Site Selection Method Research Articles

Research on Low-Carbon Site Selection Decision Model and Algorithm for Green Logistics

This work proposes a low-carbon site selection decision model and algorithm for green logistics. The focus is to minimize carbon emissions while optimizing the distribution network. With the development of smart shared logistics and increasing environmental awareness, there is an urgent need to tackle issues in urban logistics, including pollution, resource inefficiency, high carbon energy usage, and greenhouse gas emissions. By integrating geographic, environmental, and operational data, the proposed site selection model incorporates multi-objective optimization to balance carbon efficiency with cost-effectiveness in logistics site selection. We employ a hybrid algorithm combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to solve the complex decision-making process. The model is tested using real-world logistics data, and the results demonstrate significant reductions in carbon emissions and transportation costs when compared to conventional logistics site selection methods. Further sensitivity analysis reveals that the model is robust under varying transportation demands and geographic constraints. Also, we highlight the role of green logistics in achieving corporate sustainability goals aligning with global carbon neutrality initiatives. We believe this model offers a practical approach to fostering environmentally responsible supply chains and will contribute to the advancement of sustainable logistics operations, helping organizations make informed, eco-friendly decisions in site selection.

Optimizing site selection for construction and demolition waste resource treatment plants using a hesitant neutrosophic set: a case study in Xiamen, China

The site selection of construction and demolition waste resource treatment plants is a complicated multi-criteria decision-making (MCDM) issue of ‘not in my back yard’ (NIMBY) facilities, which is affected by spatial, economic, societal, environmental and other factors. Traditional site selection MCDM methods expose the shortcomings of evaluation information representation and spatial factor analysis. This study develops an optimized MCDM site selection method in a hesitant neutrosophic set (HNS) environment, capable of effectively handling uncertainty and fully incorporating spatial considerations. The innovation of this research lies in integrating HNS into MCDM, combined with geographic information system technology, revised DEMATEL-based analytic network process method, cosine similarity and Euclidean distance similarity measures. A case study in Xiamen, China, validates this method’s effectiveness, and sensitivity analysis and comparative studies demonstrate its superior stability, robustness and resolution. The main contribution of this study is the development of an MCDM optimization method for siting large-scale NIMBY facilities.

Research and Modeling of Commercial Location Selection Based on Geographic Big Data and Mobile Signaling Data—A Case Study of the Central Urban Area of Beijing

The layout and site selection strategy of commercial facilities are crucial for both enterprise performance and market image, while also significantly impacting the overall planning of urban commercial environments. However, conventional methods of choosing sites sometimes depend on outdated management information systems or static statistical models, which may not take into account all relevant factors and have poor data quality. By utilizing geographical big data and geographical artificial intelligence, this study improves the viability of commercial layout and site selection methods. This study utilizes mobile phone signaling data from Beijing combined with point-of-interest (POI) data from within the Sixth Ring Road of Beijing to identify user behaviors using algorithms. Through a combination of BiLSTM-RF and reinforcement learning algorithms, a population location prediction algorithm is constructed to address the issues of inaccurate and outdated population flow data in commercial site selection. The forecast distribution has a high level of accuracy, with a prediction accuracy rate of 73.2%. Additionally, based on geographical big data, the urban landscape is reconstructed to create a 3D model of Beijing. An immersive interactive commercial site selection system is implemented using the Unreal Engine.

A Localized GIS-Based Multi Decision Criteria Approach in Determining Suitable Sites for Community Toilet Facilities in BASECO Compound, Manila City

Abstract. Access to sanitation remains a major concern in the Philippines due to the increasing population and growing demand for basic needs. This study addresses poor sanitary conditions in slum areas, specifically in BaSECo Compound, Manila, where informal settlers rely on shared toilets. Utilizing GIS and MCDA, the study identifies areas suitable for community toilet facility (CTF) development. The Modified Huff Three-Step Floating Catchment Area (MH3SFCA) method quantified spatial accessibility to existing toilets. The SPAI map shows low accessibility in blocks 15 and New Site, with Gawad Kalinga, Habitat, and Old Site (N) having none. The Analytical Hierarchy Process (AHP) prioritized six criteria, with accessibility being the most significant (33.18%), followed by proximity to water supply (31.76%), communal septic tanks (14.53%), land use (8.2%), flood-prone areas (7.44%), and ground slope (4.89%). The final suitability map identified 14.12% of the 0.6 sq. km. area as highly suitable, including Block 15, Gawad Kalinga, Habitat, and Old Site (N). Ground truth verification visited five candidate sites, which were tested for improved SPAI maps. The inclusion of facilities in Block 15, GK 2, OSN, and H improved accessibility in northern BaSECo. Significant service area improvements for each CTF were observed using Voronoi polygons. This study demonstrates the effectiveness of integrating GIS and MCDA methods for CTF site selection.

Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.

Site Selection of Large Shopping Malls in Wanbailin District of Taiyuan City

The construction and site selection of shopping malls are directly related to their commercial operation effectiveness and market competitiveness. Some scholars have conducted extensive research in this field, particularly achieving notable results in terms of influencing factors, methodologies, and technological applications for site selection, yet there are still some deficiencies. Traditional site selection methods primarily rely on qualitative analysis and empirical judgment. However, with technological advancements, Geographic Information System technology has gradually been applied to shopping mall site selection. GIS technology enables the integration of multiple data sources, spatial analysis, and visualization, providing a scientific basis for site selection decisions. This article utilizes ArcGIS software to construct an application model for shopping mall site selection in Wanbailin District. Through spatial analysis, eligible areas are identified, achieving a scientific and visual approach to site selection. By considering the main factors affecting the location of large shopping malls in Wanbailin District and using ArcGIS as the basic platform, an application model is built to find the optimal location for building shopping malls. The results show that introducing GIS into mall location planning improves efficiency and accuracy while providing a reliable basis for scientific location.

Sustainable solution to the critical challenges of an oceanic wave farm for maximizing power generation

In this paper, a general wave farm design procedure has been presented. For example, the design procedure is applied to the Bay of Bengal, Bangladesh. Firstly, the most effective site is selected to construct a wave farm considering various techno-economic factors. A coordinated drawing for the proposed site selection procedure is presented, which includes a coastal area map and sea, grid map, wave height, and different assorted factors. Transmission line cost is analyzed for nearly equipotential locations. Then wave height, time period, wave power, and vertical speed are observed for the selected location. In the next step, a wave farm layout has been designed considering point absorber type device and the oceanic wave dataset with statistical analysis. Further, a linear electrical generator (LEG) is designed and analyzed for the wave farm. The generator is simulated in the ANSYS/Maxwell environment. Since the wave parameters vary from time to time, the LEG is simulated for several vertical speeds. To find the highest output power, the load profiles are analyzed for each speed separately. In the next step, the LEG is optimized. Simulation result shows that the flux distribution of optimized LEG is better than its initially designed counterpart. Efficiency of the optimized LEG is 7 % greater than its initial design. The proposed method of site selection, statistical analysis of the wave dataset, and the optimized LEG design are combined in this paper for maximum wave energy extraction.

An Optimized Site Selection Method for Human-Life Search and Rescue in Inland Waters

An Optimized Site Selection Method for Human-Life Search and Rescue in Inland Waters

Avoid non‐probability sampling to select population monitoring sites: Comment on McClure and Rolek (2023)

Abstract Population monitoring programmes typically rely on sampling because it is impossible to survey all the sites within the study area. In such a situation, the general recommendation to obtain unbiased estimates of population trends is to select monitoring sites using probability sampling. However, site selection not based on probability sampling, such as selecting sites with the largest abundance of individuals at the beginning of the monitoring programme, is common in practice. Nevertheless, these methods carry the risk of obtaining biased trend estimates. Using simulations, McClure & Rolek (2023) investigated whether three non‐probability sampling site selection methods can yield unbiased trend estimates under some specific conditions. For two of these methods, that is selecting high quality sites and selecting sites known to be occupied, the authors conclude that there is a major risk of obtaining biased trend estimates. For the third method, that is selecting sites with the largest initial abundance, they found conditions in which unbiased estimates can be obtained. They conclude that the general recommendation to use probability sampling should be revised. Here, we show that the authors' results, although perfectly correct, do not invalidate this recommendation. First, we point out that the authors made strong assumptions about the populations' functioning in their simulations, especially that inter‐annual variance in abundance is similar for all sites, which is unlikely in most real populations. We show through simple simulations that even slightly relaxing this assumption invalidates the authors' results. We also point out that for most of the hypotheses made by the authors, it is generally not known at the beginning of a study whether they will be respected. Furthermore, the authors did not provide evidence that selecting sites based on high initial abundance leads to more precise trend estimates than probability sampling methods. Therefore, neither the benefits nor the risks of this method are known. We conclude that until evidence is provided that abundance‐based site selection improves estimate precision and the situations in which it provides unbiased estimates are clearly identified, using probability sampling should remain the rule.

