Site Selection Model Research Articles

A multicriteria small modular reactor site selection model under long-term variations of climatic conditions -- A case study for the province of Saskatchewan, Canada

Abstract Nuclear power has the potential to provide a clean solution to relieve environmental pressures caused by increasing fossil fuel consumption. As an emerging technology, small modular reactors (SMRs) are developed to tackle global needs for providing safe, clean, and economic energy. The identification of suitable sites for SMRs, which is a multifaceted process and includes multiple considerations, is a crucial step for their development. In this study, a climate-oriented SMR site recognition (CSSR) approach that reflects the long-term effects of climate change is developed to identify potential sites that are suitable for SMR placement. Specifically, annual mean temperature and annual maximum consecutive 10-day precipitation are considered under two future periods (i.e., 2040–2069 and 2070–2099) and two Representative Concentration Pathways (i.e., 4.5 and 8.5). In addition, economic and social factors, including transmission lines density, highway network density, distance to water bodies, population density and northern municipalities distribution are taken into account. To demonstrate the advantages of the proposed approach, a case study for Saskatchewan, Canada is conducted. It is expected that this study can provide a practical methodological framework for site identification of power plants under long-term variations of climatic conditions and can support Saskatchewan in initiating its nuclear power.

Study on site selection of municipal solid waste incineration plant based on swarm optimization algorithm.

In this paper, based on an improved swarm optimization algorithm, a new site selection model of a municipal solid waste (MSW) incineration plant is proposed. First, the whale optimization algorithm and particle swarm optimization algorithm are combined according to certain rules to improve the performance of the hybrid algorithm. Through a verification of the single and multi-peak functions, the results show that the algorithm achieves a good performance. The location model of the MSW incineration plant is based on many factors, including the economy, environmental protection, population scale, and operation cost. Finally, based on a sample analysis, a new location model of an MSW incineration plant is used to select the location of an Anshan MSW incineration plant, and a reasonable location is obtained.

Environmental Habitat Mapping of Green Mussel: A GIS-Based Approach for Sustainable Aquaculture in the Inner Gulf of Thailand

The green mussel (Perna viridis) is one of the most commercially-important cultured species along the coast of Thailand. In this study, a suitable aquaculture site-selection model (SASSM) was developed to identify the most suitable areas in the inner part of the Gulf of Thailand (InnerGoT) for green mussel culture. Satellite-derived chlorophyll-a (Chl-a) and hydrodynamic model outputs for sea surface temperature (SST), salinity, maximum water current (MWC), and bathymetry between 2018 and 2019 were used as input to the SASSM. The results show that suitability scores in mussel aquaculture areas were lowest (1–3) during the Southwest (SW) monsoon, rainy season (July–August), and highest (6–7) during the Northeast (NE) monsoon, cold season (November–December). Moderate suitability scores (4–5) were obtained during the monsoon transition from the NE monsoon to the SW monsoon, summer (April–May). The study area was further divided into three zones: the western, central, and eastern regions. The western and eastern parts showed high suitability scores (5–7) while the central zone exhibited low suitability scores (2–4). The model results show a similar pattern to the actual mussel production in the study area. Seasonal events (i.e., flood and dry seasons) were incorporated into the model to examine the seasonal effects on the suitable mussel aquaculture areas. The suitability scores during the SW monsoon in 2018 were more sensitive to changes in SST and salinity relative to 2019. The higher freshwater discharge and lower temperature in 2018 relative to 2019 resulted in the accrual of suitable aquaculture areas. This pattern is consistent with the productions of the green mussel, where higher production was recorded in 2018 (2002.5 t) than in 2019 (410.8 t). However, correlations among atmospheric (air temperature, rainfall, and wind) and oceanographic factors (SST and MWC) were significant in the western and central regions, suggesting that the suitability of green mussel aquaculture in these regions is vulnerable to environmental disturbances. Thus, the SASSM can be a powerful tool in providing useful information on spatial management for marine aquaculture in environmentally-dynamic coastal systems.

