Site Selection Process Research Articles

Future Site Suitability for Urban Waste Management in English Bazar and Old Malda Municipalities, West Bengal: A Geospatial and Machine Learning Approach

The rapid urbanization occurring globally has significantly intensified the challenges of waste management in densely populated metropolitan areas. A growing amount of waste has become a major concern for municipal authorities and local governments due to the limited availability of suitable land. Geospatial techniques, such as Geographic Information Systems (GISs) and remote sensing, combined with machine learning, play a crucial role in identifying suitable sites for urban waste management. These techniques assist planners in making well-informed decisions that strike a balance between environmental preservation and urban expansion by examining spatial data on land use, population density, and environmental concerns. Geospatial tools provide a data-driven basis for policy and urban planning, ensuring effective land use, reducing ecological hazards, and promoting sustainable urban growth for municipalities such as English Bazar and Old Malda. It can also pose serious threats to the environment, public health, and communities. Focusing on the English Bazar and Old Malda Municipalities in India, this paper examines the use of geospatial technologies to identify suitable sites for waste disposal. The research aims to address the complex processes of waste generation, collection, and disposal in urban environments. Using GIS and a Multi-Criteria Decision Analysis (MCDA) approach, the study employs the Analytic Hierarchy Process (AHP) alongside the Random Forest (RF) model and a machine learning (ML) technique to identify potential waste disposal sites within the English Bazar and Old Malda Municipalities in the Malda district. Eight key criteria were considered in the site selection process: land elevation; distances from surface water, roads, railways, and urban areas; groundwater depth; land use and land cover; and distance from sensitive and restricted areas. AHP analysis showed that 8%, 26%, and 27% of the sites were categorized as very highly suitable, moderately suitable, and unsuitable, respectively. Meanwhile, 38%, 17%, and 13% of the areas were classified as unsuitable, moderately suitable, and very highly suitable according to the RF model. The overall accuracy and Kappa coefficient indicated that the AHP method (overall capacity of 83.83% and Kappa coefficient of 0.7894) was slightly better than the RF model (overall capacity of 80.61% and Kappa coefficient of 0.7474) for site suitability analysis. This research underscores the broad relevance of geospatial technology in creating resilient and environmentally sustainable cities while offering valuable guidance on effectively allocating waste disposal sites. The findings provide crucial insights for urban planners and decision-makers, facilitating the identification of optimal locations for sustainable waste management in urban settings.

Site selection for biomass-solar hybrid renewable energy facilities: Spatial modelling based on fuzzy logic-geographic information systems

The aim of this study is to propose a fuzzy logic-geographic information systems (GIS) based spatial model within the context of hybrid renewable energy, rural areas, and spatial planning approach in the site selection process of renewable energy facilities. In this context, potential suitability index and site selection suitability index for biomass-solar hybrid renewable energy facilities are determined within the spatial model, through a case study conducted in Türkiye. The study involves a two-stage process: the first stage focuses on establishing the national-level potential, and the second stage involves site selection in areas with high potential. Suitable locations are determined based on the optimal suitability index values, which are derived from fuzzy logic operators including AND, OR, SUM, PRODUCT, and GAMMA, applied in the GIS. The study results indicate that by assigning different γ values to the fuzzy GAMMA operator, results for the fuzzy AND (γ = 0.45–0.60), fuzzy OR (γ = 1.00), fuzzy SUM (γ = 1.00), and fuzzy PRODUCT (γ = 0.00–0.15) operators can be obtained. Furthermore, as the γ value increases, the suitability index also rises, and the optimal site selection occurs at γ = 0.90. The performance of the fuzzy GAMMA operator in big data sets and two-stage processes demonstrates its applicability in site selection.

