1 Introduction

Urbanization, population growth, expansion of the economy, and movement of people from rural to urban have presented significant challenges in handling solid waste in municipalities [40, 58, 74, 78, 85]. This soil waste management issue affects worldwide emerging and wealthy nations [29, 38, 63, 83]. The process involves various stages such as creation, segregation, storage, collection, processing, transfer, and disposal of municipal solid waste (MSW), considering environmental, aesthetic, engineering, economic, public health, and social factors [14, 70]. Poor management of MSW presents significant threats to both people as well as the environment. These hazards include the spread of diseases, increased risk of fires, formation of unpleasant odours, contamination of air and water, disruption of aesthetics, and economic losses [45, 64]. Consequently, long-term solutions are urgently needed, necessitating effective management structures to minimize environmental impacts despite complexities and subjectivity [34, 43]. Implementing strategic measures can alleviate the financial burden of technical and administrative expenses and mitigate potential environmental risks associated with efficient waste management [35, 6]. India, for example, generates 1.50 lakh metric tonnes (MT) of solid waste per day, projected to double to 300 MT by 2047 [21, 33, 69]. Many Indian towns lack recycling infrastructure, leading to increased landfill usage. Despite efforts to reduce and recycle waste, smart waste management remains inadequate in most Indian cities. Scientific landfill site selection in urban areas is crucial because uncontrolled garbage burning emits hazardous gases [50]. To minimize costs associated with liner installation and prevent soil contamination, designers can leverage the soil's minimal permeability coefficient [51]. The waste management hierarchy prioritizes source reduction, recycling, composting, energy recovery through burning, and disposal [26].

The potential of incineration as a waste management solution is a subject of ongoing investigation. Nevertheless, it is crucial to recognise the environmental hazards linked to incineration, including the contamination of air and groundwater. Despite numerous studies exploring alternative waste management approaches, landfilling remains a crucial aspect of solid waste management [53]. Various methods exist for the final disposal of MSW, including open dumping, burning, composting, and incineration. Nevertheless, landfilling is widely recognized as a predominant and viable approach for waste disposal. Effective decision-making regarding the siting of MSW landfills necessitates the utilization of GIS and MCDM techniques [19, 39]. Over the past two decades, significant research has been conducted in the field of solid waste management. Kumar and Hossain [46] employed the Spatial Analyst tool and AHP model in their analysis, which are valuable for making multi-criteria decisions. Saatsaz et al. [66] assessed landfill site suitability using the AHP method and suggested its applicability in reviewing the viability of decommissioned reservoirs housing potentially hazardous liquids. Kamdar et al. [39] utilized a combination of GIS and AHP to analyze optimal landfill placement, potentially influencing waste management regulations. The complexity of considering various factors underscores the utility of AHP in decision-making related to landfill siting [47]. This methodology facilitates the systematic breakdown of complex issues for analysis, addressing the limitations of previous studies. The present study aims to overcome these limitations by employing the MCDM technique within a GIS framework for landfill site selection in Balurghat Municipality. AHP is favoured for its simplicity, consistency, and effectiveness in pairwise comparative assessment, making it a valuable tool for decision-makers. Existing open dump sites in the study area have been associated with adverse effects on nearby residents, underscoring the need for scientific approaches like AHP and GIS in identifying suitable landfill sites.

The utilization of MCDM techniques has witnessed a notable surge in addressing real-world challenges in recent years. MCDM approaches offer a structured framework to aid decision-makers in selecting the most optimal and satisfactory alternative amidst uncertain circumstances [10]. Extensive studies have identified prominent MCDM methodologies such as AHP, Analytic Network Process (ANP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), among others, as prevalent tools in this context [11, 20]. Presently, these methodologies are pivotal in the process of identifying suitable landfill sites. Eminent academicians and scholars, including Saaty, Pears, Khodabakhshi, and others, have extensively utilized decision-making methodologies in their research, highlighting both the merits and limitations of these approaches [41]. MCDM techniques offer a straightforward application as collaborative decision-making tools, enabling the involvement of diverse experts from various fields [84].

Additionally, MCDM possesses the capability to address ambiguity and uncertainty inherent in expert opinions and real-world parameter variations, rendering it highly suitable for decision-making tasks [67]. TOPSIS methodology presents a user-friendly approach that is easily implementable, particularly aided by the abundance of software tools facilitating automated computations. This accessibility extends its utility to a diverse array of stakeholders engaged in landfill site selection. However, it is imperative to acknowledge that TOPSIS operates under the assumption of complete certainty regarding all criteria and their independence from each other. Such idealized conditions may not consistently align with the complexities of real-world scenarios. Notably, while TOPSIS enjoys widespread acceptance for large-scale applications, its suitability for smaller geographical areas has been scrutinized, as evidenced by studies such as those conducted by [57, 80]. PROMETHEE offers visual representations of alternative rankings, such as radar charts or histograms, facilitating the interpretation and communication of results to stakeholders, thereby aiding in transparency and consensus building. However, implementing PROMETHEE can pose challenges, particularly for decision-makers unfamiliar with the method. It demands expertise in multicriteria decision analysis (MCDA) and may entail a steep learning curve for novices [22]. On the other hand, while ANP techniques offer notable advantages for assessing landfill site suitability, including comprehensive evaluation and handling complex relationships, they also present challenges concerning data requirements, subjectivity, complexity, implementation, and interpretation [2, 28].

