J. Cent. South Univ. (2018) 25: 570-585

DOI: https://doi.org/10.1007/s11771-018-3762-3

Potential landfill site selection for solid waste disposal using GIS and multi-criteria decision analysis (MCDA)

S. Kapilan¹, K. Elangovan²

1. Department of Civil Engineering, Akshaya College of Engineering and Technology, Kinathukadavu, Coimbatore–642109, India;

2. Department of Civil Engineering, PSG College of Technology, Coimbatore–641004, India

Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Abstract: Proper solid waste disposal is an important socioeconomic concern for all developing countries. Municipalities have their own policies, individual approaches and methods to manage the solid wastes. They consider wastelands outside the urban area as the best suitable for the solid waste disposal. Such improper site selection will create morphological changes that lead to environmental hazards in the urban and its surrounding areas. In this research, the site selection for urban solid waste disposal in the Coimbatore district used geographical information system (GIS) and multi-criteria decision analysis (MCDA). Thematic layers of lineament density, landuse/landcover, population density, groundwater depth, drainage density, slope, soil texture, geology and geomorphology were considered as primary criteria and weights for criteria, and sub-criteria were assigned by MCDA analysis. The resultant weight score was validated by consistency ratio so that the efficiency of the selected criteria was justified. The overlay analysis in GIS environment provides 17 potential zones in Coimbatore district, among which, four suitable sites were screened and refined with the help of field investigation and visual interpretation of satellite image. The result of landfill suitability map shows the effectiveness of the proposed method.

Key words: municipal solid waste; landfill site; multi-criteria decision analysis; remote sensing; GIS; Coimbatore

Cite this article as: S. Kapilan, K. Elangovan. Potential landfill site selection for solid waste disposal using GIS and multi-criteria decision analysis (MCDA) [J]. Journal of Central South University, 2018, 25(3): 570–585. DOI: https://doi.org/10.1007/s11771-018-3762-3.

1 Introduction

The increasing population and rapid urbanization are of great concern to the municipal authorities for the management of solid wastes. The rapid growth of population demands the use of non-renewable resources and dumps its by-products such as toxic solid waste into the environment. Such type of solid wastes has resulted in deteriorated soil, air and water bodies, which causes serious health hazards for the public [1]. The disposal of generated solid wastes is formed once in a day in urban and semi-urban areas. However, rapid growth of cities with huge population creates a lack of basic infrastructure services for municipal solid waste (MSW) disposal [2]. The main reason for urban waste accumulation is somehow related with the living standard and well-being of the population through industrialization [3]. The management of solid waste disposal is still a complicated issue for the city planners, as it requires financial, environmental and political consideration [4]. Site selection for solid waste disposal is the primary task for solid waste management. Solid wastes in the urban areas mostly include wastes of plastics, glass, fabrics, and metals, which have complex composition and late degradable characteristics, creating more harm to the environment. The present waste management techniques rely on reuse, recycling, reduction and recovery of energy concept [5]. Nevertheless, elimination of all forms of waste is almost impossible and such waste can be handled by adopting suitable environmental friendly methods. One such suitable method is the landfill, although it is found at the bottom of waste management hierarchy and requires huge attention to reduce its environmental impact [1]. Due to its low cost of handling with respect to other forms of waste management techniques, the developing countries adopt landfill as a suitable method for solid waste disposal.

Enormous literatures on landfills are theoretically available for selection of solid waste landfill sites [5–11]. The site suitability for landfills as outlined in the literature requires complex and multidisciplinary approach, which includes ecological, social, technical, economic and environmental considerations. Moreover, settlement of solid wastes is based on the waste composition, density and moisture content [12], hence, geomorphic condition of landfill sites should satisfy these considerations. The management of such huge disaggregated data by traditional means is a tedious and time-consuming task and often results in poor output. Geographical information system (GIS) based decision analysis tools help in resolving this problem and suggests suitable sites for landfilling, waste segregation and recycling process [6, 13–17].