Transdisciplinary research on the safety case for nuclear waste repositories with a special focus on uncertainties and indicators

In the search for a repository site for high-level radioactive waste in Germany, the perception of safety and trust in the actors are central to public acceptance. In communicating safety, methods of safety assessment and the role of uncertainties need to be addressed. Given the complexity of the issue, there is a need for indicators that are suitable both for assessing the long-term safety of repositories and for communicating with the general public. Similarly, there is a requirement to communicate uncertainties in an accessible manner. The TRANSENS project provides basic research in nuclear waste management (NWM) and utilizes a transdisciplinary approach: Non-experts who are not directly affected by the site selection process and who have no stated interest in NWM are involved in the research process, as are practice actors. A series of four transdisciplinary workshops was specifically designed to explore the perspectives of individuals with a high level of disciplinary knowledge but no system knowledge of NWM. Participants were selected from doctoral students in science and technology who had no prior knowledge in this area. Two of these workshops address the questions stated above and are presented here. The article describes the considerations underlying the workshop planning and implementation phases, and the content developed in the workshops on indicator selection and visualisation of uncertainties. The participants compiled a list of desirable indicator properties, which showed a high degree of congruence with the relevant literature. A proposal for a database to collect, administer and assess uncertainties shows similarities with the approach followed by the German implementer and complements it with an interactive visualisation. Transdisciplinary work is resource-intensive and its use in a research context must be carefully considered for each individual application. A transdisciplinary approach was successfully used for the purposes of method validation, method optimisation and the development of disciplinary impulses. An application of transdisciplinary approaches for optimising the Safety Case of nuclear repositories is feasible.

Site selection for a high-level nuclear waste repository in Germany: a short guide

The government agency BGE started a site selection process for a high-level nuclear waste repository in 2017. The first phase of data screening was completed in 2020 and resulted in the identification of search areas covering 54% of the national territory. The actual second phase will be completed by 2027 or later, and the areas for aboveground investigations will be identified. The obligatory nongovernment input is provided by follow-up regional conferences, which are best supported by external scientific expertise. The large amount of text documents and maps and the interactive internet tools such a 3D viewer provide a suitable basis for external evaluation. The rationale is to apply stricter criteria than those used in the first phase to drastically reduce the size of the search area. With this updated set of criteria, the size of the search area can be reduced to 2,662 km2 or 0.7% of the national territory. This size is convenient for detailed aboveground investigations such as high-density 3D seismic surveys.

Selection of potential location for technopark based on pineapple SMEs using AHP-GIS techniques in Kampar Regency

Kampar Regency, as the largest pineapple producer in Riau Province, has yet to provide significant added value for the surrounding SMEs. The limitations in technology and innovation, infrastructure support, and market access have prevented this potential from being optimally utilized. A Technopark can provide the necessary facilities and infrastructure to enhance production efficiency, innovation, and product quality, thus driving local economic growth. The objective of this study is to identify and determine potential locations for the development of a pineapple-based Technopark in Kampar Regency. This study is crucial as a fundamental consideration in selecting the technopark location and assessing the effectiveness and success of the technopark area. The method used in this study is AHP-GIS to analyze relevant parameters in the site selection process for the technopark area. Parameters considered in this study include slope, land use, availability of raw materials, accessibility of roads, access to water resources, proximity to universities, market access, population density, and landfill. The analysis results indicate that the percentage of land highly suitable for the technopark location is 0.78%, covering an area of 8943 hectares. Based on the analysis, it is recommended that potential locations for the development of a pineapple SMEs-based technopark in Kampar Regency are dispersed in Tambang District, encompassing three villages: Rimbo Panjang, Kualu Nenas and Tarai Bangun. The findings of this study align with the spatial planning of Kampar Regency.

Temporal Behavior of Glacially Induced Stresses and Strains at Potential Sites for Long‐Term Storage of Used Nuclear Fuel in Canada

AbstractContinental‐scale glaciations cause deformation, geopotential, rotation and stress changes of the Earth. Subsurface stress changes have implications to future activities such as carbon capture and storage, enhanced oil recovery and deep geological disposal of nuclear waste. We model glacially induced stresses, strain changes and deformation for North America, with emphasis on the two potential sites for long‐term storage of used nuclear fuel in Canada (Saugeen Ojibway Nation (SON)‐South Bruce area in southwestern Ontario and Wabigoon Lake Ojibway Nation (WLON)‐Ignace area in northwestern Ontario). We apply a revised, high‐resolution ice history of the past glacial cycle from the University of Toronto Glacial Systems Model, assumed to be representative for future glacial cycles, together with a set of seven different one‐ and three‐dimensional earth structures. We find that glacially induced stresses and strains can vary strongly throughout a glacial cycle, whereas especially the horizontal components can change from tensional to compressive in nature. Such changes can happen within a few 1,000 years, caused by drastic and rapid ice thickness increase or decrease above the potential site. Despite SON‐South Bruce being located further away from the ice sheet center than WLON‐Ignace and temporarily in the forebulge of the developing ice sheet during glaciation, stresses and strains are very similar in magnitude and range at both sites. We also see the potential that the glacially induced stresses can alter the direction of the pre‐existing maximum horizontal stress at SON‐South Bruce. These results will be incorporated in the site safety and site selection process.

Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China

Influenced by increasing global extreme weather and the uneven spatiotemporal distribution of water resources in monsoon climate areas, the balance of agricultural water resources supply and demand currently faces significant challenges. Conducting research on the spatial allocation trade-offs and synergistic mechanisms of agricultural water resources in monsoon climate areas is extremely important. This study takes the spatial layout of reservoir site selection in water conservancy projects as an example, focusing on Shandong Province as the research area. During the site selection process, the concept of water resource demand is introduced, and the suitability of reservoir siting is integrated. It clarifies ten influencing factors for suitability degree and five influencing factors for demand. A bi-objective optimization model that includes suitability degree and demand degree is established. Utilizing machine learning methods such as the GA_BP neural network model and the GA-bi-objective optimization model to balance and coordinate the supply and demand relationship of agricultural water resources in the monsoon region. The study found that: (1) in the prediction of suitability degree, the influencing factors are most strongly correlated with the regulatory storage capacity (regulatory storage capacity > total storage capacity > regulating storage coefficient); (2) compared with single-objective optimization of suitability degree, the difference between water supply and demand can be reduced by 74.3% after bi-objective optimization; (3) according to the spatial layout optimization analysis, the utilization of water resources in the central and western parts of Shandong Province is not sufficient, and the construction of agricultural reservoirs should be carried out in a targeted manner. This study provides new ideas for promoting the efficient use of water resources in monsoon climate zones and the coordinated development of humans and nature, reflecting the importance of supply and demand balance in the spatial allocation of agricultural water resources, reducing the risk of agricultural production being affected by droughts and floods.

Evaluating Taiwan’s Geothermal Sites: A Bounded Rationality Data Envelopment Analysis Approach

Amid rising global demand for renewable energy, geothermal power emerges as a vital, low-carbon solution to enhance energy security and sustainability. Taiwan, strategically located on the seismically active Pacific Ring of Fire, possesses an untapped geothermal potential that is underutilized due to complex site selection challenges. This study specifically addresses the need for a more precise and psychologically attuned site selection process, aiming to optimize the development of geothermal resources in regions with complex geological settings. Utilizing the Modified Bounded Rationality Data Envelopment Analysis (MB-DEA) model, this research integrates traditional DEA with bounded rationality to factor in the risk preferences of decision-makers, offering a novel approach that enhances accuracy in evaluating geothermal sites. This study addresses the critical challenge of accurately selecting geothermal energy sites in geologically complex regions like Taiwan, where traditional methods fall short, aiming to significantly boost the efficiency and effectiveness of geothermal energy exploitation as part of Taiwan’s transition to renewable energy sources. Applied to 30 potential sites across Taiwan, our model provides a detailed assessment based on technical, economic, and psychological criteria, revealing variations in site suitability influenced by stakeholder risk attitudes. Key locations such as Datun Mountain, Maoxing, and Taolin consistently rank highly, confirming their robust potential irrespective of risk preferences. At the same time, other sites show marked sensitivity to shifts in decision-making attitudes. This work significantly advances the methodology of renewable energy site selection by demonstrating the utility of incorporating psychological factors into analytical models, which not only refines decision-making processes but also aligns with Taiwan’s strategic energy planning goals. This study also underscores the importance of accurate geographical data in complex terrains, suggesting further refinement and dynamic integration of bounded rationality for future research.