In the 1980s, Saaty introduced the AHP, which has since become one of the most widely utilized methods for multi-attribute decision-making [66]. The benefits of employing AHP and WOA for landfill site selection in Balurghat Municipality are that AHP stands out as an efficient technique for addressing decision problems across various domains [5]. Additionally, AHP offers a reliable approach for assessing the consistency of decision-maker evaluations, thereby mitigating bias in the decision-making process [8, 72]. Still in contemporary times it may be a debacle if the practice is that human decision is not behind the system rather decision by artificial intelligence (AI) especially for realistic and delicate issue of such type. Moreover, the spatial functionalities inherent in ArcGIS empower decision-makers to visually assess the effects of different factors on the urban environment, facilitating improved comprehension and communication of outcomes to stakeholders. Through the integration of AHP with GIS technology, the process of data amalgamation and analysis becomes more efficient, leading to expedited decision-making and accepting the decision as cost-effective [49, 59, 60, 79].

The objective of this study is to evaluate the feasibility of potential landfill sites in Balurghat Municipality, adhering to scientific and environmental standards. Criteria selection, informed by expert opinions and prior research, aims to assign relative importance to factors influencing landfill site suitability. Utilizing resulting maps for spatial analysis enables the identification of natural and anthropogenic factors impacting the environment. MSW management poses significant challenges in Balurghat, including air pollution, soil and water contamination, and climate change risks. Addressing these challenges requires careful consideration of morphological and economic factors to optimize waste disposal strategies while minimizing environmental and public health risks [3]. Thus, a comprehensive assessment of landfill locations is imperative for sustainable waste management in Balurghat Municipality.

2 Materials and methods

2.1 Study area

The Balurghat municipality, situated in the Dakshin Dinajpur district, is acknowledged as one of the swiftly progressing municipalities in the area. Positioned strategically along the banks of the river Atreyee, its geographic boundaries extend from a longitude of 88° 45′ E to 88° 47′ E and from a latitude of 25° 12′ N to 25° 15′ N. characterized by a flat topography with an average elevation of 25 m and the maximum temperature in the Balurghat municipality area reaches 35.9 °C, while the minimum temperature drops to 7.1 °C during winter, with an annual average temperature of 24.9 °C. Additionally, this region experiences an annual average rainfall of 1807.9 mm [24]. To address the issue of solid waste disposal, a 5 km buffer zone has been established to encompass urban areas and nearby villages, resulting in a total research area of 50.25 km2, with the Balurghat municipality covering 12.35 km2. Balurghat Town attained municipality status in 1951 and it comprises 25 wards (DCHB, 2011), with an estimated population of approximately 210,000 individuals as of 2023. Within this population, the Balurghat municipality generates 75.11 tonnes of solid waste per day, as per one municipality official’s statement during an interview in 2023. The economy of this region predominantly relies on agriculture, trade, and commerce. This region has significant geographic advantages owing to its accessibility via rail and road transportation, including National Highway 512. The open dumping scenario in Balurghat municipality is a significant environmental concern. Waste is disposed of in open areas without proper containment or treatment, leading to pollution of air, soil, and water sources (Fig. 1). This unregulated dumping poses health risks to residents and contributes to the degradation of local ecosystems. The concern of solid waste management in Balurghat municipality revolves around effective collection, transportation, treatment, and disposal of waste to ensure environmental sustainability and public health. Key challenges may include inadequate infrastructure, lack of segregation of wastes broadly into biogenic and non-biogenic sources, limited public awareness, and insufficient funding for sustainable waste management practices. Addressing these issues requires collaborative efforts among local authorities, community participation, and the implementation of innovative strategies for waste reduction, like waste level sensors and the use of smart waste beans and recycling. So urgent measures are needed to implement effective waste management strategies, such as recycling programs and sanitary landfill sites, to mitigate the detrimental impacts of open dumping on the community and the environment.