The decision-making is a kind of data mining process helps to solve day-to-day problems using the standard optimization techniques. There are several techniques available in literature, however, few of them are feasible for site selection applications. The multi criteria decision analysis (MCDA) is an analytical hierarchy process (AHP) which is developed by SAATY [18]. This technique provides a mean of decomposing the criteria into sub-criteria that can be hierarchically ranked on a numerical scale [19, 20]. The MCDA technique integrated with GIS provides qualitative and quantitative results compared to that of the simple weighted average techniques [5, 21–23].

The present study aims to identify specific sites for solid waste disposal in the Coimbatore district. Coimbatore municipal corporation is divided into five major zones covering 100 wards in its administrative boundary. In Coimbatore city, the major waste generation points are mostly the households, hotels, restaurants, industries, hospitals, market places, slums, bus-stands and community halls. The municipal corporation collects the solid wastes by street sweeping, door to door collection, collection from bins and open dumping and collection of drain silt. However, they execute the processing and the disposal of the solid wastes in a common way without consideration of socio- physiographic parameters such as settlements, groundwater and drainage network, agriculture and other environmental considerations. The socio- environmental factors considered in this work is based on the guidelines of Municipal Solid Waste (Management & handling) Rules, India (2000), Central Pollution Control Board, India (CPCB, 2003) and Central Public Health and Environmental Engineering Organization (CPHEEO, 2000).

2 Study area

The study area is located in the north-western part of the Tamil Nadu state with the elevation of about 410 m above mean sea level (MSL). The coverage of Coimbatore district is 4732 km², which includes 10 taluks and 295 villages. The district is surrounded by Nilgiri biosphere reserve forest in the North, Valparai reserve forest in the south, Western Ghats in the west and the eastern part is predominantly dry, bounded by Erode and Tiruppur districts. The location map of the study area is shown in Figure 1. The study area is the second largest city in Tamil Nadu and fastest growing tier-II cities in India. The district preserves salubrious climate with an average annual temperature of 26.3 °C. The average annual rainfall is 693 mm amongst northeast and southwest monsoon contributing to 47% and 28%, respectively. Besides, Coimbatore district is a major hub for textiles, manufacturing industries, education and commerce. Owing to these salient features, people are likely to settle in Coimbatore other than any districts of Tamil Nadu. The district has a population count of 3458045 among which 2618940 are residing in the urban area and 839105 are in the rural area (Census of India, 2011). The waste generated in the municipal corporation is 601 Mt/d, out of which 76% of the solid wastes are biodegradable that are primarily originated from the residences and markets.

Figure 1 Location map of proposed study area

3 Materials and methods

Methodology in this strategy includes thematic map generation, assigning weight factor, weighted overlay analysis by multi-criteria decision analysis and site prioritization. The schematic structure of methodological framework is presented in Figure 2. The detailed description of data collection and processing methods is described in the subsequent sections.

3.1 Thematic data

In this work, nine thematic layers namely, lineament density, landuse/landcover, population density, drainage density, groundwater table, slope, soil texture, geology and geomorphology have been used for site suitability analysis. Lineament feature in the present study area is extracted from the panchromatic image of Landsat 7 ETM+ data. Landsat data (path: 144 and row: 52, 53) acquired on 21 February, 2002 (cloud coverage less than 3.64) is used for lineament extraction. The images were geometrically corrected by radiometric correction algorithm [24]. Sobel filter with different orientation (N-S, NE-SW, E-W and NW-SE) is applied to the panchromatic images for extraction of linear features. These linear lines were assigned to binary values (1 for line, 0 for others) and imported into ArcGIS 10.3 platform. The line features were converted to vectors by raster to vector spatial analysis toolbox. The length of polyline features measured by geometry calculations and actual lineaments are identified through onscreen visual interpretation using Google earth. Further, line density tool is used to generate lineament density map of the present study region. Likewise, Landsat 8 (OLI/TIRS) multispectral data acquired on 20 March 2016 is used for landuse/ landcover (LULC) classification. Prominent features like agriculture, fallow land, scrub land, settlement, forest and water bodies were classified by support vector machine (SVM) algorithm with adjustable learning parameters [25]. The data of groundwater table for Coimbatore district are retrieved from annual report of Central Ground Water Board (2014–2015). The data were imported as a point feature in ArcGIS and the interpolation map was generated by using IDW spatial analysis tool.

