scholarly journals The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation

2021 ◽  
Vol 11 (24) ◽  
pp. 11909
Author(s):  
Wei Chen ◽  
Yijun Shi ◽  
Wei Wang ◽  
Wenjing Li ◽  
Chao Wu
Keyword(s):  
Resource Allocation ◽  
Disaster Relief ◽  
Effective Area ◽  
Spatial Optimization ◽  
Disaster Prevention ◽  
Short Term ◽  
Evacuation Simulation ◽  
Maximum Capacity ◽  
Emergency Shelters ◽  
Quantitative Basis

As an important space for disaster prevention, the construction of emergency shelters is crucial for the creation of a complete disaster relief facility network. Based on the goal of the prevention of day and night disaster, short-term fixed shelters are taken as the study object of the present work, and models are designed for evacuation simulation and the spatial optimization of shelters. According to the simulation, 680 of the 2334 demand points were found to be incompletely evacuated, and the average time for everyone to be evacuated was 10.3 min. Moreover, of the 888 short-term fixed shelters, only 218 did not reach their maximum capacity. In the context of short-term fixed sheltering, Haizhu was found to have the largest number of non-evacuated people (1.11 million), and the average number of non-evacuated people in Yuexiu was the largest (2184). According to the spatial optimization data of the shelters, the numbers of target plots for new shelter resources that must be added in Haizhu, Yuexiu, Liwa, and Tianhe are 406, 164, 141, and 136, respectively, the effective shelter areas of which are 2,621,100, 2,175,300, 812,100, and 1,344,600 m2, respectively. A total of 487 short-term fixed shelters and 360 temporary shelters were newly added, and the recommended scales for Haizhu, Liwan, Tianhe, and Yuexiu were 243, 70, 58, and 116, respectively, with average effective areas of 6169 m2, 5577 m2, 8707 m2, and 12,931 m2, respectively. Additionally, the recommended scales of newly added temporary shelters in Haizhu, Liwan, Tianhe, and Yuexiu are 163, 71, 78, and 48, with an average effective area of 2706, 2581, 4017, and 6234 m2, respectively. These findings provide a direct quantitative basis for the spatial optimization of various types of emergency shelters, and the method proposed in this paper supports the planning and layout of emergency shelters, as well as the improvement of the efficiency of urban resource allocation.

Florida voters react to Biden's Caribbean policy

2021 ◽  
Keyword(s):  
Disaster Relief ◽  
Electoral Politics ◽  
The State ◽  
Pivotal Role ◽  
Economic Recovery ◽  
Short Term ◽  
Content Type ◽  
Family Connections ◽  
The Caribbean ◽  
The Government

Significance Many areas of the Caribbean have trade, investment and family connections with communities in Florida. As the state now plays a pivotal role in US electoral politics, crises in the region can take on added political importance for parts of Florida’s electorate. Impacts Forecasts of short-term economic recovery for Florida remain highly uncertain given the continuing impact of the pandemic. Clashing interests across the Caribbean may demand greater coordination of US policy than the government can currently offer. Healthcare and disaster relief capabilities within the state are severely overstretched and could be overwhelmed by a new crisis.

Simulation and Experiment of Mass Evacuation to a Tsunami Evacuation Tower

2015 ◽  
Author(s):  
Takao Kakizaki ◽  
Jiro Urii ◽  
Mitsuru Endo
Keyword(s):  
Nankai Trough ◽  
Test Field ◽  
Disaster Prevention ◽  
Evacuation Simulation ◽  
Evacuation Time ◽  
Simulation And Experiment ◽  
Group Population ◽  
Motion Characteristics ◽  
Mass Evacuation ◽  
Digital Human

A 3D mass evacuation simulation using precise kinematic digital human (KDH) models and an experimental study are discussed. The tidal wave associated with the large tsunami caused by the Great East Japan Earthquake was responsible for more than 90% of the disaster casualties. Unfortunately, it is expected that other huge tsunamis could occur in Japan coastal areas if an earthquake with magnitude greater than 8 occurred along the Nankai Trough. Therefore, recent disaster prevention plans should include evacuation to higher buildings, elevated ground, and construction of tsunami evacuation towers. In the evacuation simulation with 500 KDHs, the mass consists of several subgroups. It is shown that the possible evacuation path of each group should be carefully determined to minimize the evacuation time. Several properties such as evacuee motion characteristics of KDHs, number of evacuees, exit gates and, number of injured persons were carefully considered in the simulation. Evacuee motion was also experimentally investigated by building a test field that simulates the structure of an actual tsunami evacuation tower for accommodating approximately 120 evacuees. The experimental results suggest that an appropriately divided group population may effectively reduce the overall group evacuation time. The results also suggest that the fatigue due to walking during evacuation adversely affect the total evacuation time, especially the ascent of stairways. The experimental data can be used to obtain more accurate simulations of mass evacuation.

scholarly journals Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

2020 ◽  
Vol 9 (1) ◽  
pp. 41 ◽  
Author(s):  
Wei Chen ◽  
Yao Fang ◽  
Qing Zhai ◽  
Wei Wang ◽  
Yijie Zhang
Keyword(s):  
Spatial Distribution ◽  
Supply And Demand ◽  
Emergency Shelter ◽  
Evacuation Simulation ◽  
Point Of Interest ◽  
Space Organization ◽  
Quantitative Basis ◽  
Python Programming ◽  
Land Reuse

Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning.

scholarly journals Analysis of Human Resource Allocation Model for Tourism Industry Based on Improved BP Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Shuo Zhu ◽  
Yan Liu
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Human Resource ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Random Perturbation ◽  
Tourism Industry ◽  
Short Term ◽  
Term Memory ◽  
Neural Network Algorithm

This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short-term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry.

scholarly journals A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3966
Author(s):  
Theodoros Anagnostopoulos ◽  
Theodoros Xanthopoulos ◽  
Yannis Psaromiligkos
Keyword(s):  
Resource Allocation ◽  
Short Term Memory ◽  
Short Term ◽  
Coverage Area ◽  
High Importance ◽  
Location Allocation ◽  
Emergency Situations ◽  
Efficient Planning ◽  
Emergency Events ◽  
Long Short Term Memory

Resource allocation of the availability of certain departments for dealing with emergency recovery is of high importance in municipalities. Efficient planning for facing possible disasters in the coverage area of a municipality provides reassurance for citizens. Citizens can assist with such malfunctions by acting as human sensors at the edge of an infrastructure to provide instant feedback to the appropriate departments fixing the problems. However, municipalities have limited department resources to handle upcoming emergency events. In this study, we propose a smartphone crowdsensing system that is based on citizens’ reactions as human sensors at the edge of a municipality infrastructure to supplement malfunctions exploiting environmental crowdsourcing location-allocation capabilities. A long short-term memory (LSTM) neural network is incorporated to learn the occurrence of such emergencies. The LSTM is able to stochastically predict future emergency situations, acting as an early warning component of the system. Such a mechanism may be used to provide adequate department resource allocation to treat future emergencies.

Sign in / Sign up

Export Citation Format

Share Document