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.

G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction.

G protein-coupled receptor (GPCR) transmembrane protein family members play essential roles in physiology. Numerous pharmaceuticals target GPCRs, and many drug discovery programs utilize virtual screening (VS) against GPCR targets. Improvements in the accuracy of predicting new molecules that bind to and either activate or inhibit GPCR function would accelerate such drug discovery programs. This work addresses two significant research questions. First, do ligand interaction fingerprints provide a substantial advantage over automated methods of binding site selection for classical docking? Second, can the functional status of prospective screening candidates be predicted from ligand interaction fingerprints using a random forest classifier? Ligand interaction fingerprints were found to offer modest advantages in sampling accurate poses, but no substantial advantage in the final set of top-ranked poses after scoring, and, thus, were not used in the generation of the ligand-receptor complexes used to train and test the random forest classifier. A binary classifier which treated agonists, antagonists, and inverse agonists as active and all other ligands as inactive proved highly effective in ligand function prediction in an external test set of GPR31 and TAAR2 candidate ligands with a hit rate of 82.6% actual actives within the set of predicted actives.

A Simulation Method of Site Selection for Liquid Ammonia Tanks

A Simulation Method of Site Selection for Liquid Ammonia Tanks

Electrochemical Allylic C(sp3)-H Isothiocyanation via [3,3]-Sigmatropic Rearrangement.

The direct allylic C(sp3)-H functionalization provides a straightforward protocol for the synthesis of valuable molecules. We report herein the first chemo- and site-selective method for allylic C(sp3)-H isothiocyanation of various internal alkenes under mild electrochemical conditions. This method exhibits broad functional group tolerance and excellent selectivity and can be applied for late-stage isothiocyanation of bioactive molecules. Combined experimental and computational studies indicate that the reaction proceeds via an unexpected [3,3]-sigmatropic rearrangement.

CiteSpace-based Logistics Distribution Center Site Selection Research

: China's logistics industry is developing at a high speed, but the distribution efficiency can't match with it, to reduce the waste of resources and costs on the way of distribution, it is crucial to study and improve the distribution center siting issue. Taking the 494 core journals on the study of logistics distribution center siting in Knowledge Network as the basic data, CiteSpace is used to visualize and analyze the related literature in this research field. Through the keyword clustering analysis, this field can be divided into four directions: the main body of research, the influence factors of site selection, the purpose of site selection, and site selection methods, and the comprehensive analysis shows that the early focus of China's research is on the site selection methods, such as genetic algorithm and center of gravity method, and the future research direction is the joint optimization of the site selection-path and the two-layer planning.

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.

Applying analytic hierarchy process for site selection of a recreational-educational children complex in Shiraz City, Iran

The development of educational-recreational complexes for children has a significant impact on their cognitive growth and behaviour. Proper site selection is essential to ensure the success of such projects. This paper aims to identify the best location for a children’s educational-recreational complex in Shiraz, Iran. The study used a descriptive-comparative and survey method to determine effective criteria for site selection. A questionnaire was administered to experts in architecture, urban design, and planning to identify appropriate sites. The Analytic Hierarchy Process (AHP) method was then employed to select the best location. The hierarchical model included three levels: goal (educational-recreational site selection), criteria (natural potential, accessibility, land size, neighbourhood and social context, expansibility, appropriate land use (the neighbourhood between proposed and master plan land use)), and three alternative sites. The results showed that a site located near residential areas, green spaces, and natural elements is suitable for the development of an educational-recreational complex for children. The study recommends the use of the SWOT (strength, weaknesses, opportunities and threats) - AHP method for site selection in children’s complexes. The paper highlights the need for a reliability test or table/figure to support the research findings.

Influence of the Seabed Site and Island Structure on Seismic Response of Marine Artificial Islands

Compared with the marine structures such as sea-crossing bridges and offshore wind turbines, the marine artificial islands are bottom sitting structures and particularly sensitive to site conditions. Therefore, the study focuses on determining the effect of seabed site conditions and construction methods on the seismic performance of marine artificial islands. A seismic wave analysis model of seabed-seawater layer–artificial island coupling is developed by combining the self-programmed fluctuation analysis program and the dynamic analysis software. Firstly, the seismic response of marine artificial islands with different site conditions are analyzed by inputting P and SV waves. On that basis, the effects of incidence angles on the seismic response are explored. Then the study further explores the effects of different backfill materials and protective structures on the seismic response of the artificial island. Numerical results show that the silty soft soil layer can amplify the seismic response of seabed sites, and this amplification effect increases with the increase of the depth of silty soft soil layers. Moreover, the coral sand as backfill material can reduce the seismic response of marine artificial islands. The seismic response of marine artificial islands can also be reduced in the model with a protection structure. These results will provide some theoretical references for the site selection and construction method of marine artificial islands.

