An Evacuation Simulator for Exploring Mutual Assistance Activities in Neighborhood Communities for Earthquake Disaster Mitigation

Japan is at great risk of being struck by huge earthquakes. When a strong earthquake occurs, various other disasters such as fire, collapsing buildings, and road blockages simultaneously occur as a result. In such a situation, it is difficult to ensure that the local emergency activities by, for example, the public fire company and community volunteers, are sufficient. Considering this issue, mutual assistance among residents, such as firefighting, evacuating victims, and helping those in need of assistance to designated safety sites, is extremely important. This paper proposes the development of an evacuation activities simulator, considering the capability of mutual assistance under various earthquake disasters to support exploration of community-based activities. In particular, the simulator calculates the time that local resident agents take to evacuate to the designated safety site, and the number of agents that can and cannot evacuate. Users can change the ratio of those who cannot evacuate to the designated safety site based on whether they are without some support or with persons who support them. Therefore, users can compare the simulation results of various outcomes. Through the experimental demonstration the following findings were obtained. Confirming the simulation results, users can understand that human suffering is reduced by mutual assistance activities. In addition, users can distinguish when the capability of mutual assistance is high or low, and when the capability of mutual assistance is changed according to the time of day due to the presence of the commuting population. Therefore, users can explore the countermeasures used to reduce human suffering when the capability of mutual assistance is low

INVESTIGATION INTO GAME-BASED CRISIS SCENARIO MODELLING AND SIMULATION SYSTEM

A crisis is an infrequent and unpredictable event. Training and preparation process requires tools for representation of crisis context. Particularly, crisis events consist of different situations, which can occur at the same time combining into complex situation and becoming a challenge in coordinating several crisis management departments. In this regards, disaster prevention, preparedness and relief can be conceptualized into a design of hypothetical crisis game. Many complex tasks during development of emergency circumstance provide an opportunity for practitioners to train their skills, which are situation analysis, decision-making, and coordination procedures. While the training in physical workouts give crisis personal a hand-on experience in the given situation, it often requires a long time to prepare with a considerable budget. Alternatively, computational framework which allows simulation of crisis models tailoring into crisis scenario can become a cost-effective substitution to this study and training. Although, there are several existing computational toolsets to simulate crisis, there is no system providing a generic functionality to define crisis scenario, simulation model, agent development, and artificial intelligence problem planning in the single unified framework. In addition, a development of genetic framework can become too complex due to a multi-disciplinary knowledge required in each component. Besides, they have not fully incorporated a game technology toolset to fasten the system development process and provide a rich set of features and functionalities to these mentioned components. To develop such crisis simulation system, there are several technologies that must be studied to derive a requirement for software engineering approach in system’s specification designs. With a current modern game technology available in the market, it enables fast prototyping of the framework integrating with cutting-edge graphic render engine, asset management, networking, and scripting library. Therefore, a serious game application for education in crisis management can be fundamentally developed early. Still, many features must be developed exclusively for the novel simulation framework on top of the selected game engine. In this thesis, we classified for essential core components to design a software specification of a serious game framework that eased crisis scenario generation, terrain design, and agent simulation in UML formats. From these diagrams, the framework was prototyped to demonstrate our proposed concepts. From the beginning, the crisis models for different disasters had been analysed for their design and environment representation techniques, thus provided a choice of based simulation technique of a cellular automata in our framework. Importantly, a study for suitability in selection of a game engine product was conducted since the state of the art game engines often ease integration with upcoming technologies. Moreover, the literatures for a procedural generation of crisis scenario context were studied for it provided a structure to the crisis parameters. Next, real-time map visualization in dynamic of resource representation in the area was developed. Then the simulation systems for a large-scale emergency response was discussed for their choice of framework design with their examples of test-case study. An agent-based modelling tool was also not provided from the game engine technology so its design and decision-making procedure had been developed. In addition, a procedural content generation (PCG) was integrated for automated map generation process, and it allowed configuration of scenario control parameters over terrain design during run-time. Likewise, the artificial planning architecture (AI planning) to solve a sequence of suitable action toward a specific goal was considered to be useful to investigate an emergency plan. However, AI planning most often requires an offline computation with a specific planning language. So the comparison study to select a fast and reliable planner was conducted. Then an integration pipeline between the planner and agent was developed over web-service architecture to separate a large computation from the client while provided ease of AI planning configuration using an editor interface from the web application. Finally, the final framework called CGSA-SIM (Crisis Game for Scenario design and Agent modelling simulation) was evaluated for run-time performance and scalability analysis. It shown an acceptable performance framerate for a real-time application in the worst 15 frame-per-seconds (FPS) with maximum visual objects. The normal gameplay performed capped 60 FPS. At same time, the simulation scenario for a wildfire situation had been tested with an agent intervention which generated a simulation data for personal or case evaluation. As a result, we have developed the CGSA-SIM framework to address the implementation challenge of incorporating an emergency simulation system with a modern game technology. The framework aims to be a generic application providing main functionality of crisis simulation game for a visualization, crisis model development and simulation, real-time interaction, and agent-based modelling with AI planning pipeline