Figure 2 Schematic structure of methodological framework used in present study

Village wise population data for 2011 is obtained from census of India website (http://censusindia.gov.in/). The data were used to create population density map in ArcGIS environment. The SRTM DEM with 30 m resolution data is used to extract drainage density present in the region. The DEM data are imported into River Tool software (version 4.0, http://rivix.com/) and extract drainage density features using drainage network module. Further, density features were imported into ArcGIS software and transformed to point features by spatial analyst toolbox. The points are converted into a raster map by IDW spatial interpolation technique. On the other hand, the DEM is used to extract slope features by ArcGIS spatial analysis tool. Soil features in the present study region is extracted from existing soil map provided by the agricultural engineering department (AED), Tamil Nadu, in the year 2012. Similarly, geology and geomorphology features are extracted from the district resource map published in 1995 by the Geological Survey of India, Hyderabad, India.

3.2  GIS-based multi criteria decision analysis (GIS–MCDA)

The GIS–multi criteria decision analysis is a decision making process that transforms the geographical data into the hierarchical network based on the importance of criteria and its sub-criteria. The methodology of the GIS–MCDA contains the following processes. At first, the most important parameters (criteria) are assigned for the problem. In the present case, population, groundwater table, landuse/landcover, slope of the area, drainage density, soil type, lineament density, geomorphology and geology are the crucial parameters for landfill site selection. The parameters are converted into thematic layers for decision-making using analytic hierarchy process (AHP) [18, 20]. The score for each criterion and its sub-criteria is fixed based on relative importance to each other as well as guidelines of existing research publications (Table 1) [26–33]. The rating of the scale is assigned on a 9 point continuous scale (9, 8, 7, 6, 5, 4, 3, 2, 1, 1/2, 1/3, 1/4, 1/5, 1/6, 1/7, 1/8, 1/9). If the factor value is on the left side of 1, comparison matrix constituted with actual value otherwise constituted with reciprocal value. The score 9 for the most important and 1 for equal importance whereas 1/9 represents the least important [34].

The normalization matrix method is used to calculate priority vector (weight) of each criterion and its sub-criteria. The value of each cell in the matrix is constituted by score value of the comparison matrix divided by the column total.

Table 1 Grading values for selected criteria

Continued

Further, principal eigenvector (weight) of each factor is calculated from the average value of row matrix. The consistency of the comparison matrix is evaluated by the consistency ratio (CR, R_c).The CR value for each matrix can be calculated as follows:

                                (1)

CI=                            (2)

where λ_max is the product between each element of the weight and the column total of the comparison matrix, n is the number of criteria or sub-criteria. The random consistency index (RI) is a constant parameter which is assigned on the basis of criteria and its sub-criteria we considered. Based on the reference of SAATY [18, 20], the RI value for factors is assigned as 1.41. In individual, the factors such as population density, groundwater table, landuse/landcover, slope, soil type, geomorphology and geology use 6 sub-criteria and therefore the RI value is 1.24. Conversely, RI values for drainage density (with 5 sub-criteria) and lineament density (with 3 sub-criteria) are 1.12 and 0.58, respectively. The derived weight values are assigned to each thematic layer and reclassified it by ArcGIS raster reclassification tool. Further, site suitability index (SSI, I_ss) is calculated by:

                       (3)

where subscript ‘w’ is the weight of the factor and ‘sw’ is the weight of the sub-factor. The letters of the decision criteria are P–population density, GWt– groundwater table, LULC–landuse/landcover, Sp– slope, D–drainage density, Sl–soil type, L– lineament density, Gmp–geomorphology and Geol– geology. The result of SSI raster image has different pixel values based on the weights of the criteria and sub-criteria. The raster data were reclassified into three classes: highly suitable, less suitable and not suitable using standard classification scheme (quantile classification method) [35].

