Site Selection Model Research Articles

Locating the suitable large-scale solar farms in China's deserts with environmental considerations

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4–84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47–110 PWh which is 1.7–3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8–62.6 gigatons annually, offsetting 73–170 % of global emissions. Covering just 4.8–11.5 % of China's desert area (8 × 104–19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

Performance Evaluation for the Expansion of Multi-Level Rail Transit Network in Xi’an Metropolitan Area: Empirical Analysis on Accessibility and Resilience

As the main form of new urbanization, the coordinated development of cities in metropolitan areas requires reliable and efficient rail transit skeleton support. However, in the rapid development of metropolitan areas, the layout and analysis of multi-level rail transit systems have a certain lag. Taking the Xi’an metropolitan area as an example, this study analyzes the comprehensive accessibility and resilience of the multi-level rail transit network, and proposes an expansion plan accordingly. The traffic analysis zone (TAZ) is divided by towns and streets, and the relationship between points of interest (POIs) and the regional average level is analyzed using DEA. The improved weighted average travel time model is built with the analysis results as regional weights; a site selection model based on multiple construction influencing factors is proposed, and four expansion plans, namely, economic optimal, environmental optimal, transport optimal, and integrated optimal, are designed. The peak passenger flow scenario and the “failure–reparation” scenario during the entire operation period are designed to analyze the resilience of four plans, and the resilience is quantified by the elasticity curve of the maximum connected subgraph ratio (MCSR) changing over time. The research results show that the transport optimal plan has the best comprehensive accessibility and resilience, reducing travel costs in Houzhenzi Town, which has the worst accessibility, by 34%. The expansion model and evaluation method in this study can provide an empirical example for the development of other metropolitan areas and provide a reasonable benchmark and guidance for the development of multi-level rail transit networks in future urban areas.

Fair allocation of small-scale finfish mariculture zones in multi-use MPAs using multi criteria evaluation with stakeholder preferences

Mariculture zones in MPAs are primarily determined based on cost-effective/environmental criteria and rarely incorporate local community preferences. This study proposed a GIS-based site selection model to solve the conflicting issues in designing mariculture zones to support small-scale finfish farmers in MPAs. The work was undertaken in the Anambas Archipelago MPA to represent a commonly populated small-island MPA in Indonesia and other developing countries. A three-stage site selection model (constraint, site suitability, and stakeholder preference) with various criteria was employed using a modified parameter-specific suitability function (PSSF) with a non-weighted geometric mean. The stakeholder preference was used to counterbalance the dominance of environmental sub-models. The first stage analysis using the constraint sub-model determined that only 10.16% (1199.70 km2) of the total extent of the study area (11,811.77 km2) was categorized as feasible for finfish mariculture. The subsequent site suitability sub-model determined that only 32.3% (387 km2) and 30.1% (361.6 km2) of the feasible areas were classed best for finfish mariculture in dry and wet seasons, respectively. Areas classified as good covered 41.25% (494.9 km2) and 40.1% (481.65 km2) of the total feasible areas in the dry and wet seasons, respectively. The stakeholder preference sub-model had classified 33.21 km2 (2.77%) of the feasible area as the best sites that could be allocated as mariculture zones for the local community. The site selection model successfully incorporates multiple site selection parameters, addresses poor data availability and improves fair allocation of mariculture zone in the MPA via two specific approaches. The nature of the geometric mean prevents the allocation of suitable areas in the MPA-specific zones, such as core zones. Second, the stakeholder preference improves the fairness of the mariculture zone allocation due to the incorporation of local fish farmers' preferences. The proposed site selection model could be used to designate mariculture zones in a data-poor MPA environment and facilitate local communities in developing sustainable small-scale finfish mariculture in MPAs.

Study On Real Estate Location Based on GIS Technology

Through a review of relevant literature,this study categorizes factors affecting real estate site selection into three main categories: environmental, social, and economic, termed as the "triple bottom line." Based on the "triple bottom line," a PUM (Portfolio Underwriting Model) model was constructed. Meanwhile, GIS technology was utilized to identify the locations of public transportation stations, medical institutions, schools, and fire stations within Xiamen City. Additionally, different regions were delineated into suitable residential areas based on slope, aspect, land type, and per capita GDP. Combining the PUM model with GIS technology ultimately aids in real estate market site selection. The research findings indicate that integrating the PUM model and GIS technology for real estate site selection allows for a more scientific and comprehensive evaluation of various site selection factors, thereby providing important decision-making support for developers and investors.

Improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

A study on the macro-micro two-stage site selection of electric-hydrogen hybrid refueling stations based on GIS and Fuzzy-TODIM

With the increasing global demand for clean energy and sustainable development, hydrogen energy has gained significant attention as an environmentally friendly energy carrier. Hydrogen refueling stations, serving as crucial intermediaries in hydrogen production, storage, transport, and refueling, play a pivotal role in the hydrogen energy industry's layout. A novel model for hydrogen refueling stations based on DC microgrids utilizing renewable energy sources for hydrogen production and refueling has become a focal point of interest. However, there is currently limited research on the site selection of electric-hydrogen hybrid refueling stations. Addressing current needs and research gaps, this study proposes a macro-micro two-stage site selection model based on DC microgrids. In the macro-level site selection stage, factors such as regional photovoltaic and wind resources, hydrogen energy policies, and the layout of the hydrogen energy industry are comprehensively considered, with Guangdong Province, China selected as the study area. ArcGIS is utilized to create thematic layers and establish constraints. The DEMATEL method is employed to determine the weights of macro-level site selection criteria. The results indicate that the most suitable areas are located in coastal regions. This study's site selection model offers a scientific and systematic approach to evaluating electric-hydrogen hybrid refueling stations in DC microgrids, providing a scientific basis for decision-making for governments and investors.

Method for selecting emergency material reserve bases in chemical industry parks under typhoon scenarios

Rationally selecting emergency material reserve bases (EMSBs) for chemical industry parks is crucial to ensure timely emergency responses during major accidents. To identify practical site selection schemes for EMSBs under typhoon scenarios, a prioritization/sorting site selection model considering typhoon disasters for EMSBs is developed. Firstly, an improved k-means clustering algorithm is employed to cluster typhoon paths and classify them into several critical typhoon scenarios, which are used to assess the risk of typhoon disasters affecting chemical industrial parks in specific areas. Then, according to the region's functional characteristics of EMSBs, candidate areas with emergency capabilities are identified through kernel density analysis, and safe candidate areas are further determined based on the identified typhoon scenarios. Finally, the safe candidate areas are ranked based on emergency path distances, and the preferred site selection areas are ultimately obtained through administrative planning analysis of the top-ranked areas. Furthermore, a case study is conducted based on data from Zhejiang Province, verifying the effectiveness of the decision-making method.

Distributed Photovoltaic Site Selection and Capacity Planning Considering Power Mutual Support in Multiple Power Distribution Areas

Abstract With the rapid development of local economy, the form of independent power supply from a single power supply in the power distribution area is difficult to meet the demand for high reliability. The interconnection and power mutual assistance planning of power distribution areas under large-scale distributed photovoltaic access have attracted widespread attention. Due to the randomness and volatility of distributed photovoltaics, its different access methods and capacities will affect the safe and stable operation and power mutual assistance capabilities of the distribution area. In order to study the site selection and capacity planning problem of distributed photovoltaics, this paper builds a site selection and capacity model based on the three-phase power flow equation with the maximum external power of the station area as the optimization goal and the safe operation of the distribution area as constraints. Furthermore, an optimization method for distributed photovoltaic site selection and capacity based on improved particle swarm algorithm is proposed. Finally, this paper verified the effectiveness of the optimization method through simulation analysis on the IEEE33 three-phase grid model. The simulation results show that this method of site selection and capacity determination can achieve maximum power transmission in the power distribution area while ensuring the safe and stable operation of the area.

Mapping Terrestrial Conservation: Showcasing Model Assessment for Map Performance Evaluation

Habitat preservation, conservation, and protection of threatened species require careful site selection procedures. The presence of several site-selection models encourages model assessments to determine their usefulness and functionality for conservation purposes. Map model assessments are not conventionally conducted between site selection models to understand the nature of their decision-making strategies and prioritization. Therefore, the objective of this study was to determine the relationship between a recently published site selection model, the Safe Zone with MaxEnt, representing the Species Distribution Model, and the Forest Landscape Integrity Index, representing an intact forest habitat. As expected, there was considerable variation among the three models due to their different algorithms and themes of concern; nevertheless, these had some level of agreement in selecting potential areas for conservation. In addition, this study presented a convenient method for map data collection and the analysis required for a comprehensive understanding of the model agreements and relationships with other models. The nature of the decision making of the Safe Zone model and its potential utility for selecting Strict Protection Zones was presented. This study highlighted the intersection of geospatial analysis, conservation, and model evaluation. Finally, study emphasized the importance of informed decision-making in environmental planning and management through rigorous map assessment. The limitations of this study include the varied pixel resolutions of global and local maps, temporal predictions, and the fact that the models to which the SZ was compared are limited to models and maps available in the area of interest.

