Assimilation of Perimeter Data and Coupling with Fuel Moisture in a Wildland Fire - Atmosphere DDDAS

We present a methodology to change the state of the Weather Research Forecasting (WRF) model coupled with the fire spread code SFIRE, based on Rothermel's formula and the level set method, and with a fuel moisture model. The fire perimeter in the model changes in response to data while the model is running. However, the atmosphere state takes time to develop in response to the forcing by the heat flux from the fire. Therefore, an artificial fire history is created from an earlier fire perimeter to the new perimeter, and replayed with the proper heat fluxes to allow the atmosphere state to adjust. The method is an extension of an earlier method to start the coupled fire model from a developed fire perimeter rather than an ignition point. The level set method is also used to identify parameters of the simulation, such as the spread rate and the fuel moisture. The coupled model is available from openwfm.org, and it extends the WRF-Fire code in WRF release.Comment: ICCS 2012, 10 pages; corrected some DOI typesetting in the reference

Prediction Time Assessment in a DDDAS for Natural Hazard Management: Forest Fire Study Case ✩

This work faces the problem of quality and prediction time assessment in a Dynamic Data Driven Application System (DDDAS) for predicting natural hazard evolution. In particular, we used forest fire spread prediction as a case study to show the applicability of the methodology. The improvement on the prediction quality when using a two-stage DDDAS prediction framework has been widely proved. The two-stages DDDAS has a first phase where an adjustment of the input data is performed in order to be applied in the second phase, the prediction. This paper is focused on defining a new methodology for prediction time assessment under this kind of prediction environments by evaluating, in advance, how a certain combination of simulator, computational resources, adjustment strategy, and frequency of data acquisition will perform, in terms of prediction time. Since the time incurred in the hazard simulation is a crucial part of the whole prediction time, we have defined a methodology to classify the simulator’s execution time using Artificial Intelligence techniques allowing us to determine upper bounds for the DDDAS prediction time depending on the particular input parameter setting. This methodology can be extrapolated to any DDDAS for predicting natural hazards evolution, which uses the two-stage prediction scheme as a working framework. Keywords

Efficient knowledge retrieval to calibrate input variables in forest fire prediction

Forest fires are a serious threat to humans and nature from an ecological, social and economic point of view. Predicting their behaviour by simulation still delivers unreliable results and remains a challenging task. Latest approaches try to calibrate input variables, often tainted with imprecision, using optimisation techniques like Genetic Algorithms. To converge faster towards fitter solutions, the GA is guided with knowledge obtained from historical or synthetical fires. We developed a robust and efficient knowledge storage and retrieval method. Nearest neighbour search is applied to find the fire configuration from knowledge base most similar to the current configuration. Therefore, a distance measure was elaborated and implemented in several ways. Experiments show the performance of the different implementations regarding occupied storage and retrieval time with overly satisfactory results.Los incendios forestales son una grave amenaza para seres humanos y para la naturalza desde el punto de vista ecológico, social y económico. Predecir su comportamiento usando simulaciones todavía da resultados poco fiables y sigue siendo una tarea desafiante. Trabajos más recientes, intentan calibrar variables de entrada, muchas veces imprecisas, aplicando técnicas de optimización como algoritmos genéticos. Para converger más rápido hacia soluciones más adecuadas, el algoritmo genético es guiado con conocimiento obtenido de fuegos históricos o sintéticos. Hemos desarrollado un método robusto y eficiente para almacenar y recuperar ese conocimiento. Aplicamos la búsqueda del vecino más cercano para encontrar la configuración del fuego más similar a la configuración actual dentro de la base de conocimiento. Para esto, hemos elaborado una función de distancia y la hemos implementado de diferentes maneras. Experimentos muestran el rendimiento de las distintas implementaciones considerando el almacenamiento ocupado y el tiempo de recuperación con resultados muy satisfactorios.Els incendis forestals són una amenaça important tant pels homes com per a la natura des d'un punt de vista ecològic, social i econòmic. La predicció del comportament dels incendis forestals utilitzant simulació encara genera resultats poc fiables i, per tant, segueix essent un desafiament important. Aproximacions recents a aquest problema, intenten calibrar les variables d'entrada dels simuladors, les quals sovint presenten un grau important d'incertesa, utilitzant tècniques d'optimització com poden ser els Algoritmes Genètics (AG). Per tal de que la convergència dels AG a una solució bona sigui ràpida, l'AG es guia mitjançant el coneixement obtingut d'històrics d'incendis o focs sintètics. Per aquest treball s'ha desenvolupat un mètode eficient i robust d'emmagatzemament i recuperació del coneixement. El mètode anomenat Nearest Neighbour Search s'aplica per trobar la configuracióo guardada en la base de coneixements que més s'assembli a la configuracióo real de l'incendi. Per a tal efecte, s'ha desenvolupat una mètrica de distància la qual ha estat implementada de diferents formes alternatives. L'experimentació realitzada mostra resultats encoratjadors en el rendiment de les diferents implementacions tenint en compte l'emmagatzemament ocupat i el temps de recuperació de la informació

Pedestrian steering behaviour modelling within the built environment

Prediction of pedestrians’ steering behaviours within the built environments under normal and non-panic situations is useful for a wide range of applications, which include social science, psychology, architecture, and computer graphics. The main focus is on prediction of the pedestrian walking paths and the influences from the surrounding environment from the engineering point of view

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Path planning for first responders in the presence of moving obstacles