Fig. 1
figure 1

Location map of the study area: a Location of West Bengal in the map of India, b Location of Dakshin Dinajpur District in the map of West Bengal c Balurghat Municipality along with a 5 km buffer area, d, f Open dumping scenario in Balurghat Municipality area, e Existing landfill site in Balurghat Municipality

Full size image

2.2 Data base

The process of selecting suitable landfill sites involved gathering data from various reliable sources to identify influencing factors. Geological data sourced from the Geological Survey of India (GSI) was transformed into a lithological formation map, while groundwater level statistics were obtained from the India-Water Resource Information System (WRIS). Census data distinguishing between urban and rural areas was taken from the Census of India (2011) to calculate population density across administrative boundaries. Furthermore, socio-physical parameters such as distance from rivers, canals, roads, airports, and historic sites were identified using shapefiles generated from high-resolution imagery from open street maps and Google Earth Pro. All these layers are derived from different data sources, as shown in Table 1. The ArcGIS software facilitated the calculation of Euclidean distances, establishing appropriate distance buffers for each criterion. To assess LULC two multispectral satellite images were procured, ensuring adequate resolution for classification. Utilizing these images, a distance buffer from sensitive places was derived, contributing to the comprehensive evaluation process.

Table 1 Thematic layers used for landfill site suitability of the study area
Full size table

2.3 Methodology

A methodological framework was devised to identify alternative landfill sites for solid waste disposal. Utilizing GIS software and MCDM techniques, this framework is fortified for comprehensive analysis. Ten criteria were carefully selected in this study to meet the intricacies of selecting scientific landfill sites. Weights were assigned to these criteria employing the AHP methodology. In the site selection modeling process, various factors were considered, including Hydrological factors (Depth of water, Distance from River), Topographical factors (Elevation, Slope, Lithology), Land related factors (LULC, Land value), Socio-cultural factors (Population Density, Distance from Sensitive places) and Accessibility factor (Distance from road) in arriving at the most suitable landfill sites. The methodological flowchart for this study is described in Fig. 2.

Fig. 2
figure 2

Methodological flowchart for potential landfill site suitability analysis

Full size image

2.3.1 Multi-criteria decision-making approach

MCDM comprises a series of systematic methods aimed at dissecting intricate choice dilemmas. These methodologies involve decomposing the issue into smaller, more digestible components, scrutinizing each component individually, and subsequently integrating them coherently to derive a solution that is both comprehensible and pertinent. The objective of decision-making is to ascend the hierarchy; thus, the hierarchy becomes progressively detailed until qualities are delineated. The AHP harnesses the decision-maker's subjective perceptions alongside factual evidence to generate optimal choices. Landfill site suitability assessment was conducted on potential landfill sites identified within the study area, following the extraction of restricted regions during the preliminary screening phase. The decision-making process for landfill site suitability mapping necessitates the adoption of a multi-criteria approach and spatial analysis techniques. Techniques for articulating preferences regarding the relative importance of a set of measurable geographical parameters influencing suitable site for landfilling lie at the core of GIS-based MCDM evaluation. AHP, a widely recognized and utilized method for dealing with heterogeneous elements, has been employed to assign relative priority ratings to the various components. The significance and applicability of each criterion were assessed to determine their relative weight.

2.3.2 Analytical hierarchy process

AHP is utilized in multi-criteria analysis, a discipline focused on scrutinizing and deciding based on multiple criteria. This technique dissects decision complexities into smaller components and then consolidates them into a more manageable framework [37]. Once the hierarchy is established, the subsequent steps involve assigning numerical values to each criterion and constructing pairwise comparison matrices for every element, as shown in Table 2.

Table 2 Matrix for pairwise evaluation of ten thematic layers for suitable landfill site selection
Full size table

This approach employed the comparison matrix technique, wherein, aided by Saaty's proximity table, weights were assigned to each criterion based on its relative comparison to others which is shown in Table 3.

Table 3 Saaty’s 9-point intensity scale for determining parameters' weight
Full size table

A key advantage of this method is its ability to facilitate the evaluation of judgments from multiple stakeholders. Given that each individual within a group provides assessments for each criterion, consolidating these evaluations to reach a consensus is crucial. In such instances, group members may be influenced to reach an agreement on specific criteria [75].

Alternatively, dissimilar assessments can be merged using the geometric mean. In this study, Eq. (1) is utilized to compute the geometric mean for each group member's pairwise comparison matrix (A). The initial phase of this method entails deriving the final pairwise comparison matrix, followed by subsequent computational stages outlined below.

Step 1,

$$A= \begin{array}{ccc}{C}_{11}& {C}_{12}& {C}_{13}\\ {C}_{21}& {C}_{22}& {C}_{23}\\ {C}_{31}& {C}_{32}& {C}_{33}\end{array}$$
(1)

where, in this comparison matrix, C11 represents the corresponding values of the ith row (first row) and jth column (first column).