Site selection and prediction of urban emergency shelter based on VGAE-RF model

As urban development accelerates and natural disasters occur more frequently, the urgency of developing effective emergency shelter planning strategies intensifies. The shelter location selection method under the traditional multi-criteria decision-making framework suffers from issues such as strong subjectivity and insufficient data support. Artificial intelligence offers a robust data-driven approach for site selection; however, many methods neglect the spatial relationships of site selection targets within geographical space. This paper introduces an emergency shelter site selection model that combines a variational graph autoencoder (VGAE) with a random forest (RF), namely VGAE-RF. In the constructed urban spatial topological graph, based on network geographic information, this model captures both the latent features of geographic unit coupling and integrates explicit and latent features to forecast the likelihood of emergency shelters in the construction area. This study takes Beijing, China, as the experimental area and evaluates the reliability of different model methods using a confusion matrix, Receiver Operating Characteristic (ROC) curve, and Imbalance Index of spatial distribution as evaluation indicators. The experimental results indicate that the proposed VGAE-RF model method, which considers spatial semantic associations, displays the best reliability.

Designing compact, connected and gap-free reserves with systematic reserve site selection models

Protected areas play a crucial role in current global policies to mitigate the erosion of biodiversity and systematic reserve site selection models are increasingly involved in their design. These models address the optimisation problem that seeks to cover spaces hosting biodiversity features with nature reserves at a minimum cost for human activities. To increase the likelihood of a successful implementation, reserves need to be spatially consistent. Widely used decision support tools such as Marxan and PrioritizR commonly enforce compactness indirectly by penalising the reserve perimeter in the objective function. Few other optimisation models explicitly consider spatial properties such as limited fragmentation, connectivity of selected sites, and buffer zones around them, etc. So far, no reserve site selection model can guarantee the production of a connected, compact, and gap-free reserve all at once. The impossibility of designing reserve solutions with desirable spatial properties using existing models makes it difficult to implement such solutions in the real world. Therefore, we propose a mixed-integer linear program to build a reserve that is connected, compact, and gap-free. To enforce these spatial attributes within a reserve site selection model, we used a multicommodity flow approach. We tested the computational feasibility of our model on generated instances and the real instance of Fernando de Noronha. The results indicate that a single model can be used to enforce compactness, connectivity, and the absence of gaps. Using this optimisation model, conservation practitioners can design reserve solutions with desirable spatial properties, thereby increasing the likelihood of a successful implementation.

Marine aquaculture spatial planning on market orientation for Pacific oyster in Shandong, China

Accessing the production value is essential to the selection of suitable sites for aquaculture farms. This study employed a Dynamic Energy Budget (DEB) model and used market-oriented indicators, including shell length, flesh weight, condition index and minimum farming duration, to analyze the suitability for Pacific oyster aquaculture in offshore Shandong, China. Chlorophyll a (Chl-a) concentration, Total Suspended Sediment (TSS) concentration, and Sea Surface Temperature (SST) were extracted by remote sensing and used as forcing variables in the bioenergetic model. Four market-oriented indicators and five zones in the study area were chosen to develop a Suitable Market-Aquaculture site-selection model (SMASM) and estimate the oyster farming suitability. The results demonstrated that Sanggou Bay exhibits high potential for Pacific oyster year-round aquaculture. Eastern Laizhou Bay is suitable for the autumn-spring fattening culture of adult oysters. Laoshan Bay and Changshan Islands are suitable for promoting rapid growth in juvenile oysters. The results of our model were consistent with the oyster production value in Shandong, showing an overall increasing trend except for the anomalous value observed in 2019. Sudden drops in water temperature, red tides, and significant increases in turbidity can exert detrimental effects on oyster production, which has been evidenced to be influenced by Super Typhoon Lekima in 2019. Although this study concentrated on a specific geographical region, the model could be applied to other regions to develop market-oriented suitability management for species and environments worldwide.