Navigation services have gained much importance for all kinds of human activities ranging from tourist navigation to support of rescue teams in disaster management. However, despite the considerable amount of route guidance research that has been performed, many issues that are related to navigation for first responders still need to be addressed. During disasters, emergencies can result in different types of moving obstacles (e.g., fires, plumes, floods), which make some parts of the road network temporarily unavailable. After such incidents occur, responders have to go to different destinations to perform their tasks in the environment affected by the disaster. Therefore they need a path planner that is capable of dealing with such moving obstacles, as well as generating and coordinating their routes quickly and efficiently. During the past decades, more and more hazard simulations, which can modify the models with incorporation of dynamic data from the field, have been developed. These hazard simulations use methods such as data assimilation, stochastic estimation, and adaptive measurement techniques, and are able to generate more reliable results of hazards. This would allow the hazard simulation models to provide valuable information regarding the state of road networks affected by hazards, which supports path planning for first responders among the moving obstacles. The objective of this research is to develop an integrated navigation system for first responders in the presence of moving obstacles. Such system should be able to navigate one or more responders to one or multiple destinations avoiding the moving obstacles, using the predicted information of the moving obstacles generated from by hazard simulations. In this dissertation, the objective we have is expressed as the following research question: How do we safely and efficiently navigate one or more first responders to one or more destinations avoiding moving obstacles? To address the above research questions, this research has been conducted using the following outline: 1). literature review; 2). conceptual design and analysis; 3). implementation of the prototype; and 4). assessment of the prototype and adaption. We investigated previous research related to navigation in disasters, and designed an integrated navigation system architecture, assisting responders in spatial data storage, processing and analysis.Within this architecture, we employ hazard models to provide the predicted information about the obstacles, and select a geo-database to store the data needed for emergency navigation. Throughout the development of the prototype navigation system, we have proposed: a taxonomy of navigation among obstacles, which categorizes navigation cases on basis of type and multiplicity of first responders, destinations, and obstacles; a multi-agent system, which supports information collection from hazard simulations, spatio-temporal data processing and analysis, connection with a geo-database, and route generation in dynamic environments affected by disasters; data models, which structure the information required for finding paths among moving obstacles, capturing both static information, such as the type of the response team, the topology of the road network, and dynamic information, such as changing availabilities of roads during disasters, the uncertainty of the moving obstacles generated from hazard simulations, and the position of the vehicle; path planning algorithms, which generate routes for one or more responders in the presence of moving obstacles. Using the speed of vehicles, departure time, and the predicted information about the state of the road network, etc., three versions (I, II, and III) of Moving Obstacle Avoiding A* (MOAAStar) algorithms are developed: 1). MOAAstar– I/Non-waiting, which supports path planning in the case of forest fires; 2). MOAAstar–II/Waiting, which introduces waiting options to avoid moving obstacles like plumes; 3). MOAAstar–III/Uncertainty, which can handle the uncertainty in predictions of moving obstacles and incorporate the profile of responders into the routing. We have applied the developed prototype navigation system to different navigation cases with moving obstacles. The main conclusions drawn from our applications are summarized as follows: In the proposed taxonomy, we have identified 16 navigation cases that could occur in disaster response and need to be investigated. In addressing these navigation problems, it would be quite useful to employ computer simulations and models, which can make reliable predicted information about responders, the targets, and obstacles, in finding safe routes for the responders. The approach we provide is general and not limited to the cases of plumes and fires. In our data model, the data about the movement of hazards is represented as moving polygons. This allows the data model to be easily adjusted to merge and organize information from models of different types of disasters. For example, the areas that are affected by floods can also be represented as moving polygons. To facilitate the route calculation, not only the data of obstacles but also the information about the state of road networks affected by obstacles need to be structured and stored in the database. In planning routes for responders, the routing algorithms should incorporate the dynamic data of obstacles to be able to avoid the hazards. Besides, other factors, such as the operation time of tasks, the required arrival time, and departure time, also need to be considered to achieve the objectives in a rescue process, e.g., to minimize the delays caused by the moving obstacles. The profile of responders is quite important for generation of feasible routes for a specific disaster situation. The responders may have different protective equipment that allows them to pass through different types of moving obstacles, and thus can have different classification of risk levels to define the state of the road network. By taking into account the profile of the responders, the navigation system can propose customized and safe routes to them, which would facilitate their disaster response processes. On the basis of our findings, we suggest the following topics for future work: As presented Wang and Zlatanova (2013c), there are still a couple of navigation cases that need to be addressed, especially the ones that involve dynamic destinations. More algorithms would be needed to solve these navigation problems. Besides, some extreme cases (e.g., the obstacle covers the target point during the course of an incident) also need to be investigated. Using standard Web services, an Android navigation application, which can provide navigation services in the environment affected by hazards, needs to be developed and tested in both the daily practice and real disasters. In this application, a user interface with various styling options should also be designed for different situations, e.g., waiting and moving, day and night, and urgent and non-urgent. Because the communication infrastructure may not be available or work properly during a disaster response, a decentralized method is needed to allow different users to negotiate with each other and to make local agreements on the distribution of tasks in case there is no support from the central planning system. Another type of multi-agent system would be needed to handle this situation. Introduce variable traveling speed into the re-routing process. The vehicle speed plays an important role in generation of routes avoiding moving obstacle, and can be influenced by many factors, such as the obstacles, the type of vehicles, traffic conditions, and the type of roads. Therefore, it would be needed to investigate how to derive the current and future speed from trajectories of vehicles. Apply the system to aid navigation in various types of natural disasters, using different hazard simulation models (e.g., flood model). More types of agents would be needed and integrated into the system to handle heterogeneous data from these models. Extensions of the data model are also required to meet a wider range of informational needs when multiple disasters occur simultaneously

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

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