Step 2,

The values of each column were added up separately inside the matrix, and the column total for every pairwise matrix was represented by the following equation:

$${C}_{ij}= \sum_{i=1}^{n}{C}_{ij}$$
(2)

Step-3

The following equations may be used to represent the normalization for each column value after the second equation:

$${X}_{ij}= \frac{{C}_{ij}}{\sum_{i=1}^{n}{C}_{ij}}=\begin{array}{ccc}{X}_{11}& {X}_{12}& {X}_{13}\\ {X}_{21}& {X}_{22}& {X}_{23}\\ {X}_{31}& {X}_{32}& {X}_{33}\end{array}$$
(3)

Step-4

After normalization, the number of criteria was divided by the row sum in the normalization matrix. These weights for the priority vector are computed using the following expression:

$${W}_{ij}= \frac{\sum_{j=1}^{n}{X}_{ij}}{n}=\left[\begin{array}{c}{W}_{11}\\ {W}_{12}\\ {W}_{13}\end{array}\right]$$
(4)

Step 5

To ensure the reliability of judgmental values in decision-making, priority weights are assigned to each criterion based on the consistency ratio (CR) value. Saaty (1980) established that a CR below 0.10 (10%) indicates an acceptable level of consistency in the comparison matrix.

Step-6

By calculating the average value of each consistency vector, the primary eigenvector (λmax) was obtained. Applying the following formula, the principal eigenvalue (λmax) was determined.

$${\lambda }_{max}= \sum_{i}^{n}{CV}_{ij}$$
(5)

Step-7

The Consistency Index (\(CI\)) was calculated in order to assess the extent to which a matrix has departed from consistency. According to Han and Tsay (1998), the determination of the CI necessitated the inclusion of the maximum eigenvalue (\(\lambda max\)) value in the calculation process. The calculation of the \(CI\) was performed as follows:

$$CI= \frac{{\lambda }_{max}-n}{n-1}$$
(6)

The most significant eigenvalue is represented by \(\lambda max\), and n represents the number of criteria.

Step-7

The Random Index (R.I.) represents the CI of a randomly produced pairwise comparison matrix. The RI is determined purely by the quantity of components that were compared. Table 4 displays the RI index values for the CI.

Table 4 Random Consistency Index value
Full size table

Step-8

The CR value ultimately determines the consistency of the relative weights allocated for each criterion. The determination of the final consistency ratio was achieved by the comparison of the CI with the RI.

$$CR=\frac{CI}{CR}$$
(7)

The construction of the CR ensures that it accurately represents a reasonable amount of consistency in the pairwise comparisons. The AHP analysis may proceed if the CR is equal to or below 0.10. In the event that the consistency value exceeds 0.10, it becomes necessary to change the judgmental value in order to address the underlying reason for the discrepancy. The variable λmax represents the result of multiplying each element of the weight matrix with the corresponding column that makes up the comparison matrix. The variable ‘n’ denotes the total number of parameters being considered. RI stands for the random consistency index. The constant parameter RI is determined based on certain criteria. In the AHP method, a value of 1.49 was allocated as the RI for the 10 × 10 matrix. The table displays the confidence interval (CI) and critical ratio (CR) values produced from the previous equation, which are 0.06096139 and 0.040913685, respectively. When the coefficient of reliability is below 0.1, it indicates that the resultant response value demonstrates an appropriate level of consistency. If the calculated CR is over the predetermined level, it is considered that the judgment made in the comparison matrix is inconsistent, and thus, the evaluation process has to be reassessed. In this investigation, a high level of consistency was achieved, as shown by a coefficient of reliability value of 0.06096139. This value is inside the acceptable threshold, suggesting that the data collected in this study is reliable and consistent. Consequently, the AHP matrix exhibits consistency, thus enabling the use of parameter weights for weighted overlay analysis. A suitability map was generated by the implementation of a weighted overlay analysis, wherein the relative weights of each criterion were considered. The AHP was used in combination with GIS to conduct a suitability evaluation. This assessment was then utilized to classify the research region into four categories.

2.4 Landfill site selection criteria

In this study, a set of ten criteria was chosen to evaluate the suitability of landfill sites. The selection process adhered to established regulations governing landfills globally and was informed by an extensive review of pertinent literature [34]. GIS tools were employed to execute various tasks in the creation of criterion maps. These tasks encompass buffering, proximity analysis, density analysis, distance calculation, and data reclassification [26]. The significance of these criteria, along with their sub-criteria and selection methodology, is elucidated herein.

2.4.1 Slope

The selection of appropriate landfill sites relays significantly on the slope of the terrain [4, 52]. In this study, the slope of the study area was evaluated using a Digital Elevation Model (DEM) with a resolution of 30*30 m within a GIS platform. The slope was categorized into four distinct ranges: (0.3–1.6°), (1.7°–3°), (3.1°–5.2°), and (5.3°–18.6°), each assigned a rank indicating its suitability for landfilling. Ranks 1, 2, and 3 denoted areas very highly suitable, highly suitable and moderately suitable. Conversely, rank 4 attributed to slopes falling within the (5.3°-18.6°) categories as not suitable, as illustrated in Fig. 3c. The establishment of landfills on slopes with steeper inclines is deemed technically impractical for several reasons. Firstly, such areas are susceptible to leachate migration, posing a significant risk of water and soil contamination [6, 27, 73]. Moreover, from an economic standpoint, constructing landfills on steep slopes is considered unfeasible [81, 71].