4 Weight score for criteria and sub- criteria

4.1  Population density

The area covered by Coimbatore Municipal Corporation is almost occupied by households, hotels, restaurants, industries, hospitals, market places, slums, bus-stands and community halls. Landfilling sites within these areas might be harmful for the human beings and therefore less population density areas are mostly preferred. The population density data are categorized into minimum of 100 km^–2 and maximum of above 5000 km^–2. The score value for least population density is given most importance and high population density is given least importance. The weight and score value for each sub-criterion are shown in comparison matrix (Table 2). The table reveals that least weight of 0.04 falls on urban areas and the maximum weight of 0.41 falls on rural and forest areas. However, least population sites far away from the urban areas are considered as unsuitable due to the expensive waste transportation.

4.2  Groundwater table

The groundwater table is said to be the distance between the ground surface and the water table [33]. The depth of groundwater table provides an insight of contamination risk of groundwater in order to limit anthropogenic contamination. Groundwater data of Coimbatore district showing shallow water depth range from 1 to 5 m are observed at Karamadai, Sulur, Thnodamuthur and Pollachi. The area from Kinathukadavadu to Pollachi has water depth range no more than 10 m which delineates enrichment of groundwater source. Conversely, areas in south of Mettupalayam, east of Thondamuthur, east of Vellalur and southeast of Kinathukadavu have water depth of more than 30 m. The groundwater depth map is divided by five grades based on the guidelines of published research works [23, 30]. The shallow groundwater depth areas are the least considered zones for landfilling while deep groundwater depth areas are highly preferable for landfilling. The weight and score value for groundwater depth criteria is shown in Tables 1 and 2. It is revealed that least weight of 0.04 falls on shallow depth areas and maximum weight of 0.35 falls on high groundwater depth areas.

4.3  Landuse / landcover

The landuse/landcover (LULC) was prepared with five major classes namely agriculture, settlements, fallow land, scrubland and forest. Of the total area, forest covered 38.62%, followed by settlements 18.15%, agriculture 13.14%, fallow land 12.66%, scrubland 10.27% and water bodies 7.17%.The areas within forest, settlement and water bodies are considered inappropriate for landfilling sites and are therefore given less importance. The same is applied to agriculture land, as they are sensitive to ecological system. The fallow lands are uncultivated lands that are mostly not used to raise crop. The northeastern part of the study area is mostly of fallow lands due to considerable urban expansion. The scrublands composed of shrubs or grass dominated lands are unsuitable for human habitation due to the presence of natural disturbance such as dense plants and poisonous insects. The fallow and scrub lands are away from the human activities hence, they are considered as most suitable areas for landfilling sites. The weights for each sub-criterion in Table 2 shows least weight of 0.04 for water bodies and maximum weight of 0.31 for scrub land.

4.4  Slope

The stability of land surface is measured by the slope value present in the region. In the landfill suitability analysis, slope parameter plays a vital role to identify suitable sites with respect to the waste load into the surface. The steep slope with overload leads to landslide in the region. Moreover, vehicles consume more fuel for transportation. Therefore, flat or gentle sloping areas are more suitable for landfilling sites. The slope map is prepared with five categories (0–2°, 2.01°–5°, 5.01°–10°, 10.01°–20°, 20.01°–30° and >30°). The central and northeastern part of the study area has most of gentle slope surface whereas northwestern and southern parts of the study area are steep slope due to the presence of Western Ghats. The flat and gentle slope areas have secured high score values than others (Table 2).