Wildfire spread simulation modeling for risk assessment and management in Mediterranean areas

Wildfires are a key problem in many terrestrial ecosystems, particularly in the Mediterranean Basin, and climate change will likely cause their increase in future years. Wildfire behavior simulator models are very useful to characterize wildfire risk, identify the valued resources more exposed to wildfires and to plan the best strategies to mitigate risk. In this work, we first carried out a review of wildfire spread and behavior modelling, and then focusing on FLAMMAP model. Then, we evaluated the effects of diverse strategies of fuel treatments on wildfire risk in an agro-pastoral area of the North-central Sardinia (Italy) that has been affected by the largest Sardinian wildfire of recent years (Bonorva wildfire, about 10,500 ha burned, on July 2009). Finally we analyzed the combined effects of fuel treatments and post-fire treatments with the aim to mitigate wildfire and erosion risk, linking the minimum travel time algorithm with the Ermit modeling approach in a study area located in Northern Sardinia (Italy), mostly classified as European Site of Community Importance. Overall, the results obtained showed that wildfire behavior simulator models can support forest fire management and planning and can provide key spatial information and data that can be helpful to policy makers and land managers

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces

Optimizing Stadium Evacuation by Integrating Geo-Computation and Affordance Theory

The purpose of this project was to optimize football stadium evacuation time by integrating geo-computation with affordance theory from perceptual psychology to account for evacuee characteristics: age, gender, physical fitness, alcohol consumption, and prior experience attending football games at The University of Southern Mississippi (USM), evacuating from large, outdoor public places, and with hazard events. According to the Uniting and Strengthening America by Providing Appropriate Tools Required to Intercept and Obstruct Terrorism (USA PATRIOT) Act, football stadiums are part of the country’s critical infrastructure warranting special government protection. Evacuation modeling was identified as an important component of game day emergency preparation. Research shows that: (1) the age, gender, and physical fitness of an individual impact his/her locomotion speed; (2) evacuation route choice is influenced by the perception of its safety and effectiveness; and (3) prior evacuation experience affects evacuation decision-making processes. By including these factors, this research, conducted at USM’s M.M. Roberts Stadium, represents the reality of evacuee movement and behaviors that influence stadium evacuation time. A questionnaire-based survey was administered to game attendees prior to a USM home game to gather evacuee attribute data that influenced locomotion speed. This data, plus secondary spatial data, were used in an agent-based model to model individual evacuee movement. The time required for all evacuees to exit the stadium and campus was 165.16 minutes. This time was significantly shorter than evacuation times from the same location using non-location-specific evacuee locomotion speeds, suggesting that use of local data is vital to accurately depicting evacuation time. The findings also indicated that age and gender were the two main factors that impacted locomotion speeds. The main contributions of this study were: (1) optimizing evacuation time by using location-specific locomotion speeds and (2) providing insights into how evacuees’ physical and mental health influence their evacuation decision-making processes. The U.S. government and sports management industry could use these findings to increase game day safety and security. Due to the spatio-temporal nature of evacuation modeling and perceptions of evacuees that impact evacuation time, this research contributed to the fields of geography, computer science, sport management, psychology, and emergency management