Spatial and seasonal variability in benthic impact of mussel farms: Predicting and mitigating impacts using ambient oxygen conditions

Environmental impacts are a cause for concern when developing and expanding aquaculture and to be sustainable potential negative effects need to be addressed. The intensity and extent of these impacts likely vary among sites and seasons, depending on multiple factors including the physical and biological setting and operational aspects. Using a combination of sampling techniques, we investigated the spatial variability in epibenthic impacts in eleven commercial mussel farms, on the Swedish west coast. We found increased levels of organic content, changes in epibenthic macrofauna and increased cover of Beggiatoa sp., a documented indicator of hypoxia. The extent of these impacts was generally limited to the extent of the farms. Because the cover of Beggiatoa sp. was particularly clear and because oxygen conditions in the sediment is of great importance to the structure and function of these habitats, we analysed spatial patterns using an index of the benthic footprint (BFI) accounting for both intensity and extent of impacts. In the summer, the BFI varied strongly among farm-sites and subsequent analyses showed that it highly correlated with ambient bottom oxygen concentration. Repeated sampling during early spring, however, showed that impacts were quickly reversible also in the most impacted sites. Thus, we conclude that in Swedish coastal waters the benthic footprint calculated on the % cover of Beggiatoa sp. is highly dependent on ambient oxygen concentration. We suggest that knowledge about spatial and temporal patterns of oxygen in the bottom water can be used to predict the severity of impacts and provide an important criterion in a site-selection process aimed at developing a sustainable food industry.

Integrating socioeconomic and ecological data into restoration practice.

Driven by the United Nations Decade on Restoration and international funding initiatives, such as the Mangrove Breakthrough, investment in mangrove restoration is expected to increase. Yet, mangrove restoration efforts frequently fail, usually because of ad hoc site-selection processes that do not consider mangrove ecology and the socioeconomic context. Using decision analysis, we developed an approach that accounts for socioeconomic and ecological data to identify sites with the highest likelihood of mangrove restoration success. We applied our approach in the Biosphere Reserve Marismas Nacionales Nayarit, Mexico, an area that recently received funding for implementing mangrove restoration actions. We identified 468 potential restoration sites, assessed their restorability potential based on socioeconomic and ecological metrics, and ranked sites for implementation with spatial optimization. The metrics we used included favorable conditions for propagules to establish and survive under sea-level rise, provision of ecosystem services, and community dynamics. Sites that were selected based on socioeconomic or ecological metrics alone had lower likelihood of mangrove restoration success than sites that were selected based on integrated socioeconomic and ecological metrics. For example, selecting sites based on only socioeconomic metrics captured 16% of the maximum attainable value of functioning mangroves able to provide propagules to potential restoration sites, whereas selecting sites based on ecological and socioeconomic metrics captured 46% of functioning mangroves. Our approach was developed as part of a collaboration between nongovernmental organizations, local government, and academics under rapid delivery time lines given preexisting mangrove restoration implementation commitments. The systematic decision process we used integrated socioeconomic and ecological considerations even under short delivery deadlines, and our approach can be adapted to help mangrove restoration site-selection decisions elsewhere.

A Review of the Sustainable Siting of Offshore Wind Farms

The continued technical and economic development of offshore wind farms needs to match their sustainable siting transparently and fairly. Aiming to assess existing methodologies widely used in the field of OWFs spatial planning, as well as to identify the proposed enhancements for the improvement of such methods, this study examines 80 peer-reviewed papers over the past eight years. The analysis encompasses articles from 34 scientific journals, with a notable concentration in the journals Renewable Energy, Renewable and Sustainable Energy Reviews, and Energies, and it sheds light on geographical distribution, journal classification, funding sources, and the various methodological approaches. Most of the studies were conducted in Turkey, China, and Greece; half of the surveyed papers utilize multi-criteria decision-making approaches, predominantly addressing bottom-fixed technologies for offshore wind farms, which currently dominate the field. The 80 papers are categorized into five methodological domains: Marine Spatial Planning, Feasibility Analysis, Probabilistic Methods, Meteorological Data, and Multi-Criteria Decision Making. One hundred and seventy criteria were identified and condensed into a final set of 41 critical criteria. This article provided an overview of the site selection process and the most crucial findings and recommendations.