Fig. 3
figure 3

Criteria for Suitable Landfill Site Selection: a Elevation, b Lithology, c Slope, d Land use and Land cover, e Depth of Water, f Population Density, g Distance from Sensitive Places, h Distance from Road, i Distance from River, j Land Value

Full size image

2.4.2 Elevation

Elevation significantly influences the design and operation of landfills, making it a crucial factor to consider. The effectiveness of a landfill site is intricately linked with its elevation, and vice versa [1, 68]. To analyze this, elevation data was processed using an SRTM DEM within the Arc GIS environment. In elevated landfill sites, leachate tends to flow towards lower areas. Therefore, areas with higher elevations were given less weight, while those with lower elevations were given more weight [13, 50]. The study area ranged from 6 to 40 m above mean sea level (MSL) and was categorized into four elevation groups: 6–23 m (very highly suitable), 24–25 m (highly suitable), 26–28 m (moderately suitable), 29–40 m (Not suitable) as illustrated in Fig. 3a. The most suitable elevation was observed at lower heights, while the least suitable elevation was typically found in highly elevated areas.

2.4.3 Land use and land cover (LULC)

Land use patterns are influenced by a combination of environmental and human factors, both on temporal and spatial scales. Areas deemed unsuitable for agricultural activities are frequently earmarked for landfill operations [12, 33]. The importance of considering LULC in the selection of landfill sites has been underscored by Ali et al. [7, 17] who emphasize its role in shaping public perception and acceptance. In this study the LULC analysis utilized high-resolution imagery (Landsat 8, 30-m resolution), with land feature signatures collected and supervised classification techniques employed [7, 33]. LULC area statistics of the study area are described in Table 5. From a commercial standpoint, barren lands are deemed optimal for landfill purposes, assigned a rank of 1. In contrast, water bodies and built-up areas are assigned ranks of 4 and 5, respectively, denoting their unsuitability for landfilling. This is primarily due to the potential adverse impacts on nearby residential areas, including issues such as odours, dust, and noise emanating from the landfill site [73, 76].

Table 5 LULC area statistics of the study area for the year 2023
Full size table

2.4.4 Distance from rivers

Placing landfills near streams and rivers is commonly avoided to mitigate surface water pollution. Several studies have recommended specific distances of 100–300 m from water bodies for landfill sites [4, 7, 12, 18, 27, 33]. This distance helps prevent leachate and toxic gases from landfills from contaminating surface water bodies, including rivers, lakes, wells, and wetlands [48, 54, 59, 60]. In this study, the suitability of distances from landfills to water bodies was assessed. Distances less than 1000 m and between 1000 to 1500 m were found to be unsuitable, with ranks of 4 and 3, respectively. A distance of 1500 m to 2000 m was considered moderately suitable. Distances greater than 2000 m were assigned a rank of 1, indicating very high suitability for landfilling in the Balurghat municipality, as illustrated in Fig. 3i.

2.4.5 Distance from road

Landfill sites should be strategically located close to the existing road network to mitigate rising expenses associated with garbage collection and transportation. Placing a landfill too far from established roads would incur increased construction costs due to the need for new road infrastructure [1, 9, 26]. Moreover, siting a landfill adjacent to residential areas can have adverse impacts on the environment, public health, and the visual appeal of the region [12, 30, 53]. In this investigation, it was found that distances less than 400 m and between 400 to 800 m were highly suitable for landfill placement, receiving ranks 1 and 2 respectively. Conversely, distances ranging from 800 to 1200 m were deemed moderately suitable, while distances exceeding 1200 m were considered unsuitable for landfilling within the Balurghat municipality, as illustrated in Fig. 3h.

2.4.6 Distance from sensitive places

Protecting sensitive areas such as schools, colleges, hospitals, health centers, playgrounds, children's parks, forest cover, airport zones, and other green spaces within urban environments is crucial to safeguarding the natural environment. Landfilling activities in close proximity to these locations can have direct or indirect adverse effects on the surrounding land and ecosystems [44, 73, 82]. In accordance with the guidelines outlined in the Municipal Solid Waste Management Act of 2016 in India, certain criteria must be met to determine suitable landfill sites. Specifically, locations within a radius of less than 500 m from sensitive areas are deemed not suitable for landfilling. Conversely, sites located at a distance of 2000 m or more from these sensitive zones are generally considered acceptable [55]. For this study, data representing sensitive areas were acquired from Google Earth Pro. These input layers were then processed, converting the vector data into raster format. Subsequently, the raster layer was classified into four distinct zones based on proximity to sensitive areas: less than 1000 m (not suitable), 1000–1500 m (moderately suitable), 1500–2000 m (highly suitable), and greater than 2000 m (very highly suitable). Priority was given to areas with greater distances from sensitive locations, as illustrated in Fig. 3g. This approach ensures that the selection of landfill sites takes into account the protection of sensitive areas and minimizes potential negative impacts on the surrounding environment.