Sensitivity Analysis of Aerial Wildfire Fighting Tactics with Heterogeneous Fleets Using an Agent Based Simulation Framework

The increase in the average temperature of the global surface temperature caused longer wildfire seasons, which have caused more severe and frequent incidents, resulting in higher expenses, unrecoverable losses and civilian casualties. Moreover, the increased number of wildfires has contributed to higher levels of carbon in the atmosphere, further exacerbating global warming. Fighting wildfires is a complex phenomenon that requires various resources, and the System of Systems (SoS) approach can be leveraged to analyze the problem. This study utilizes an SoS simulation framework to model wildfire suppression missions, focusing on a mixed fleet composition of suppression drones with different characteristics such as airframe configurations, payload capacity, flight velocity, and powertrain architectures. The study evaluates multiple suppression tactics, considering factors such as fleet composition, available agents, and resources. The results of the analysis show the impact of various environmental parameters on fire growth and provide a rigorous sensitivity analysis for wildfire containment use cases. The use of the SoS framework helps to reveal nuanced patterns at the SoS level, which can aid in the development of new solutions for wildfire fighting. This study highlights the importance of considering the complexities of the problem and the need for innovative approaches to combat wildfires effectively

Geospatial approach using socio-economic and projected climate change information formodelling urban growth

Urban growth and climate change are two interwoven phenomena that are becoming global environmental issues. Using Niger Delta of Nigeria as a case study, this research investigated the historical and future patterns of urban growth using geospatialbased modelling approach. Specific objectives were to: (i) examine the climate change pattern and predict its impact on urban growth modelling; (ii) investigate the historical pattern of urban growth; (iii) embrace some selected parameters from United Nations Sustainable Development Goals (UN SDGs) and examine their impacts on future urban growth prediction; (iv) verify whether planning has controlled urban land use sprawl in the study area; and (v) propose standard operating procedure for urban sprawl in the area. A MAGICC model, developed by the Inter-Governmental Panel on Climate Change (IPCC), was used to predict future precipitation under RCP 4.5 and RCP 8.5 emission scenarios, which was utilized to evaluate the impact of climate change on the study area from 2016 to 2100. Observed precipitation records from 1972 to 2015 were analysed, and 2012 was selected as a water year, based on depth and frequency of rainfall. A relationship model derived using logistic regression from the observed precipitation and river width from Landsat imageries of 2012 was used to project the monthly river width variations over the projected climate change, considering the two emission scenarios. The areas that are prone to flooding were determined based on the projected precipitation anomalies and a suitability map was developed to accommodate the impact of climate change in the projection of future urban growth. Urban landscape changes between 1985 and 2015 were also analysed, which revealed a rapid urban growth in the region. A Cellular Automata/Markov Chain (CA-Markov) model was used to project the year 2030 land cover of the region considering two scenarios; normal projection without any constraint, and using some designed constraints (forest reserves, population and economy) based on some selected UN SDGs criteria and climate change. On validation, overall simulation accuracies of 89.25% and 91.22% were achieved based on scenarios one and two, respectively. The projection using the first scenario resulted to net loss and gains of - 7.37%, 11.84% and 50.88%, while that of second scenario produced net loss and gains of -4.72%, 7.43% and 48.37% in forest, farmland and built-up area between 2015 and 2030, respectively. The difference between the two scenarios showed that the UN SDGs have great influence on the urban growth prediction and strict adherence to the selected UN SDGs criteria can reduce tropical deforestation, and at the same time serve as resilience to climate change in the region