Building a grey-based multi-criteria decision-making model for offshore wind farm site selection

The selection of suitable sites is a critical step for the successful development of offshore wind power. This study aims to establish an assessment framework to select the optimal alternative under multiple conflicting criteria. A hybrid model that combines the grey decision-making trial-and-evaluation laboratory-based analytic network process (grey DANP) and probability-based grey relational analysis (P-GRA) methods is proposed. Grey DANP can not only map the influential network relationship of the criteria, but also generate a set of criteria influence weights. In addition, P-GRA, which uses the concept of interval analysis, can be used to determine the gap between the alternatives and the ideal referential solution (IRS). P-GRA retains the interval value type in the calculation process to avoid the loss of potential information. The proposed model optimizes the traditional DANP and GRA methods, which improving their effectiveness and practicability for practical applications. This paper contributes to solving the problem of farm site selection based on the potential for sustainable development and the novelty of the analysis model. A case study of a Taiwan example is used to examine the feasibility of the proposed model. Finally, a sensitivity analysis was conducted to demonstrate the robustness and innovativeness of the proposed model.

Optimization research on the site selection of fire safety for mega projects sites based on multi-objective particle swarm

There are large fire safety hidden dangers in the construction site of mega-projects. In order to improve the ability of fire safety emergency response on site, in this paper, the number of demand points on the construction site are firstly determined, and through using risk assessment of operating conditions method, the risk is evaluated and the risk level is determined. Secondly, according to the construction site layout criteria and fire safety technical criteria, and taking the economy, distance, time and coverage of fire safety site selection as the basic factors, a multi-objective site selection optimization model for fire safety points is established. Multi-objective particle swarm optimization is used to solve the multi-objective site selection model, and a series of fire safety point site selection schemes are obtained. Finally, the analytic hierarchy process is used to select the best scheme from a series of schemes. The research ideas and conclusions of this paper provide a scientific and reasonable analysis framework and ideas for site selection of fire safety points for mega projects, which has certain applicability and practicability.

MCDM Model for Natural Gas Pressure Reducing Station Site Selection

Considering the increasing scenario of natural gas consumption, it is necessary that all agents in the chain use methods that structure decision-making and problem-solving processes. This paper proposes a multicriteria decision model to solve a site selection problem for a pressure reducing station. A natural gas distribution company was selected to test the model and the preference modeling was conducted through the flexible interactive tradeoff (FITradeoff) approach, according to the preferences of the decision maker (DM). FITradeoff's decision support system was used to assess the alternatives of the model, through the inference of the criteria scale constants. The results proved the robustness of the model and the DM evidenced consistency in its preferences. Also, the FITradeoff method demonstrated to be intuitive to apply, since a smaller effort is required from the DM and this is because the procedure does not require complete information in the scale constants elicitation process.

A Heuristic Approach to Siting and Design Optimization of an Onshore Wind Farm Layout

The forecasted electricity demand in Saudi Arabia may be around 120 GW/year by 2032. As per the latest government announcement, Saudi Arabia is aiming to install 57.5 GW of renewable energy capacity by 2030. In this study, firstly, a wind map is developed based on the historical wind data, recorded over a 39-year period, followed by the development of the geographic information system (GIS)-based multi-criteria decision making (MCDM) model for suitable wind farm site selection for Hijaz, the western region of Saudi Arabia. This region is selected as it has a population density of around 25 per sq. km, the highest in Saudi Arabia. For the model, data from various ecological, environmental, and socioeconomic criteria are considered. Finally, the optimization of the wind farm layout on the identified suitable region of 5.5 km × 4 km is performed using the deep-array wake model, DAWM. The optimized layout has locations for 30 wind turbines of 3 MW rated capacity. This optimization process minimizes energy losses and costs and maximizes power production. The net and gross energy production from the wind farm are expected to be 143 GWh and 156 GWh, respectively, with an array loss of 8.25% at a cost of energy of USD 65.66 per MWh, and a capacity factor of 17.7%. The cost calculations include the capital cost of constructing the access roads and a complete collector system with two substations. The optimized turbine positions in the layout have a major and minor axis separation of 1680 m and 448 m, respectively.