T81 - Characterization of Peer Support Services for Substance Use Disorders in 11 U.S. Emergency Departments in 2020: Findings From a Nida Clinical Trials Network Study Site Selection Process

T81 - Characterization of Peer Support Services for Substance Use Disorders in 11 U.S. Emergency Departments in 2020: Findings From a Nida Clinical Trials Network Study Site Selection Process

Proposal for a managed aquifer recharge feasibility index for southern Portugal using multi-criteria decision analysis

Water scarcity in the Mediterranean region requires the adoption of new and optimized water resources management tools that allow a progressive climate change adaptation, being one of the main drivers for the employment of alternative water storage measures such as Managed Aquifer Recharge (MAR). To promote the implementation of these measures, this study evaluates the application of a MAR feasibility index mapping to southern Portugal, employing Multi-Criteria Decision Analysis (MCDA). A participatory approach – developed within the AGREEMAR project – aims for the characterization of the intrinsic site suitability, water availability and demand, where the stakeholder inputs are embedded in the weighting computation process.The results obtained demonstrate prevalent moderate MAR feasibility across the entire region (78% of the area) with an increased percentage of the area corresponding to high MAR feasibility (22%) strongly related to highly suitable geological features. A comparative analysis with the Analytical Hierarchical Process (AHP) showed minor high MAR feasibility areas (7%), and feasibility class transitions between the directly above or below classes.A sensitivity analysis, conducted based on a one-at-a-time (OAT) variation of the criteria weights, assessed the impact of the weighting methods on feasibility class changes. For the AGREEMAR method, changes in the weight of 10 of the 28 considered criteria resulted in negligible changes when compared to the original feasibility map, while for AHP 3 out of 28 produced minor changes. The remaining criteria showed important modifications in the feasibility maps, which underlined AGREEMAR method weights provided higher stability in the results.The study provides information about a MAR site selection process procedures, allowing replication. The dissemination of the results is expected to raise awareness of MAR among stakeholders and support scientifically-based water resources management decision-making.

Identification of potential marine protected areas based on cumulative risk assessment: case study in Balikpapan Bay, Indonesia

The effectiveness of designing Marine Protected Areas (MPAs) can support the Marine Spatial Planning (MSP) process and vice versa. However, selecting suitable sites for MPAs is challenging due to anthropogenic activities increasing the cumulative risk of ecosystems in coastal regions. This study demonstrates cumulative risk application in Balikpapan Bay, Indonesia, as an MPA site selection process example with meaningful ecological and socio-economic information. We coupled InVEST and Marxan modeling software to analyze multiple conservation scenarios. Results indicated fishing, coastal development and oil gas exploration were the greatest risk activities to the habitat and species. These results were used as a cost layer in Marxan to select protection areas. We identified 189.09 km2 areas as a high priority, with the upstream bay being the highest, decreasing downstream. The findings provide valuable input and support allocating space to a potential MPA in the MSP process to improve the management of alternative development.

Efficient site selection for clinical trials using simulated annealing.

1558 Background: Identifying sites with eligible patients for multi-site oncology clinical trials is challenging given increasingly complex inclusion/exclusion criteria, limited site resources, and the need to ensure diversity in trials. This is a complex systems problem with many factors that present nonlinear behaviors. Given manifold combinations of sites, research capabilities, and study characteristics, artificial intelligence solutions may help research sites and sponsors optimize site selection to achieve enrollment, speed, and diversity of goals. We report on one of the first deployments of a multi-AI model solution to optimize site selection for an ongoing clinical trial. Methods: The clinical trials were translated into a standardized digital form expressed as rules fully encompassing the inclusion and exclusion criteria. A patient cohort was created from the criteria using the ConcertAI oncology research database, including site affiliations. The sites had numerical features specific to the protocol indication, such as physician count and projected patient count. Each site was scored by its features with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to create higher alignment to the best matched sites. Feature weights allowed TOPSIS to emphasize desirable attributes in an interpretable way. A simulated annealing algorithm, adept with nonlinear objectives, explored site combinations, yielding a high-scoring, constraint satisfying solution. Independent simulations measured convergence and provided a distribution of solutions. Shapley analysis clarified site contributions to score and constraints. The implementation was compiled and parallelized. Results: Utilizing a Multiple Myeloma clinical trial protocol with 7 criteria, we formed a cohort of 15,881 patients across 1,246 US clinical trial sites. Each site had 12 features. The weights optimized patient diversity while enrolling at least 10 patients per month across all sites to achieve the targeted study accrual timeline. The result was a set of 3 sites enrolling 12 patients per month with 51% diversity, in agreement with the optimal solution obtained through detailed, multi-week, manual analysis. As constraints increased, and exact verification became impractical, convergence was gauged by decreasing variance in independent solutions. Conclusions: Our study introduced a robust, efficient, and interpretable approach to clinical trial site selection. It addressed the challenges posed by increasingly complicated protocols, requirements for study participant diversity, and heterogeneous site features. The multi-AI model scalability underscored its practical utility in streamlining the site selection process for clinical research networks, academic centers, industry sponsored trials, cooperative group studies, and investigator-initiated studies.