Enhancing Logistics Optimization

With the expansion of the logistics network, enterprise logistics distribution faces increasing challenges, including high transportation costs, low distribution efficiency, and unstable distribution networks. To address these issues, this study focuses on optimizing enterprise logistics distribution using a double-layer (DL) model. In this paper, we propose a DL model for optimizing enterprise logistics distribution. The DL model is designed to find the optimal solution using the particle swarm optimization (PSO) algorithm. By leveraging location data from the region, the DL model evaluates and compares alternative distribution centers to determine the most efficient distribution strategy. The results demonstrate that the DL site selection model developed in this study effectively addresses the tasks of logistics center location and distribution optimization among alternative distribution centers. Comparison tests reveal that the distribution path proposed by the DL model is more accessible and cost-effective compared to alternative approaches.

Towards Sustainable Urban Futures: Integrating a Novel Grey Multi-Criteria Decision Making Model for Optimal Pedestrian Walkway Site Selection

Today, urban development faces complex global challenges that require new approaches to influence its practices and policies. By prioritizing sustainability, mobility, and community resilience, we are moving towards a holistic approach that protects the well-being of residents and the environment. In this dynamic urban design environment, finding ways to identify pedestrian-friendly spaces and create safe, efficient, and sustainable urban environments is a major challenge, and it requires research that seeks to optimize this choice, especially in terms of integrating numerical modeling methods and a wide range of measurement tasks. This research is concerned with optimizing pedestrian accessibility, which directly contributes to reducing environmental impact, improving non-motorized transport, and reducing traffic congestion. It integrates a novel, mixed multi-criteria decision-making (MCDM) model that benefits from the use of Grey’s theory. This research’s purpose goes beyond addressing cities’ immediate challenges; it seeks to contribute to a broader understanding of sustainable urban planning. This study represents a pioneering effort to identify the challenges of optimal site selection for pedestrian walkways. By using a multi-criteria decision-making approach based on mathematical modelling and facility engineering, this effort addresses gaps in the current understanding of site selection criteria and aims to lead a paradigm shift toward a sustainable, efficient, and inclusive urban environment. The results show that the updated RANCOM-PIV greyscale is quite robust and reliable, when comparing the final results of the PIV greyscale and the MARCOS greyscale.

Deployment Method for Aircraft-Based Maritime Emergency Communication Resource Reserve Bases

Maritime emergency communication facilities play a crucial role in establishing communication links between land and sea, serving as essential communication means for maintaining maritime safety, disaster response, and emergency rescue operations. With the increasing frequency of marine activities, the rapid response capability of maritime emergency communication is becoming increasingly critical. With their characteristics of high-speed mobility, broad coverage and flexibility, aircraft serve as carriers for emergency communication facilities. The selection of aircraft bases is paramount in meeting the requirement of rapid response for maritime emergency communication. In this paper, we present a multi-objective optimization model for site selection by considering the coverage capabilities of different carriers. The model incorporates hierarchical coverage of unmanned aerial vehicles (UAVs) and helicopters with a genetic algorithm. Through a case study of the Bohai Sea, this paper verifies the feasibility and effectiveness of the model.

Optimizing the Layout of Service Facilities for Older People Based on POI Data and Machine Learning: Guangzhou City as an Example

Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people.

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.

Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters.

The Advantages of the OWA (Ordered Weighted Averaging) Method in Decision Making and Reliability Testing of Spatial Multi-Criteria Site Selection (SMCSS) Model

The Advantages of the OWA (Ordered Weighted Averaging) Method in Decision Making and Reliability Testing of Spatial Multi-Criteria Site Selection (SMCSS) Model

Research on the Construction of Emergency Relief Center Site Selection Model Based on Firefly Algorithm

Aiming at the problem of site selection of rescue center under multi-objective after natural disaster, this paper constructs a site selection model to minimize the freight cost and maximize the time satisfaction of disaster victims. Firstly, the site selection model framework will be constructed according to the objectives, secondly, the model framework will be set up with specific constraints, and finally, the improved firefly optimization algorithm (SGSO) will be applied to conduct virtual simulation experiments. The SGSO algorithm is only used with the theory of the set of good points for the algorithm to initialize the population, and at the same time, it combines the inverse chord function with the step convergence factor, and it designs a formula for updating the position of the individuals in the population. On the basis of passing 8 standard test functions, virtual simulation experiments are carried out, and it is concluded that the SGSO algorithm can effectively solve the multi-objective siting problem, and provide more ideas and guarantees for the future research.