A SOP planning method for distribution network based on improved genetic algorithm considering EV integration

With the progress of electric vehicles, there comes a huge number of problems about cost inflation and workload increases in power distribution systems, which caused by the growing number of electric vehicles plugging. Hence, an optimal planning scheme of soft open point (SOP) considering the connection of electric vehicle to power distribution system is proposed. First of all, a SOP two-layer planning model for site selection and capacity determination was established to seek for site selection and capacity determination, so as to minimize the cost. The hybrid optimization algorithm composed of improved genetic algorithm and power flow algorithm was used to solve the problem. Further, the variation of voltage level, before and after the SOP connected to the distribution network, is also analyzed. Finally, the rationality and feasibility of the two-layer programming model of SOP site selection and constant capacity and the hybrid optimization algorithm composed of improved genetic algorithm and power flow algorithm are analyzed with IEEE 33 node example system, and the planning conclusion of effectively reducing the operating cost of the distribution system is obtained. After plugging in SOP according to the planning results, the node voltage value, the average per unit value and the performance of the distribution system are improved to a certain extent. Meanwhile, the operation efficiency, relative error and the convergence capacity according to the modified algorithm are superior to conventional algorithm.

Site Selection for Pre-Hospital Emergency Stations Based on the Actual Spatiotemporal Demand: A Case Study of Nanjing City, China

Rapid economic and social development has been accompanied by the occurrence of many major issues throughout the world. Specifically, there is an ever-increasing demand for emergent medical services among the public. In order to ensure timely responses to emergency demands, it is critical to reasonably configure the emergency stations. In general, emergency stations are mostly distributed according to the distribution of emergency demands and response time, which, however, fails to consider the negative impacts of randomly occurring emergency demands and traffic delays. In this study, first aid demands are combined with traffic states based on the spatiotemporal big data set covering model, which alleviates the negative impacts of randomly occurring first aid demands and traffic delay time on the planning of pre-hospital first aid stations. Moreover, the accuracy of the site selection model is improved, which meets the requirements that all randomly occurring simulated first aid demands can be approached within the target time under planning conditions and actual traffic constraints. Taking Nanjing City as an example, this study obtains multi-source big data, such as ambulance-carried GPS data from June 2018 to June 2019, Gaode Map-recorded traffic congestion data, and survey data of emergency rescue facilities. Basing on the processing and analysis of these data, it shows that first aid demands in Nanjing City are highly region-specific with high time delay. Various required factors are determined based on modeling and analysis, and the target time is agreed to be 8 min. The average vehicle speed on each road is calculated, accompanied by the establishment of an actual road network model. In this context, the transit time from the randomly distributed first aid stations to the hospital can be calculated, which are set to satisfy the model conditions so as to obtain the solution. Finally, such a solution is adjusted and verified according to the land-use situation. The results of this study, based on spatiotemporal big data, are expected to provide insights into improving the site selection model and enhancing efficiency while providing new technical methods.

A GIS-based multi-criteria model for offshore wind energy power plants site selection in both sides of the Aegean Sea.

Location selection for offshore wind farms is a major challenge for renewable energy policy, marine spatial planning, and environmental conservation. This selection constitutes a multi-criteria decision-making problem, through which parameters like wind velocity, water depth, shorelines, fishing areas, shipping routes, environmental protection areas, transportation, and military zones should be jointly investigated. The aim of the present study was thus to develop an integrated methodology for assessing the siting of bottom-fixed offshore wind farms in two different countries (with different legal, political, and socio/economic characteristics). Our methodology combined multi-criteria decision-making methods and geographical information systems and was implemented in Cyclades (Greece) and in the sea area of İzmir region (Turkey). Experts used fuzzy sets and linguistic terms to achieve more consistent and independent rankings and results. In the Turkish region, the results showed that 519 km2 (10.23%) of the study area is suitable for offshore wind farms, while in the Greek region, only 289 km2 (3.22%) of the study area was found to be suitable. This spatial suitability analysis may contribute to provide some useful recommendations for the spatial marine planning at the regional scale, as well as for the preliminary assessment of new offshore wind farms in both countries.