Table 2 Pair wise comparison matrix, factor weights and consistency ratio of sub-criteria of factors

Continued

4.5  Drainage density

The drainage density data of present study reflect the close spacing of channels as well as formation of surface and subsurface present in the region. The drainage density is a key parameter to evaluate surface runoff present in the region. The lesser drainage density reveals less possible of surface runoff whereas higher drainage density is prone to higher runoff prevailed in the region. The obstacles present in the runoff path may lead to serious flood prevailed in the region. Therefore, the areas that are away from the potential drainage density are more suitable for landfilling sites. In the present case, lesser drainage density (<2 km/km²) secured higher weights (0.38) and higher drainage density (>5 km/km²) secured lesser weights (0.04) (Table 2).

4.6  Soil type

Solid wastes applied to the soils that have less permeability reduce surface erosion, runoff and improve the degradation rate [36]. The present study area has different soil types namely, clay loam, silt clay loam, coarse granule loam, coarse sandy loam, loamy sand and sandy clay loam. The soil group of coarse granule loam, coarse sandy loam and loamy sand have high permeability rate than others. The soil group of loamy sand has moderated infiltration rate whereas sandy clay loam, silt clay loam and clay loam have poor infiltration rate. It reveals that soil type with less infiltration (permeability) rate is more suitable for solid waste disposal. Table 2 shows the weights of the soil type sub-criteria. As seen, soil texture of clay loam obtains higher weights (0.41) and soil texture of coarse granule loam obtains lower weights (0.04).

4.7  Lineament density

Lineament density represents the intensity of the surface fractures present in a unit area [37–39]. The lineaments are linear features, which reflect secondary porosity and permeability of the rock structure based on folding, faulting and fracturing [40]. The lineament trends in the present study area are mostly of NW–SE, NE–SW and NEE–SWW directions. The area with less lineament density is more suitable for landfilling sites because rocks with infiltration rate act as a shield for ground water–solid waste contamination. The present study area is mostly of less lineament density (0–0.4 km/km²). The northwest and southern parts of the study area have moderate to high lineament density due to the hilly terrain present in the region. The weights of lineament density is shown in Table 2, which illustrates that the less lineament density attained weight factor of 0.57 and high lineament density attained weight factor of 0.09.

4.8  Geomorphology

The surface geomorphology reflects the topography of the landforms, drainage pattern and formation of surface sediments and their relation to the geological features. The present study is made up of structural and fluvial origin and has six major classes namely, structural hills, flood plain, colluvial fan, buried pediment, shallow weathered buried pediplain and moderately weathered buried pediplain. The structural hills present in the region are covered by hilly terrain with dense forest. The bajada and colluvial plain are present in the foothills with poor sorted sediments with bed. The flood plains are located parallel to the river flow and the region has a possibility of flood overflow due to runoff or heavy rainfall. Therefore, possible sites in these areas are not suitable for landfilling. The buried pediments are deposited by stream erosion and weathering and it has a gentle slope nature so that likely to be suitable for landfilling site. The shallow and moderate weathered buried pediplains are derived by continuous process of pedimentation and therefore it increases the waste degradation. The weight score for geomorphological parameters in Table 2 reveals that buried pediplain attained high weights (0.45) and structural hills (0.04) attained less weight for selection of landfilling sites.

4.9  Geology

The geology of the study area is underlain by consolidated hard rock formations of Archaean age and unconsolidated, porous formation. The rock types such as charnockite gneiss and pyroxene granulites, fluvial and garnet silliminate graphite gneiss has high infiltration rate hence, it is not suitable for landfilling process. On the other hand, granitoid and gneiss and magmaties have less infiltration rate and therefore less suitable for waste disposal. The area with basic anorthosite and granites has poor infiltration rate that is highly suitable for landfilling sites. Table 2 illustrates high weight for granites (0.47) and low weight for charnockite gneiss and pyroxene granulites and fluvial (0.05).