2.4.7 Land value

The significance of land valuation emerges as a critical factor in the development and management of landfill sites across various regions [15, 42, 47]. To accurately assess land values, a survey was conducted utilizing GPS technology to pinpoint precise values across different locations within the study area. This survey gathered x and y coordinates (latitude and longitude) to generate a spatial map, employing the Inverse Distance Weighting (IDW) tool within the ArcGIS platform [42, 65]. Through a combination of primary surveys conducted with local residents and insights from secondary sources, it was determined that land valuation in the vicinity of prime downtown area or a bit distance of town central business district (CBD) point ranges from 0.80 lakhs to 11.65 lakhs per Katha which is depicted in Fig. 3j [16]. Notably, areas with lower land values were prioritized and received higher AHP rankings. It also made out the fact that if the fertility of the land is low and not proximal to CBD it may be used for landfill and further if not suitable for institutional and sport or recreational built-up schemes already accepted by local or district or state governances.

2.4.8 Depth of water

To prevent contamination of underground drinking water from landfill contaminants, especially in areas with shallow groundwater, it is crucial to place landfills far from groundwater sources [23, 26, 53]. In a study utilizing data from the WRIS, the IDW interpolation method was employed to create a water depth map [42]. The analysis revealed water depths ranging from 4 to 16 m below the surface. By employing the natural break classifier in ArcGIS, the study delineated four distinct classes depicted in Fig. 3e. Areas with depths up to 10 m were identified as unsuitable for landfill placement and were consequently excluded from consideration. The remaining classes were assigned weights determined through expert scores calculated via the AHP [31]. Locations with the deepest groundwater received the highest AHP score, indicating their enhanced suitability for landfill siting.

2.4.9 Population density

The population density of Balurghat Municipality plays a crucial role in the socioeconomic considerations for identifying appropriate landfill sites. With extensive urban development and increasing occupancy within its boundaries, the Balurghat municipality faces significant pressure to manage waste effectively. However, the presence of landfill sites in residential areas poses serious risks to public health. Hence, it's imperative to choose landfill sites located at a considerable distance from human settlements to mitigate adverse effects on property values and community well-being. In a study conducted by Ahire et al. [3], areas with low population density and minimal human habitation were prioritized for the establishment of suitable landfill sites. The research revealed a wide range of population densities, varying from as low as 1 person per square kilometer to as high as 14,061 persons per square kilometer within the municipality. Notably, locations with a population density exceeding 3751 people per square kilometer were deemed less suitable, receiving a suitability rank of 4. Conversely, areas with the lowest population density were considered most favorable, earning a suitability score of 1. Figure 3f illustrates the outcomes of this study, highlighting the distribution of population density and corresponding suitability scores across different areas within Balurghat Municipality. These findings underscore the importance of factoring population density when determining the optimal location for landfill sites, emphasizing the need to prioritize public health and environmental sustainability in municipal planning initiatives. The model efficiently points out on- suitability of the populous areas for waste disposability rather it identifies barren and non-populous sites and sites unoccupied by human dwelling. Here lies the realistic applicability of the AHP model.

2.4.10 Lithology

Given the significant role that lithology and layering play in predicting leachate migration, it is essential to consider their complexities. Areas characterized by straightforward lithography are considered advantageous as they facilitate more accurate predictions of contaminant dispersion by experts [61]. Geological barriers composed of weakly oxidized and calcareous concretions of sand, silt, and clay are identified as the most effective, whereas those containing iron nodules within sand, silt, and clay are deemed less effective [25]. The lithological structure of the study area represents four distinct classes which are depicted in Fig. 3b and it signifies the suitable sites having sand, silt and clay compositions. The goodness is that such composition will facilitate chances of infiltration of turbid water but feebly oxidized sand, silt and clay complex would not be commensurate for landfill site because there would have problem of embodiment of the particulate pollutants with iron oxides in soil. Here also the vanity of the model fits too realistic.

2.5 Weighed overlay analysis (WOA)

The utilization of the weighted overlay function within GIS environment, the integration of multiple geographical layers to derive a comprehensive and conclusive outcome. The multi-parametric model constitutes a pivotal component within the MCDM process, adhering to compensatory combination principles. This methodology has found effective application in various investigations and analyses, such as the assessment of landfill site selection [3, 32, 36, 73]. The present study employed the WOA technique to identify optimal landfill locations. This involved assigning weights, derived from the AHP, to different factors which is depicted in Table 6.

Table 6 Weight calculated for the AHP parameters to establish Landfill site suitability of the study area
Full size table

The selected criteria, in raster format, underwent reclassification to ensure uniform cell sizes. Subsequently, these reclassified criteria were merged to generate a unified suitability layer. The WOA is defined as:

$${\text{WOA}}=\sum_{I=1}^{N}Wi*Ri$$

In the given context,

\(Wi\) represents the weight assigned to a certain decision criterion, \(Ri\) denotes the raster layer associated with the same criterion, and n represents the total number of decision criteria.