Modifying the conceptual model of site selection in the organisation of association conferences

International association conferencing is the largest and most profitable segment of the events industry. Competition to host these events is fierce and many destination management organizations (DMOs) offer a range of incentives to attract professional meeting planners to their destination. Many second-tier destinations have invested in their facilities and incentives in order to compete with more traditional conference cities. However, to date there has been a limited exploration of the process of destination or site selection. Prior research has generated conceptual models of site selection, which, though insightful, do not adequately reflect the role of DMOs in the process. A critical evaluation of these models and the supporting literature on site selection has provided the basis for a qualitative inquiry which has involved twenty-five elite professionals (meeting planners and DMO staff). The results from the interviews has led to the creation of an amended conceptual model of site selection which incorporates Weber’s (2001. Meeting planners’ use and evaluation of convention and visitor bureaus. Tourism Management, 22(6), 599–606) list of DMO services and draws particular attention to the role of subvention in site selection.

Evaluating a Suitable Aquaculture Site Selection Model for Cobia (Rachycentron canadum) during Extreme Events in the Inner Bay of the Penghu Islands, Taiwan

Despite numerous studies on the effect of a cold weather disaster on fisheries in 2008, no operational systems have been developed to monitor the threat of such an event to mariculturists in the Penghu Islands (PHI) region of Taiwan. The present study employed a suitable aquaculture site selection map of the inner bay of the PHI to reduce aquaculture losses and mortality rates of cobia (Rachycentron canadum) during extreme events. Daily marine environmental data, including sea surface temperature (SST), chlorophyll-a concentration (chl-a), and wind speed in the winter, were collected. An extreme event was defined as a period of over 11 days in a month of strong winds (>6 m/s). Four parameters in the PHI inner bay, including SST, cold-water intrusion days, chl-a, and offshore distance to the PHI coastline, were used to evaluate suitable aquaculture sites for cobia culture. The results indicated that La Niña events could not be used as a factor to detect cold-water intrusion events and select suitable aquaculture sites in the PHI. The evaluated suitable aquaculture site selection map, obtained using an arithmetic mean model and a geometric mean model, revealed that the avoidance sites during extreme events were concentrated in the northern and northwestern PHI. Suitable areas were concentrated in the southeastern areas. We further suggested that commercial cobia aquaculture operations in the PHI inner bay could be moved to the suitable sites in southeastern PHI during extreme events.

Comprehensive evaluation model of wind farm site selection based on ideal matter element and grey clustering

China has carried out the relevant research on the evaluation of the wind farm site selection, but the evaluation index are mainly from the economic aspect and comprehensive benefit evaluation research of the wind farm site selection exists defects. In order to comprehensively to consider the explicit and implicit benefits of wind farms, firstly, a comprehensive evaluation index system for the wind farm site selection is established, which mainly includes economic, environmental and social aspects. Secondly, an integrated weight model with subjective and objective weight method is constructed, and the ideal matter element extension evaluation model and the grey clustering evaluation model for the wind farms site selection are proposed. Finally, case studies show that the proposed comprehensive evaluation model can rank the comprehensive benefits of wind farms site selection. Based on the comprehensive evaluation results of wind farm site selection, a comprehensive evaluation model of wind farm site selection is proposed to this paper, which can provide the decision basis of the decision makers. This paper establishes an index evaluation model from three aspects of economy, environment and society, examines the advantages and disadvantages of each access location of wind power system, so as to make a reasonable site selection. In view of the current development of the electricity market, this paper constructs a differential weighting model based on the built evaluation model for wind power system location. The simulation results show that the proposed model can effectively guide the location optimization of wind power system.

Introducing the coupled stepwise areal constraining and Mahalanobis distance: a promising MCDM-based probabilistic model for landfill site selection.