5 Results and discussion

5.1  Landfill suitability map

The suitable landfill site was delineated by integrating the thematic layers with suitable weights using GIS–MCDA techniques. The comparison matrix was developed for eight criteria and the weights were calculated for the factors by a sequence of multiplication. The acceptable consistency ratio (0.0452) is obtained from the pair wise matrix. The calculated weight of each factor is presented in Table 3. The weight results show that the population is the most important factor (0.261) and geology is the least important factor (0.0248). The weight factor of groundwater table (0.2022) is comparable with the population factor. The reason for the significance given to the population and groundwater table is that the disposal sites can cause explosion, air and water pollution that are harmful to the human beings. Landuse/landcover factor is considered as another important factor (0.1578) because landfilling sites should be away from the social criteria such as settlements, cultivated lands and hilly terrains. Followed by slope, drainage, soil, lineament and geomorphology factors obtain the weight scores. The weights for criteria and sub-criteria are integrated to the thematic layer and landfill suitability map is prepared by site suitable index (SSI) analysis in GIS environment. The resultant map has been categorized into highly suitable; less suitable and not-suitable (Figure 3). The suitable sites resulted in the site suitability index (SSI) map are mostly in the northeast and southern part of the Municipal Corporation. This is due to the presence of drainage and vegetation features in the west, southwest and northwest region. In particular, northwest part is surrounded by dense forest with hilly terrain surface that create complex road accessibility.

Table 3 Pair wise comparison matrix, factor weights and consistency ratio of factors

5.2  Post-processing analysis

The MCDA approach in conjunction with GIS based overlay analysis provides 17 suitable sites in Coimbatore district (Figure 3). The result was based upon criteria and sub-criteria, which were selected from existing maps and remote sensing techniques. However, it does not provide accessibility of potential zones in the present day condition. Therefore, subsequent screening and refinement was carried out to identify landfill suitable sites based on field investigation. The potential sites were reclassified and ranked based on road network availability and proximity to the Coimbatore Municipal Corporation (Table 4) as the distance between site and major road network is important in the case of waste transportation and landfill sites should be located at a place where it can be reached to urban areas.

Sites 9, 11, 12, 13 and 14 were found to be in close proximity to the Coimbatore Municipal Corporation where it is located at Kurumbapalayam, Vellalore (12 & 13), Onappalayam and Chettipalayam, respectively. They are very close to the national highway (NH-47 and NH-209) and these areas are comprised of uncultivated land and waste land. The coverage of landfilling area is high in Kurumbapalayam (2.5052 km²) while other sites have less than 1 km². These sites have secured rank 1 and are chosen as the most suitable landfill sites in Coimbatore district. Although sites 1, 6 7 and 17 are closer to the highway, they are located away from the municipal boundary and are comprised of village, partial cultivated lands and they are near to the river drainage. Sites 2, 3, 4, 8, 15 and 16 were located too far from the municipal boundary and highway road network. Therefore, they secured rank 3 and were chosen as the less suitable sites for solid waste disposal. In past decades, Coimbatore Municipal Corporation has used existing landfilling site, which is located at Vellalore (geocoordinate is 10°57'31.25"N and 77° 0'13.61"E). The local statistical report stated that around 5×10⁵ t of solid wastes were dumped at Vellalore over the span of a year. Several residents in and around the dump site seek to relocate the dump yard due to the increasing respiratory problems, skin rashes, irritation in the eyes, groundwater pollution and mosquito menace which is reported in the Hindu newspaper [41]. Field photographs in Figure 4 show the improper waste dumping as well as air pollution caused by burning of solid wastes at open air condition. The present result helps to select an alternative landfill sites and thereby to avoid socio-environmental problems.