3 Results and discussion

In recent decades, the escalation in global population and urban expansion has led to a significant upsurge in solid waste production, posing a substantial threat worldwide. This challenge is exacerbated highly by inadequate waste management practices and insufficient funding. This study has proved the right decision-making to address this unabated issue through multiple criteria selection and by investigating the scientifically optimal selection of landfill sites that minimize environmental impact while maximizing cost-effectiveness, thereby improving the efficiency of solid waste management systems. Utilizing GIS techniques and MCDM modeling, this study has successfully identified suitable landfill sites. By employing these methods, the Balurghat Municipality has been able to pinpoint alternative dumping locations for municipal solid waste, ensuring a more sustainable waste management approach. The regulatory body responsible for determining optimal and proposed landfill site locations within urban areas will necessitate a diverse array of expertise. This includes engaging professionals such as environmental specialists, scientific personnel, GIS analysts, representatives from both central state and local governments, as well as legal experts. Their collective input is essential for evaluating the suitability of potential landfill sites. Obviously industrial linkages are there in connection to smart garbage and waste control machines which will open up factory innovations in the country if the Balurghat Municipality Waste management model scheme of selection of waste disposal sites comes to be successful from environmental, economic and urban development perception.

In the present study, ten criteria encompassing natural and financial aspects were established to address the pertinent issues within the study area. Utilizing GIS technology, each criterion was mapped, and weight values were determined through the AHP and WOA for landfill suitability mapping. Subsequently, a comprehensive site suitability map for municipal solid waste management in Balurghat municipality was generated, integrating the ten criteria layers within the GIS framework and Various GIS methodologies, such as buffer zoning, interpolation, and overlay analysis, were employed in the process. Initially, four potential zone were identified as suitable for landfill sites. However, further refinement and screening were conducted based on the specific criteria relevant to the study area. To ensure the final decision was well-informed, field observations were carried out to assess the practical suitability of the identified sites. Emphasizing the significance of field visits in research endeavors, this approach served to validate the actual conditions on the ground, enhancing the reliability and accuracy of the study's findings. In accordance with the AHP findings, as delineated in Table 7.

Table 7 Landfill site suitability criteria, with their ranking and weightage value using AHP
Full size table

The criterion of land cost garnered the highest weightage value (28.24%). This underscores the pivotal significance of assessing land cost within the study area when selecting an appropriate site for waste disposal, particularly given the upward trajectory of land cost appreciation attributed to rapid urban expansion and population growth. Following this, Distance from river (20.51%), Distance from Road (15.41%), and Distance from Sensitive places (10.68%), Population density (7.47%), Depth of water (5.66%), LULC (3.98%), Slope (3.19%), Lithology (2.68%) and Elevation (2.18%) were ranked successively in importance, which was described in Fig. 4. Furthermore, one variable is found which can be incorporated for the future work is flood inundation prone areas. There is always a chance of surface water contamination as well as flooding in the landfill areas can affect the quality of surface soil highly.

Fig. 4
figure 4

Hierarchical weighting results for AHP Multicriteria Techniques

Full size image

Consequently, the study culminated in the creation of a landfill site suitability map within a GIS environment, incorporating ten criterion layers. This facilitated the identification of the most optimal and environmentally sustainable locations for municipal landfills within the city of Balurghat. In this study, the consistency of relative weights was assessed using the AHP method. The calculated lambda (\(\lambda max\)) value for AHP was 10.55, while the Consistency Ratio was found to be 0.04, indicating that the consistency falls within an acceptable range. The suitability map delineated four distinct zones: Very high suitability zone (32.72 ha), high suitability zone (1560.96 ha), moderate suitability zone (2690.47 ha), and not suitable zone (740.69 ha) which are depicted in Table 8. Furthermore, the analysis revealed three highly suitable sites situated in the northwestern segments of the city, with an additional two sites located in the southeastern areas, spanning across uncultivated land and sparsely vegetated.

Table 8 Potential landfill site suitability area of the study area
Full size table

This site presents optimal conditions for the disposal of municipal solid waste due to its predominantly uncultivated terrain, minimal land value, absence of water bodies and vegetation, and excellent accessibility via road networks. The areas identified as most suitable for landfilling are highlighted in a dark green hue. Conversely, regions deemed less suitable are marked in orange, as illustrated in Fig. 5.

