Site Selection Models in Natural Disaster Shelters: A Review

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.

Research on the location decision-making method of emergency medical facilities based on WSR

The need for emergency medical services increased drastically during disaster relief. Poor location selection of emergency medical facilities may harm the interests of healthcare workers and patients, leading to unnecessary waste of costs. It involves multiple stakeholders' interests, a typical multi-criteria decision-making problem. Based on multiple-criteria decision-making technology, most current location selection decisions methods comprehensively consider the evaluation criteria of "issue" and "problem" simultaneously and establish mathematical models to achieve the results. Such methods are difficult to take into account the influence of different attribute factors on the final location selection results in practice. Therefore, in this study, we used the WSR methodology as a guide to divide the factors of location selection into "Wuli", "Shili" and "Renli", and proposed the WSR methodology-based multi-criteria decision-making (MCDM) framework for selecting the appropriate location for emergency medical facilities. The integrated framework consists of the Entropy Weight Method, Best-Worst Method, and interval type-2 fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies. Combined with the comparative analysis of actual cases, the results under the guidance of this framework were consistent with practicalities. Also, the sensitivity analysis showed that the location selection ranking fluctuations were not apparent with the fluctuation of criteria weights. Wherefore, the validation of the proposed method's effectiveness, feasibility, and robustness was proved, which provided a valuable reference for the location selection of emergency medical facilities.

Sustainable Design of Temporary Buildings in Emergency Situations

Unforeseen circumstances that occur anywhere in the world following natural disasters, humanitarian and health emergencies, armed conflicts, or in the presence of migratory flows, require adequate and immediate responses. This work aims to analyze the project requirements useful to realizing modular systems for residential, multifunctional, and hospital intended use, which, even if temporary, can ensure a high-performance standard in terms of comfort and energy efficiency, and at the same time guarantee the possibility of use in the widest possible range and in rapid execution times. The considered requirements have been those of settlement in the territory, energy efficiency, transportability, and re-usability. Temporary modular systems put in place with the abovementioned requirements are the basis of the design proposal; to realize this, they are made with dry technology to be reusable and energy-efficient. Furthermore, this enables the reduction of the minimum modules’ production and times of execution in applying both requirements of standardization and modular coordination. All these requirements also add to the ones relating to energy efficiency, transportability, and reusability, which are the pillars of the project for the achievement of performance above all in terms of standards and comfort levels as it is possible to find in the sustainable building of the living period.

Sustainability Model to Select Optimal Site Location for Temporary Housing Units: Combining GIS and the MIVES–Knapsack Model

Selecting the best site location for temporary housing (TH) is one of the most critical decision-making processes in the aftermath of disasters. Many spatial variables and multi-criteria indicator problems are involved in the decision-making analysis. Incorrect treatment of these components often led to failure in previous post-disaster recovery programmes. Wrong decisions caused short- and long-term negative impacts on the environment and people as well as wasting capital spending. In this regard, this research paper aims to present a novel multi-criteria decision-making approach that helps decision makers select optimal site locations to consider spatial and sustainability-related aims by assessing numerous alternatives. This new model is based on combining a knapsack algorithm and the integrated value model for sustainability assessment (MIVES) to derive optimal alternatives. This model makes it possible to objectively quantify sustainability indicators (economic, environmental, and social aspects) and derive satisfaction indices for each site (or set of sites) in terms of TH location. The model is designed to receive and filter data from a geographic information system (GIS). Using this model in future post-disaster recovery programs is believed to increase stakeholders’ satisfaction and maximise the sustainability associated with the selection.

Optimization of Urban Shelter Locations Using Bi-Level Multi-Objective Location-Allocation Model

Recently, global natural disasters have occurred frequently and caused serious damage. As an important urban space resource and public service facility, the reasonable planning and layout optimization of shelters is very important to reduce the disaster loss and improve the sustainable development of cities. Based on the review of location theory and models for shelter site selection, this study constructs a bi-level multi-objective location-allocation model, an accessibility, economy, and efficiency (AEE) model, based on sequential decision logic to maximize the economic sustainability and social utility. The model comprehensively considers factors such as the level of decision-making, the utilization efficiency, and capacity constraints of shelters. The gravity model is introduced to simulate the decision-making behavior of evacuees. A calculation example and its solution prove the high practicability and operability of the AEE model in an actual shelter site selection and construction investment, which can achieve the global optimization of evacuation time and the maximization of the use efficiency of the shelters under the financial constraints. It provides a scientific and effective decision-making method for the multi-objective location optimization problem of shelters.

Methodology and Application of Spatial Vulnerability Assessment for Evacuation Shelters in Disaster Planning

Evacuation shelters are the most important means for safeguarding people in hazardous areas and situations, and thus minimizing losses, particularly those due to a disaster. Therefore, evacuation shelter assignment and evacuation planning are some of the critical factors for reducing vulnerability and increasing resilience in disaster risk reduction. However, an imbalance of shelter distribution and spatial heterogeneity of a population are the critical issues limiting the accessibility of evacuation shelters in real situations. In this study, we propose a methodology for spatial assessment to reduce vulnerability and evaluate the spatial distribution of both shelter demand and resources, considering spatial accessibility. The method was applied to the case study of Mabi, in the context of a disaster caused by the 2018 flooding. We applied this approach to evaluate the area and identified the vulnerability of the evacuation shelters and the residents. The proposed method revealed that 54.55% of the designated evacuation shelters and 59% of the total population were physically vulnerable to the flood. The results highlight, using GIS maps, that the total shelter capacity was significantly decreased to 43.86%. The outcome assessment addressed specific vulnerable shelters and the imbalance between the demand for and resources of each shelter. Accordingly, this study provides practical information and a valuable reference for supporting local governments and stakeholders to improve future disaster planning, prevention, and preparedness.

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.