This study sets out to propose a new ensemble of probabilistic spatial modeling and multi-criteria decision-making comprised of stepwise areal constraining and Mahalanobis distance algorithms in order to assess areal suitability for landfilling. The Ardak watershed was selected as the study area due to encountering several cases of open garbage dumps and uncontrolled landfills which are one of the main sources of river water pollution in the upstream of the Ardak dam. The results revealed that the proposed algorithm successfully assists in inventory-irrespective probabilistic modeling of landfill siting which is mainly indebted to the role of areal constraining in providing training and validation samples for the Mahalanobis distance model. The latter also showed a robust pattern recognition results from which a discernible differentiation of the area was attained while the spatial dependencies between the environmental factors were taken into account. Mahalanobis distance also gave an outstanding performance in terms of goodness of fit (area under the success rate 89.367) and prediction power (area under the success rate 89.252). Based on a five-point scale classification scheme, about 2.7% and 2.6% of the study area, respectively, have high and very high suitability for landfilling, while the remaining area is shared between very low-to-moderate suitability classes. According to the current trail of literature regarding landfill site selection which mostly relies on mere areal filtering, a probabilistic model would give invaluable inferences regarding the pattern of suitability/susceptibility of the area of interest and causative role of the influential factors. Graphical Abstract.

Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach

With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the proposed method has a higher true positive rate and a lower false positive rate than the other three site selection models, which indicates that this method has higher accuracy for site selection. The site results show that areas suitable for digital signage are mainly distributed in Sanlitun, Wangfujing, Financial Street, Beijing West Railway Station, and along the main road network within the Sixth Ring Road. The research provides a reference for integrating geographical features and content data into the site-selection algorithm. It can effectively improve the accuracy and scientific nature of digital signage layouts and the efficiency of digital signage to a certain extent.

Multi-Criteria Location Model of Emergency Shelters in Humanitarian Logistics

Natural disasters can cause serious casualties and economic losses, and emergency shelters are effective measures to reduce disaster risks and protect lives. At present, the location models of refuge facilities often ignore the diversion of shelter from the perspective of humanitarian logistics and the needs of victims. Such models also seldom consider the impact of the pre-storage of relief materials on the location of shelters. In this study, on the basis of the different needs of disaster victims, shelters are divided into two types—basic life and psychological medical service guarantees. While considering the full coverage of shelter needs, capacities, and budget constraints, the shelter distance, the optimized distribution of refugees, and the pre-stock quantity of goods are optimized. The facility service quality is optimized on the basis of qualitative factors. This study proposes a multi-standard constrained site selection model to optimize the pre-disaster shelter site-allocation problem. The model is helpful for decision makers to influence shelter siting and victims’ allocating process through their expertise and to obtain a solution that compromises multiple objectives. In this study, several basic cases are generated from the actual data of certain areas in Sichuan Province, a disaster-prone region in China, to verify the effectiveness of the model.

Distributed Energy Storage Optimization Location Method Based on Improved Voltage Distribution

Aiming at the problem of voltage overrun caused by large-scale distributed generation (DG) access to distribution network, a distributed energy storage optimization location method based on improved power quality is proposed. Firstly, the voltage governance mechanism of distributed energy storage access to distribution network with large-scale distributed generation is analyzed. Secondly, a distributed energy storage optimization site selection model is proposed, which can be solved in two stages: In the first stage, the power flow calculation is performed based on the typical time section, and the set of energy storage grid-connected nodes is determined by using the weighted voltage sensitivity. In the second stage, the distribution network voltage offset and active power loss optimization are taken as the objective functions. Each node in the set is selected as a parallel node for power flow calculation to determine the final grid-connected node by using the enumeration method. Finally, the improved IEEE33 node distribution network system is taken as an example to verify the correctness of the proposed model. The results of the example show that the distributed energy storage access can effectively support the voltage, and the proposed location model has good practicability.

Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires

The combined effects of long-term fire suppression, logging, and overgrazing have negatively impacted many southwestern U.S. forests, resulting in decreased habitat quality for wildlife, and more frequent and severe wildfires. In response, land management agencies are implementing large-scale forest restoration treatments, but data on how wildlife respond to restoration treatments and wildfires are often limited. We investigated bed and den site selection of American black bears (Ursus americanus) using GPS location data and a use/available study design to assess the influence of habitat characteristics, including wildfires, prescribed burns, and thinning treatments on bed and den site selection in the Jemez Mountains, New Mexico. The most supported models suggested that black bears were more likely to select bed sites with a combination of low horizontal visibility (β = −0.007, SE = 0.002; P = 0.002) and high stand basal area (β = 0.013, SE = 0.005; P = 0.004). The highest-ranking model for den site selection indicated that black bears were more likely to select den sites with low horizontal visibility (β = −0.0102, SE = 0.004; P = 0.006). Black bears used all disturbed sites to varying degrees (45% of study area), although 48% of bed sites were located in undisturbed habitat (55% of study area) while only 11% and 2% of bed sites were located in thinned and prescribed burn sites, respectively. Thirty-nine percent of bed sites were located in previous wildfire locations; however, 67% of these sites were in areas with low burn severity. Thirty-eight percent of den sites were located in previously disturbed habitat, 8 of these sites were burned by wildfires. In order to develop effective management plans for black bears, it is essential to understand responses to landscape-scale habitat disturbances due to wildfires and restoration activities, all of which are becoming more prevalent and widespread across southwestern forests. Accounting for the timing, size, and proximity of future restoration efforts would aid in mitigating potential short-term negative effects on black bears.

Impact of Climate Extremes on Suitability Dynamics for Japanese Scallop Aquaculture in Shandong, China and Funka Bay, Japan

The assessment of extreme weather events on suitable sites for aquaculture could help in establishing sustainable coastal environmental resource management. Japanese scallop culture is an economically important marine farming activity in the coastal communities of Shandong, China and Funka Bay, Japan. In this study, we improved the suitable aquaculture site-selection model (SASSM) by using Geostationary Ocean Color Imager (GOCI) data instead of Moderate Resolution Imaging Spectroradiometer (MODIS) data, as a complementary source for higher temporal and spatial resolution data that are useful for monitoring fine-scale coastal and oceanic processes. We also applied the newly developed SASSM to the Japanese scallop production site along the Shandong coast. Finally, we analyzed the correlations between environmental factors (chlorophyll a concentration, sea surface temperature (SST), and total suspended sediment), meteorological factors (precipitation, temperature, and wind), and climatic events (winter East Asian monsoon (EAM) and El Niño/La Niña Southern Oscillation), and the impacts of climate events on suitable zones for scallop aquaculture. The new SASSM maps show that GOCI products have the potential for oceanographic investigations in Shandong, China and Funka Bay, Japan. Our results highlighted higher aquaculture site suitability for scallop in Funka Bay than in Shandong coast. During the winter with a strong EAM (2011), the suitable area for Japanese scallop aquaculture increased. Conversely, in the winter during a strong El Niño (2016), we found fewer areas that were highly suitable for scallop aquaculture in Funka Bay. SST was extremely low in Funka Bay during spring and summer 2017, which caused fewer highly suitable areas (scores of 7 and 8) for scallop aquaculture relative to other years. These findings suggest that extreme climatic events significantly impact the availability of suitable sites for marine farming and thus, should be considered in the development and design of coastal aquaculture sites.

Research on site selection modelling of new energy construction projects oriented to municipal planning

In order to overcome the problems of low index rationality and low model accuracy when constructing new energy construction project site selection model by traditional methods. The modelling method of new energy construction project location for municipal planning is proposed. According to the evaluation index of engineering site selection, a multi-type evaluation index system is constructed. Entropy weight method and analytic hierarchy process are combined to calculate the corresponding weights of the evaluation indicators. On the basis of grey evaluation principle, evaluation indexes are divided and grey evaluation weight matrix is established. Evaluation value is used to select the best scheme to complete the new energy construction project site selection. The experimental results show that the skewness coefficients of the proposed method are close to zero, which proves that the proposed method is more reasonable and the model is more accurate.