumulation.

The results of high suitability class are presented in map 1. Pixels with membership values close to unity signify areas with high suitability, while pixels with values close to zero imply areas with lower suitability.

Map 1. Fuzzy logic analysis results

Table 2 presents the number of pixels of the areas with the highest membership value.

X

Table 2- Membership values of high suitability

Values Number of pixels*0-74 1106875-84 1286685-95 31407

3.2 Boolean logic analysis

Since no similar effort has been implemented in Egypt and by taking into account that performance standards are not available for the landfills in Egypt, within the framework of the project the site selection has been accomplished by following a methodology of four steps:Step 1: Development of exclusion criteria Step 2: Delineation of exclusion and inclusion zonesStep 3: Development of inclusion criteriaStep 4: Further elaboration for specific sites that came up The results of the application of the above methodology are shown in Map 2. The suitable sites are shown with black color.

Map 2. Boolean logic analysis results

4. COMPARISON OF ANALYSIS METHODS

The results from the fuzzy analysis show a gradual suitability for a landfill site. The results from the Boolean analysis, instead, show the distinction between “yes” or “no” areas. Besides, the Boolean method cannot give us information for the selected areas. With the Boolean model 83,5% of the area is considered unsuitable and is discarded without no further consideration, while with the fuzzy model the area that could be taken under consideration and is of high suitability (value greater than 80%) is 40% of the area drawn up by fuzzy logic. This is a crucial

X

point as the first screening is to exclude the areas that need protection while at the same time make a hierarchical list of the candidate areas. We can also compare the results of the Boolean and the fuzzy analysis, for a specific site proposed by field investigation. It is marked with “X’’on both Maps 1 and 2. The site on the Boolean map has an area of 5,66 km2, while in fuzzy map, with membership values greater than 0.80 has an area of 10km2.

Flexibility is another characteristic of fuzzy logic. By using a fuzzy map, decision-makers, according the strictness of the following policy each time, regarding the socio-economics and environmental aspects, may first select areas with a high suitability membership value and then proceed to field investigations. If for any reason the site deemed inappropriate they can proceed either to another site with the same membership value or to another membership value of lower suitability. The benefit is that they don’t need to conduct a new analysis, or change the rules, or the criteria, saving time and effort.

A number of criteria, besides the ones that have been applied, such as: the required area for the establishment of a landfill, the distance from the waste generation, the road appropriateness, the land use, the land property status etc., could be considered and lead to the selection of the optimum sites. Of course this selection should not be considered as the final one as a number of parameters should be examined in the field. The selection of the most suitable site should be done after an examination among the optimum sites.

5. CONCLUSIONS

Concluding, we can say that the performance of conventional approaches of landfills siting based on environmental characteristics are poor due to their fuzziness. On the contrary, fuzzy logic is treating appropriately these phenomena and has several advantages over its counterparts as we demonstrated in this paper, such as realism through the use of linguistic variables, hierarchical ranking for the total geographic space and fewer repetitions of the model for the selection of the optimum area. This does not mean that there are not disadvantages. The main of them is: a) The lack of ready to use membership functions b) The cost for operating a fuzzy system. c) Traditional techniques have existed for a long period and are widely accepted.

The real challenge is the need to develop suitable methods for the siting of landfills, close to human thinking such as fuzzy logic, which can cope with the nature of environmental and socio-economic data. In addition the task of evaluation and development of appropriate methods of spatial analysis, as fuzzy logic, is very important in a period of geographic data availability, which in many cases is imprecise. As this method becomes more visible, landfills siting procedure will be much easier.

REFERENCES

Bezdek C. J. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum PressBurrough P. (1996) Natural Objects with Indeterminate Boundaries, In Geographic Objects with Indeterminated Boundaries ed. by Burrough and Frank A., Taylor & Francis, pp. 3-29Buckingham P.L.(1981) Regional Screening Approaches to selecting sites for New Secure

Landfills. Proceedings of Fourth Annual Madison Conference of Applied Research & Practice in Municipal & Industrial Waste, University of Wisconsin, Wisconsin

Carver J. S. (1992) Integrating Multi-Criteria Evaluation with GIS, International Journal of GIS, Vol. 5, N. 3, pp. 321-339Fotheringham S, Rogerson P. (1994) Spatial Analysis and GIS, Taylor & FrancisHung –Yueh Lin, Jehng-Jung Kao (1999). Enhanced spatial model for landfill siting analysis.

Journal of environmental engineering, 1999Jehng-Jung Kao (1996). A raster –based C program for siting a landfill with optimal

compactness. Computers & Geosciences Vol.22, No 8. pp 837-874

Jehng-Jung Kao, Hung –Yueh Lin (1996). Multifactor spatial analysis for landfill sitting. Journal of environmental engineering, 1996

Jehng-Jung Kao, Wie-Yea Chen, Hung –Yueh Lin, Show-Jyi Guo (1996). Network expert Geographic Information Systems for landfill siting. Journal of environmental engineering, 1996

ISWA, (1998). Guidance for landfilling waste in economically developing countries.

Krerkpong C., Oiming Z, Barry G. (1996). Preliminary landfill site screening using fuzzy Geographic Information Systems. Waste Management & Research (1997) 15, 197-215

Kosko B. (1996) Fuzzy Thinking, Flamingο Press, New York

Muhammad Z. Siddiqui, Jess W. Everett, Baxter E. Vieux (1996). Landfill siting using Geographic Information Systems: A demonstration. Journal of environmental engineering, 1996

Openshaw S (1997) Artificial Intelligence in Geography, Wiley, LondonZyma, R (1990) “Siting considerations for resource recovery facilities”Zadeh L.A. (1965) Fuzzy Sets, Information and Control, 8, pp. 338-353