MATERIALS AND METHODS

The study area stretches from 54° 10´ to 54° 45´ East and 36° 44´ to 36° 58´ North, comprising a region of approximately 1316Km² (Fig. 1). Themainland-cover types of the study area are dense broad-leaved forest, thinned out forest and pastures, needle-leaved woodlands, agriculture, water and residential areas. The economic growth in the area in the recent past has led to a large increase in population, driving dramatic urban expansion, land use change and increase in generation of solid wastes. Hence, for sustainable management of the solid waste produced, there is a need to locate suitable sites for future use. At present, there is a landfill site with an areaapproximately 11 ha in the west of city (Fig. 1). To meet a specific objective, it is frequently the case that several criteria will need to be evaluated. Such a procedure is called multi-criteria evaluation (Voogd, 1983 and Carver, 1991). Multi criteria evaluation (MCE) is most commonly achieved by one of three procedures (Eastman, 2001). The first involves Boolean overlay whereby all criteria are reduced to logical statements of suitability and then combined by means of one or more logical operators such as intersection and union. The second is known as weighted linear combination wherein continuous criteria (factors) are standardized to a common numeric range, and then combined by means of a weighted average. The result is a continuous mapping of suitability that may then be masked by one or more Boolean constraints to accommodate qualitative criteria, and finally thresholded to yield a final decision. According to Hopkins (1977) the most prevalent procedure for integrating multi-criteria evaluation and multi-objective evaluation (MOE) in GIS for land suitability analysis is using a weighted linear combination approach. The WLC procedure allows full tradeoff among all factors and offers much more flexibility than the Boolean approaches. The third option for multi-criteria evaluation, known as an ordered weighted average (OWA) (Eastman and Jiang, 1996). This method offers a complete spectrum of decision strategies along the primary dimensions of degree of tradeoff involved and degree of risk in the solution.

First step in the methodology consists of development of a digital GIS database that includes spatial information. Because of the different scales upon which criteria are measured, it is necessary that factors be standardized before combination. In this research, a linear scaling method is applied using the minimum and maximum values as scaling points for standardization (Eq. 1.):

where:
R= raw score,
R_min= Minimum score, and
R_max = Maximum score

It is provided the option of standardizing factors to a 0-255 byte scale. The primary issue in Multi-Criteria Evaluation is concerned with how to combine the information from several criteria to form a single index of evaluation. A criterion is some basis for a decision that can be measured and evaluated. Criteria can be of two kinds: factors and constraints. A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the activity under consideration. A constraint serves to limit the alternatives under consideration constraints classify the areas into two classes: unsuitable (value 0) or suitable (value 1).

With a weighted linear combination, factors are combined by applying a weight to each followed by a summation of the results to yield a suitability map (Eastman, 2001) (Eq. 2.):

S= _∑ W iX i ⁽²⁾

Suitability

W_i= Weight of factor i.
X_i= criterion score of factor i.

In presented method that Boolean constraints also apply, the procedure can be modified by multiplying the suitability calculated from the factors by the product of the constraints (Eq 3.)

(3)

S= ∑ W iX i* Π Cj

C_j= criterion score of constraint j
II = product

The result of this MCE will be final aggregated suitability image. In the final aggregated suitability image, zones whose area is smaller than 20 hectares are eliminated from the allocation process. In the next step, the method determines the land suitability of a zone by calculating the average of the suitability of the cells belonging to a zone (Equation 4.):

where:
S_z= Zonal land suitability
(L_i)_z= Local suitability of the cells i belonging to the zone z
n_z = Number of cells of zones z.

The results from the application of the presented methodology are zones for landfill with varying zonal land suitability. The zones are then ranked in descending order by the value of their zonal land suitability to facilitate the decision process. The evaluation criteria used in the present study are: Water permeability, depth of the underground water table, distance from rivers, distance from residential areas, and distance from roads, slope, and wind orientation.

Water permeability

This criterion classifies the whole area based on type of soil and geological characteristics. It has obtained geological mapof study area from National GeoDatabase of Iran (NGDIR) and the map of pedology (based on FAO’s method) from soil and Water Research Institute of Iran. In the present work, four zones were designated, as shown in Fig. 2. Soils with bad drainage (haplic and calcickastanozems, dystric cambisols, cambic podzols with karsts formations) were considered as undesirable areas for a landfill, having a grading value of 0. The medium permeability zone (haplic solonchaks, gleyic solonchaks, eutric and mollic gleysols, gleyic solonchaks) having a grading value of 85 (low suitability). The low permeability zone (calcaric andeutric cambisol, dystric cambisols, gleyic cambisols, humic cambisols) having a grading value of 170 (medium suitability). Finally, the very lowpermeabilityzone (calcic kastanozems, chromic luvisols, eutric cambisols, calcaric fluvisols, clayey soils and shale) were considered as optimal to site a landfill, having a grading value of 255 (High suitability).

Depth of the underground water table

This criterion classifies the whole area based on straight distance from groundwater level. These data obtained from Regional Water Organization of Golestan province. We suppose a linear monotoniacally increasing membership function for this criterion.Areas within 10 m distancefrom water table are considered unsuitable for allocating landfills (constraint). Land suitability increases linearly from 0 to 255, while the distance from water table increase from 10 to 50 m. Suitability remains at the highest value in areas located further than 50 m (factor) (Fig. 3a and b).