5.3  Implications to proper solid waste disposal

Regions with developed resources and residence have more livelihood stability compared to that of the regions of less developed areas. The rapid increase in population density leads to encroachment of water bodies for their settlements and creates a polluted infrastructure. Additionally, landfill sites in this region cause more harm to the environment. Furthermore, solid wastes may contain broken glass, aerosol cans and potentially explosive chemicals or oils and therefore landfill sites should be away from the urban, village and industrial areas. Likewise, landfill sites near to the drainage path may cause blockages that result in surface water pollution, flooding and insanitary conditions. Therefore, by no doubt these regions are not suitable for solid waste disposal. In earthquake prone areas, it is necessary to examine the general trend of ground response prior to the selection of landfill sites [42, 43]. Based on the above analysis, the landfill sites should be a wasteland away from cultivated land, forest, settlements and hazardous zones. It is also suggested that prior to landfilling, the solid wastes need to be processed by the window compost or aerobic composting and refuse derived fuel (RDF) methods.

Figure 3 Landfill site suitability map of present study area

Table 4 Selected landfill sites and its physiographic parameters (Rank # 1: most suitable, # 2: moderately suitable, # 3: less suitable)

Figure 4 Field photos:

6 Conclusions

The abrupt increase in the accumulation of solid waste and its management is one of the greatest challenges faced by the municipal authorities of Coimbatore. The present research provides optimal solution to characterize landfill suitable sites in Coimbatore district using GIS based MCDA analysis. Unlike numerical models, GIS has the capability to store, analyze and display huge spatial and aspatial data. The pair-wise comparison matrix performs decision-making and assigns optimal weights to the criteria and sub- criteria. The consistency ratio of 0.0452 which is less than 0.10 endorsed the accuracy of the comparison matrix. The integrated overlay of thematic layers in GIS environment suggested 17 potential sites around the Coimbatore Municipal Corporation. Later, the results were refined by the post-processing techniques, in which four sites at Kurumbapalayam, Vellalore (2 sites), Onappalyam and Chettipalayam were found to be more suitable for landfilling sites in Coimbatore district. These areas are mostly wastelands that are near to the municipal corporation and away from the human dwelling places, thus eliminating the risk of social conflict. Geo-environmental parameters were considered as the key factors, which govern the site selection of potential landfill sites. The geo- processing of entire methodological framework for delineating the landfill site in a developing country like India by considering the key factors is a crucial step for planning and execution. Future research will include a public participatory campaign for assessing the performance and functioning of scientifically selected landfill sites. The present results will ultimately support the municipalities and government sectors to select the landfilling sites. The awareness on proper waste disposal is necessary for those who are generating the wastes in day-to-day life. Therefore, social welfare societies should take the initiative for awareness programme and provide training to people for proper solid waste management.

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(Edited by FANG Jing-hua)

中文导读

基于GIS和多准则决策分析的垃圾填埋场选址研究

摘要：妥善处置固体废物是所有发展中国家的一个重要的社会经济问题。通常市政有自己的策略和方法来管理固体废物，他们认为城市以外的荒地是最适合固体废物处置的地方，而不当的场地选择会造成形态变化，危害城市及其周围地区的环境。本文采用地理信息系统(GIS)和多准则决策分析(MCDA)技术对Coimbatore地区城市固体废物进行选址。主要标准和权重包括线性密度、土地利用/土地覆盖、人口密度、地下水埋深、河网密度、坡度、土壤质地、地质地貌，再采用MCDA分析方法对各子标准进行划分。通过一致性比验证所选标准的权重，从而证明所选标准的有效性。通过GIS环境下的叠加分析，选出Coimbatore地区的17个可能的区域，再通过野外调查和卫星图像的可视化筛选和细化出4个适宜的地点。填埋场适宜性地图验证了该方法的有效性。

关键词：城市固体废物；填埋场；多指标决策分析；遥感；地理信息系统；哥印拜陀市（印度）

Received date: 2016-11-11; Accepted date: 2017-03-23

Corresponding author: S. Kapilan, Assistant Professor; Tel: +91-7448571066; E-mail: skapilan85@gmail.com, mailkapilan@mail.com; ORCID: 0000-0002-2656-9700