Petrophysical Analyses for Supporting the Search for a Claystone-Hosted Nuclear Repository

The Bundesgesellschaft für Endlagerung mbH (BGE; “Federal Company for Radioactive Waste Disposal”) is responsible for identifying a repository site in Germany for the storage of high-level radioactive waste, ensuring the best possible safety for at least 1 million years (StandAG, 2017). The three-phase site selection process is currently in Step 2 of its Phase I. Around 90 potentially suitable sub-areas are evaluated via individual representative preliminary safety assessments. These sub-areas cover all types of potential containment rock: allochthonous and autochthonous rock salt, claystone, and crystalline rock. Such a comprehensive assessment is challenging for a host rock that covers vast areas like claystone or autochthonous salt. One of the steps towards estimating parameters for a site search process requires the analysis of numerous logs from the areas of interest and their vicinity. Unfortunately, computing these parameters is a much more complex problem than it seems. While gamma ray logs have been the petrophysicist’s workhorse for decades, they allow no quantification of clay properties. Nuclear magnetic resonance and neutron activation logs yield critical information but are scarce in old wells. Decades of research have given good algorithms for defining wet clay porosity from resistivity logs, but inverting those for porosity and tortuosity remains challenging. Another log-derived parameter is a formation’s homogeneity, which quantifies the jaggedness of a logging curve. A vertical variogram or variable filter technique can accomplish this.

A GIS-based multi-criteria decision-making approach (GIS-MCDM) for determination of the most appropriate site selection of onshore wind farm in Adana, Turkey

AbstractEnergy stands as an indispensable global need, yet the finite nature of rapidly depleting fossil fuel reserves poses environmental challenges. To address this, numerous nations are shifting toward sustainable energy sources to foster environmental well-being while satisfying their energy requirements. Among these alternatives, wind energy emerges as a particularly efficient option. This study focuses on identifying optimal sites for installing wind power plants in Adana province through the integration of the analytic hierarchy process (AHP) and geographic information system (GIS). Within this framework, a comprehensive set of 15 criteria was delineated along with their respective sub-criteria. It is thought that the criteria used in the study will be useful not only for Adana province but also for other provinces that are suitable for wind turbine placement and planned to be built. The outcome of this investigation delineates potential areas in Adana province deemed suitable for establishing wind power plants. As a result, analysis reveals that 9.94% of the study area is conducive to establishing a wind farm (WF), while 51.66% is deemed moderately suitable. Notably, the districts of Feke, Tufanbeyli, Saimbeyli, and Pozantı emerge as particularly well-suited for this purpose. This study aims to contribute to the existing literature by identifying precise and suitable areas by combining GIS and AHP in the WF site selection process. In the study, a new research perspective is presented by taking into account the uncertainty in the site selection process and the concept of sustainability in three different dimensions: technical, economic, and environmental, thus aiming to guide decision-makers for future WF projects. In addition, it is thought that the methods and criteria used in the study will also be guiding for future WF site selection studies. Graphical abstract

Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt

Abstract Airports are among the constructions that must meet international standards and specifications established by the International Civil Aviation Organization (ICAO). Some of these parameters and criteria include topography, environmental, and operating circumstances. Navigation operations are also affected by terrain and human barriers, while noise, infrastructure, and weather factors affect the environment around airports. This article investigates using the interpretative structure model (ISM) and analytical network process (ANP) together as a new technique to select and determine the optimum site selection for constructing new international airports in the Nile Delta, Egypt. The criteria used (16 international criteria) are selected depending on the standards of the ICAO, field surveys, and previous studies. Nile Delta, Egypt, is chosen as a case study because it suffers from the lack of international airports and has a population of approximately 23 million. Therefore, new international airports must be established to serve this region, boost tourism, improve transportation, and stimulate commercial traffic. The results using the suggested new technique are compared with the traditional used methods for site selection, such as fuzzy-analytic hierarchy process. Landsat 8 images are used in this research. A quality test using the area under the curve and the receiver operating characteristic curve was applied to evaluate the new technique for site selection of international airports, depending on calculating the highest suitability index for each proposed site. From the quality tests, it is deduced that the suggested method (ISM–ANP) for airport site selection is more accurate than any other traditional method. ArcGIS 10.5 software is used to draw the final digital maps containing the proposed resulted sites. As a result, three new locations for the construction of international airports were found and selected throughout the research region (Delta Nile, Egypt) based on the used mathematical models. Therefore, the proposed novel method for determining the locations of international airports is thought to be effective and feasible, and it can be used to determine the locations of any development projects in general, particularly in developing countries, which benefits the decision-makers in making the right decisions.