Fig. 5
figure 5

Potential landfill site suitability zones of the study area

Full size image

During the initial evaluation of the selected sites, a thorough examination of public sentiment was conducted during the field visit. Various factors, such as objections, concerns, and perceptions expressed by local residents, were carefully taken into account before finalizing the choice of the 6 landfill sites. It was recognized that even though a site might meet technical and economic feasibility criteria, strong public opposition could arise due to concerns such as noise, dust, odour, and potential adverse effects associated with landfill operations. on taking care of all these, the model was freed from the inacceptance of the public and for which a low percentage of accuracy was lost due to criteria reduction like excluding sensitive places of even 1000–1300 m average. But accepting of the unrealistic output of result was jilted. When evaluating alternative locations for landfill sites, it is essential to consider the proximity to agricultural land as a key factor due to its potential impact on soil productivity. To prevent the degradation of soil quality, it is advisable to maintain a significant distance from agricultural areas when siting landfill sites. Placing landfill sites in windy areas increases the risk of solid waste dispersion into nearby agricultural lands, underscoring the importance of selecting locations distant from such areas. Within the jurisdiction of Balurghat municipality, the analysis was conducted on elevation data as part of a study aimed at pinpointing appropriate locations for landfill sites possessing terrain features conducive to safe and environmentally sustainable waste disposal. Areas of higher elevation were examined for their potential to offer natural drainage, thereby diminishing the likelihood of waterlogging and accumulation of leachate, thus bolstering the overall stability of landfill operations over time because separation of comparatively high elevation areas subtracted to find out low-lying areas subjectively necessitating suitable site finding process. Conversely, lower-lying areas were scrutinized for their susceptibility to flooding and fluctuations in the water table, which could heighten the risk of contamination and disruption to the local ecology [26, 77]. Shallow water depth areas are considered less suitable for landfilling, as they pose a higher risk of contamination. Given the significance of various sensitive zones, including agricultural land, higher education institutions, core-built-up areas, water bodies, parks, hospitals, as well as historical and religious sites, they have been deliberately omitted from the suitability mapping process. Furthermore, the distance from existing landfill sites is a crucial consideration. It is imperative to avoid situating alternative landfill sites in close proximity to existing ones to mitigate environmental and health concerns in the surrounding area. By ensuring that certain parts of the city remain unaffected. Evaluation of the functionality of landfill sites is crucial to determine their sustainability until decommissioning. From both environmental and economic perspectives, it is advantageous to prioritize larger landfill sites due to their extended operational duration. Larger sites typically sustain operations for at least ten to fifteen years longer than smaller ones as found in the Balurghat Municipal area. Additionally, opting for larger landfill sites can reduce the costs associated with constructing and designing additional landfill sites in the future. The selection of suitable landfill sites has been determined after a thorough literature review and expert opinion. The results of the study show six potential landfill sites utilizing the MCDM approach. These sites have been validated through field verification and high resolution Google Earth imagery. The reasons for selecting these sites are also based on the available literary sources (Table 9).

Table 9 Potential landfill sites with their respective land use types
Full size table

Following extensive evaluation, Sites-1 and Sites-2 have emerged as the foremost landfilling options for the city. These sites have been pinpointed as the optimal choices, offering the least adverse effects on both the environment and public health. Consequently, they represent the most favorable locations for alternative landfills. These three sites are poised to minimize environmental degradation and safeguard public health.

3.1 Validation of landfill site suitability model

Despite being a useful tool for identifying the viable model, the receiver operating characteristic curve, or ROC curve, is very scientific and useful to use. To determine the extent to which different modelling approaches can produce precise predictions, a validation process is necessary for ultimate and comprehensive predictions [39]. To validate the suitability map in the Arc GIS environment, a ROC simulation was run. A ROC value close to 1.0 implies that the model's values are realistic, while a value below 0.5 indicates an inefficient model and a higher value indicates the accuracy of the model [56]. This study employed the AUC-ROC curve with a validation dataset of 80 GPS points, which are collected during field visits and from Google Earth images, both to evaluate the accuracy of AHP for landfill site suitability Mapping. Subsequently, the quantitative validation of the landfill site suitability map was evaluated by computing the area under curve (Fig. 6). The AUC value of 0.93, which falls within the acceptable range of 0.9 to 1, shows very high reliability of the obtained results [62].

Fig. 6
figure 6

ROC curve for landfill site suitability map of the study area

Full size image

4 Conclusion

In conclusion, this research underscores the critical importance of implementing a MCDM approach within a GIS framework to identify optimal landfill sites in the rapidly expanding urban area of Balurghat Municipality. With the absence of designated disposal sites leading to unregulated waste dumping along the Atrai River, this study addresses the urgent need for a scientifically sound and socially accepted waste management solution. Through meticulous analysis, six potential landfill sites were identified, considering environmental, socio-economic, and spatial factors. The methodology employed, integrating diverse skills and scientific methodologies, contributes significantly to the understanding of suitable landfill site selection in developing nations, emphasizing scientific validity and social acceptance as key considerations.

Moving forward, it is imperative for municipalities to prioritize the development of human and infrastructural capacities while incorporating GIS and MCDM techniques into solid waste management strategies. This research lays the groundwork for addressing concerns surrounding potential landfill locations, offering a robust framework for decision-makers and stakeholders. By considering environmental health risks, socio-economic factors, and stakeholder engagement, this approach fosters sustainable waste management practices and paves the way for a cleaner and healthier urban environment in Balurghat Municipality and beyond.