Distance from rivers

Fig. 4 a and b illustrates themonotonically increasing linear membership function for distance from rivers in the evaluation of landfill suitability of the area. For deriving the rivers layer, we used visual image (ETM+ sensor ofLandsat satellite, 30^th. July 2001) interpretation and on-screen digitizing to generate individual vector layers that were transformed into raster layers with 20 m resolution. Rivers and the area within 200 m from them are considered unsuitable for allocating landfills (constraint). Land suitability increases linearly from 0 to 255, while the distance from rivers increase from 200 to 1,000 meters. Suitability remains at the highest value in areas located further than 1,000 m.

Distance from residential areas

Another evaluation criterion is related to the distance from residential areas. Based on the study developed by Lober and Green (1994) and Lober (1995), it was considered that public opposition decays exponentially with increasing distances. Suitability, in turn, increases with decreasing public opposition. Landsat ETM+ scene of the Gorgan city covering around 1316 Km² were selected for this study. The scenes which dated 30^th. July 2001 were imported into Idrisi 32 software (Eastman, 2001), geo-registered to the other layers and re-sampled to 20 meters resolution. Then the scene was classified using knowledge from the area and Maximum Likelihood supervised classification method. Seven classes were identified: water, agriculture, fallow lands, built up areas, dense broad leaved forest, thin detailed land cover classifications into a binary urban / non urban map. Residential areas and areas within forest, pastures and needle leaved woodlands. The urban extent was derived from reclassification of these1,000 m of them are considered unsuitable for allocating landfills (constraint). Land suitability increases linearly from 0 to 255, while the distance from residential areas increase from 1000 to 10,000 meters. Suitability remainsat thehighest value in areas located further than 1,000 meters (factor) (Fig. 5 a and b).

Distance from roads

Fig. 6 a and b illustrates the criterion related to the distance from road network. For deriving the roads layer, we used visual image (ETM+ sensor of Landsat satellite, 30th. July 2001) interpretation and on-screen digitizing to generate individual vector layers that were transformed into raster layers with 20 m resolution. All roads and the areas within 100 m of them are considered unsuitable for the allocation of landfills. In this case, the suitability decreases as the distance from road network increases. This results in longer distances to be less suitable as transportation costs increase. Here, we supposed that the suitability decreases linearly with distance. Land suitability decreases from 255 to 0, while the distance from roads increases from 100 m to 1000 (factor). The areas located further than 1000 m from roads are considered unsuitable. The topographical features of study have been illustrated in Fig. 7b. It describes the constraint and factor functions for landfillsuitability evaluation associated with topography. We geo-registered and re-sampled a 10 meter DEM of the area obtained from National Cartographic Center of Iran to 20 meters resolution using Idrisi32 software (Eastman, 2001). Then, we derived the slope layer from the DEM layer. Areas sloped higher than 20% are considered unsuitable for allocating landfills (constraint). Land suitability decreases from 255 to 0, while the slope increases from 0% to 20% (factor) (Fig. 7a, b).

Wind orientation

Based on records from Meteorological Organization of Iran, the most frequently encountered winds in Gorgan city are the west, northwest and southwest winds. Because the study area is mainly flat, Wind orientation acts only as a constraint around Gorgan city where areas within 5000 meters of North, West and South are considered unsuitable for the allocation of landfills. The last step in the presented methodology is the application of the WLC method, shown in Equation (3). The evaluation criteria are in a raster GIS format with a 20m resolution and UTM_40n projection. In the context of criterion weights, a wide variety of techniques exist for the development of weights. The technique used here and implemented in Idrisi32 software (Eastman, 2001) is that of pairwise comparisons developed by Saaty (1977) in the context of a decision making process known as the Analytical Hierarchy Process (AHP). In the procedure for Multi-Criteria Evaluation using a weighted linear combination (WLC), it is necessary that the weights sum to one. In Saaty’s technique, weights of this nature can be derived by taking the principal eigenvector of a square reciprocal matrix of pairwise comparisons between the criteria. The comparisons concern the relative importance of the two criteria involved in determining suitabilityfor the stated objective. Ratings are provided on a 9-point continuous scale in three parts: less important, equally (1) and more important. Since the matrix is symmetrical, only the lower triangular half actually needs to be filled. The procedure then requires that the principal eigenvector of the pairwise comparison matrix be computed to produce a best fit set of weights. These weights will sum to one, as is required by the weighted linear combination procedure. Since the complete pairwise comparison matrix contains multiple paths by which the relative importance of criteria can be assessed, it is also possible to determine the degree of consistency that has been used in developing the ratings. Saaty (1977) indicates the procedure by which an index of consistency, known as a consistency ratio. The consistency ratio (CR) indicates the probability that the matrix ratings were randomlygenerated. Saaty indicates that matrices with CR ratings greater than 0.10 should be re-evaluated (Table 1).

RESULTS

The application of the presented methodology in the Gorgan city (Iran) indicated that there are 18 zones for landfill that their zonal land suitability varied from 155.426117 to 64.149024 (Table 1) and (Fig. 8). The research was conducted to evaluate the basic suitability of all available land for sanitary landfills as an aid in the selection of a limited number of sites for more detailed evaluation. It is important to emphasize at this stage that the actual availability of land for landfill could be significantly lower than the amount shown in the Table 2. A more complete land evaluation should consider other characteristics, such as more detailed analysis of current and future land uses, economic use and price of the land and so on. Because most of these criteria demand intensive field surveying, the process is implemented after small areas have been selected by general criteria. The analysis of the level of suitability of the zones selected along the allocation process shows the little available land suitable for landfill. This situation indicates that the areas to be used for landfill are going to become progressively less accessible. This has consequences on the costs of the waste disposal system, as well as on the risks for the environment and the community. Low suitability results, for example, from the use of areas whose soil presents low suitability, or areas near the rivers.