Explainability in wind farm planning: A machine learning framework for automatic site selection of wind farms

The process of wind farm site selection involves complex considerations spanning environmental, economic, and social factors, often handled through multi-criteria decision making (MCDM) methods. However, MCDM approaches face several challenges. To address these, integrating machine learning (ML) techniques offers promise. Nevertheless, existing studies have yet to employ a standalone ML-based approach for this purpose. This study seeks to pioneer an explainable ML-based framework for automated wind farm site selection. The framework incorporates feature selection techniques, seven ML algorithms, statistical tests, and a local explainability method. The experimental area was chosen as Balıkesir province, which hosts 13% of Türkiye's installed wind energy capacity. According to the prediction performances of the ML models, Extreme Gradient Boosting-XGBoost has the highest accuracy (0.9607), followed by Light Gradient Boosting Machine-LightGBM (0.9580), Random Forest-RF (0.9518), Histogram-based Gradient Boosting-HGB (0.9387), Classification and Regression Trees-CART (0.8946), Logistic Regression-LR (0.8856), and Naive Bayes-NB (0.8456). Additionally, McNemar’s tests revealed statistically significant differences among ML models. Based on the explanations provided by SHapley Additive exPlanations, wind speed, distance to transmission lines, distance to protected zones, and elevation were the top contributing criteria. Moreover, analysis revealed regions with elevated wind speeds, higher elevations, and closer proximity to transmission lines and protected areas as favorable for wind farm installation. The study's outcomes indicate strong agreement (97%) between current wind turbine locations and those identified as suitable by ML models. Within Balıkesir province, our research has identified a significant number of highly suitable areas where wind turbines have not yet been installed. These findings underscore the critical importance of these locations for future investments in wind farm development. The proposed framework could be highly efficient for wind farm siting studies in provinces that share similar characteristics with the study area used in this study offering a robust tool for future wind farm siting studies. Finally, the study contributes to achieving UN SDG 7 by employing ML to refine wind farm site selection, thereby facilitating the transition towards sustainable and universally accessible energy.

A comprehensive review on the integration of geographic information systems and artificial intelligence for landfill site selection: A systematic mapping perspective.

Properly selecting landfill sites for waste disposal is crucial for mitigating environmental and public health risks. Geographic Information Systems (GISs) and Artificial Intelligence (AI) techniques have emerged as valuable tools for identifying suitable landfill locations. This study presents a systematic mapping study (SMS) that investigates the usage of GIS and AI in landfill site selection. We searched six databases (IEEE Xplore, ACM Digital Library, Science Direct, Emerald Insight, Taylor & Francis Online and Web of Science) using predefined keywords related to landfills, GIS and AI. From 858 initially retrieved articles, we selected 48 relevant articles for in-depth analysis. Our research aimed to answer various questions, such as publication trends, the geographic distribution of case studies, criteria for assessing landfill suitability, tools and techniques employed, preliminary site screening methods, decision-making processes, limitations and future research directions. We used bubble charts, bar charts and tables to visualize the results. The findings of our study highlight the growing interest in using GIS and AI for landfill site selection and emphasize the importance of incorporating multi-criteria decision-making techniques. Furthermore, the results reveal the need for developing more advanced AI models, addressing the limitations of current approaches and exploring novel visualization techniques for enhancing landfill site selection processes. This study provides valuable insights for researchers and practitioners in waste management, environmental science and geoinformatics. It sets the groundwork for future research on improving GIS- and AI-based landfill site selection methodologies.