Sequential MCDM methods for site selection of hydroponic geothermal greenhouse: ENTROPY and ARAS

With the increasing global population, there is a growing need for agricultural land and an associated impact on greenhouse activities. In response to issues of limited energy and natural resources, the use of hydroponic greenhouses that use renewable energy sources has become increasingly popular. Among these, the use of geothermal energy in hydroponic greenhouses has garnered particular attention. In this study, we propose a sequential solution method that incorporates the Entropy and ARAS methods for the location selection of hydroponic greenhouses heated by geothermal energy. The model considers 5 main criteria, and 21 sub-criteria, and evaluates five alternative cities in Turkey. The weighting of the criteria was established through the Entropy technique. Subsequently, the decision alternatives were ranked using the ARAS method. The results of the study revealed that Denizli province is the most appropriate location for the establishment of a hydroponic geothermal greenhouse in Turkey. The alternative cities of Aydin and Afyonkarahisar were found to be the second and third most suitable provinces respectively. This determination was made based on a thorough evaluation of various factors such as geothermal resources, land availability, and infrastructure. The results of the study align with Turkey's potential for using geothermal energy and its existing greenhouse areas. This study highlights the potential for using geothermal energy in the cultivation of crops in Turkey and suggests that the establishment of hydroponic geothermal greenhouses in these provinces can be a viable and sustainable solution for increasing food production. The proposed sequential solution method may serve as a valuable tool for guiding investment decisions related to geothermal greenhouse applications in other countries.

Waste Management: Landfill Selection Using Gis and Analytical Hierarchy Process

The widespread use of landfilling as a convenient waste management method has become unsustainable due to the rapid development causing a surge in waste generation, threatening the lifespan of existing landfills. The closure of landfills presents waste management challenges, necessitating more extensive garbage disposal facilities to accommodate the growing urban population. However, poor site selection methods can have a negative impact on both the environment and human health, which goes against the objectives of sustainable development goal (SDG). Methods like Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) facilitate decision-making processes. This integration reduces analysis time and costs enhances accuracy and minimizes errors. The Analytical Hierarchy Process (AHP) provides a framework to validate judgments and derive measurable values in decision-making involving theories and concepts. In this study, several criteria were used to create a final suitability map which is the distance from the residential area, distance from the plantation area, distance from the main road, distance from waterways, type of soil, slope, and land cover. The results indicate that distance from residential has the highest weightage of importance, which is 27%. It also found that the highly suitable new landfill area is located at Batu in Kuala Langat, Pulau Carey, and Bukit Rotan in Kuala Selangor.

Using GIS-supported MCDA method for appropriate site selection of parking lots: The case study of the city of Tetovo, North Macedonia

The provision of adequate parking spaces for vehicles has emerged as a prominent and challenging issue confronted by towns, cities, and municipal authorities in recent years. Addressing this problem necessitates a thorough examination of the prevailing physical conditions in existing parking areas, while simultaneously undertaking analyses to identify suitable locations for new parking areas or parking lots. This study focuses on the city of Tetovo, North Macedonia, investigating and assessing the available parking areas while analyzing potential sites in accordance with the city's needs and requirements. To facilitate decision-making, a Multi-Criteria Decision Analysis (MCDA) approach is employed to address the parking site selection analysis problem. The weightage of criteria utilized in the analysis is estimated, and potential parking solutions or site selections for new parking areas are identified through the combined application of Geographical Information System (GIS) and Analytic Hierarchy Process (AHP) techniques, identifying primary and sub-criteria, with a focus on Land Use and Transportation as the main criteria for selecting parking lots. The integration of GIS and AHP offers an effective and optimal methodology for site selection and identifying suitable parking locations. AHP method, applied to criteria, determined relative weights through expert opinions, while GIS facilitated spatial analysis for identifying suitable parking locations. The study identifies accessibility to main roads as the criterion carrying the greatest weight (0.517), while accessibility to cultural facilities holds the lowest weight (0.117). The study serves as a pivotal resource for sustainable urban management and decision-making, providing insights into future urban planning and the identification of suitable parking lot sites to foster sustainable development within the city.