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

Forest fire simulator system for emergency resources management support

Europe suffers approximately 65,000 fires every year, which burn, on average, half a million hectares of forest areas [1]. The main direct effect of forest fires is the destruction of the natural landscape and the consequent loss of ecosystem service that have drastic economic impact, but mainly and much more important, fires also result in the loss of human lives every year. Although being forest fires a problem present in all EU members, the most affected areas to this hazards are the southern countries due to their climatological conditions. All affected countries invest lots of resources to minimize fire damages, but many times when dealing with large fires, regional and national disaster management units are lack of efficient and reliable tools to help wildfire analysts. In this work, we describe a process to generate on-line wildfire simulations coupled with the regional weather forecast service (Servei Meteorològic de Catalunya, SMC) and the helicopter company (Helipistas S.L) who provides isochronous perimeters of the fire behaviour in a certain moment of the emergency and how both of this data sources feed the inputs for the simulation process.Europa sufre aproximadamente 65,000 incendios cada año, de media, medio millón de hectáreas forestales[1]. El principal efecto de los fuegos forestales es la destrucción de la superfície natural y como consecuencia la pérdida del ecosistema y el gran impacto económico, pero principamente y de manera mucho más importante el fuego tambien repercute en la pérdida de vidas humanas año tras año. Los fuegos forestales además de ser un problema para los miembros de la UE, se ven repercutidos, especialmente los paises del sur debido a sus condiciones climatológicas. Todos estos paises afectados invierten gran cantidad de recursos para minimizar estos efectos. Generalmente cuando se trata de grandes incendios forestales, las unidades de mando de estos medios de exinción a nivel regional y nacional se ven necesitados de herramientas eficientes y útiles para el análisis de la predicción del comportamiento de estos grandes incendios forestales. En este trabajo, describimos un sistema de predicción de incendios forestales acoplado con el servicio meteorológicos de catalunya (SMC) y la empresa de helicópteros (Helipistas S.L) los cuales proveen de los perímetros del incendio en un instante de tiempo de la emergencia y cómo estas dos fuentes de datos se anexan al proceso de simulación.Europa pateix aproximadament 65,000 incendis cada any, de mitja, cada mig-milió d'hectàrees forestals[1]. El principal efecte dels focs forestals es la destrucció de la superfície natural i com a conseqüència la pèrdua de l'ecosistema i el gran impacte econòmic, però principalment i de manera molt més important el foc, també, repercuteix en la pèrdua de vides humanes any rere any. Els focs forestals a més a més de representar un problema pels països membres de la UE, es veuen afectats els països del Sud degut a les seves condicions climatològiques. Tots aquests països afectats inverteixen grans quantitat de recursos per a minimitzar aquests efectes. Generalment quan es tracta de grans incendis forestals, les unitats de comandament d'aquests medis d'extinció a nivell regional i nacional es veuen necessitats d'eines útils i eficients per a l'anàlisis de la predicció en el comportament dels grans incendis forestals. En aquest treball, descrivim un sistema de predicció d'incendis forestals acoblat amb el servei meteorològic de Catalunya (SMC) i l'empresa d'helicòpters (Helipistas S.L) els quals proveïxen dels perímetres de l'incendi en un instant de temps de l'emergència i com aquestes dos fonts de dades annexen al procés de simulació

Paral·lelització de la simulació de la propagació d'incendis forestals

Els incendis forestals són un tipus de desastres naturals que cada any provoquen importants pèrdues a nivell mundial. Per a poder lluitar de la manera més eficient contra aquests desastres és de gran ajuda poder predir el comportament i evolució d'aquests incendis. Així, s'han desenvolupat diversos models de propagació i simuladors que intenten predir el comportament dels incendis. En aquest context, cal tenir en compte que el temps de simulació és un factor clau, ja que la predicció s'ha de realitzar molt més depressa que el temps real, per a poder prendre accions que mitiguin l'efecte dels incendis. En aquest treball s'ha analitzat el comportament i temps d'execució d'un simulador, àmpliament utilitzat en el camp, anomenat FARSITE, que presenta un temps d'execució força irregular. Un cop analitzat el simulador s'ha dut a terme una paral·lelització basada en pas de missatges (MPI) que ha permès reduir el temps de simulació de forma significativa.Los incendios forestales son desastres naturales que cada año provocan importantes pérdidas a nivel mundial. Para poder luchar de forma más eficiente contra estos desastres es de gran ayuda poder predecir el comportamiento y la evolución de estos incendios. Por este motivo se han creado diversos modelos de propagación y simuladores que intentan predecir el comportamiento de los incendios. En este contexto se ha de tener en cuenta que el tiempo de simulación es un factor clave, ya que la predicción se tiene que realizar mucha más deprisa que el tiempo real, para poder emprender acciones que reduzcan los efectos de los incendios. En este Trabajo se ha analizado el comportamiento y el tiempo de ejecución de un simulador, ampliamente utilizado, llamado FARSITE, que presenta unos tiempos de ejecución irregular. Una vez analizado el simulador se ha paralelizado usando la librería MPI que ha permitido reducir el tiempo de simulación de forma significativa.The wildfires are a type of natural cataclysm that every year cause important loses in the world. To help the fireman with an efficient way is very helpful predict the evolution of this wildfires. Because of that some research groups have developed different propagation models and simulators that want to predict the wildfire evolution. In this context the simulation time have to be much faster than the real time if we want to do some useful actions to reduce the wildfires. For this research we analysed the performance of the one of the most used simulators, named FARSITE, which represents irregular execution times. We have parallelized the MPI library and that permitted to reduce the simulation time significantly

Libro de Actas JCC&BD 2018 : VI Jornadas de Cloud Computing & Big Data

Se recopilan las ponencias presentadas en las VI Jornadas de Cloud Computing & Big Data (JCC&BD), realizadas entre el 25 al 29 de junio de 2018 en la Facultad de Informática de la Universidad Nacional de La Plata.Universidad Nacional de La Plata (UNLP) - Facultad de Informátic

Wildfire Spreading: a new application of the Beta distribution

This dissertation is in the mathematical physics area, more specifically, applications in the statistics field. The thesis, under the supervision of Dr. Gianni Pagnini, was carried out at the BCAM - Basque Centre for Applied Mathematics in Bilbao, Spain. It is the result of the continuous interaction with the team of Statistical Physics, characterised by an international, stimulating and constantly growing environment. The subject of this thesis is PROPAGATOR: a stochastic cellular automaton model for forest fire spread simulation, conceived as a rapid method for fire risk assessment. The reason behind the popularity of cellular automata can be traced to their simplicity, and to the enormous potential they hold in modeling complex systems, in spite of their simplicity. Cellular automata can be viewed as a simple model of a spatially extended decentralized system made up of a number of individual components: cells. The communication between constituent cells is limited to local interaction. PROPAGATOR is a cellular automata model which simulates wildfire spread through empirical laws that guarantee probabilistic outputs. This algorithm, whose first version was released in 2009, is currently in use, along with other software, although it is constantly being updated. In fact, the first version was requested by the Italian Civil Protection, but later it became part of the ANYWHERE project. This project, active from June 2016 to December 2019, was funded under the EU’s research and innovation funding program Horizon 2020 (H2020), which aimed to improve emergency management and response to high-impact weather and climate events such as floods, landslides, swells, snowfalls, forest fires, heat waves and droughts. As part of the ANYWHERE project, Propagator was rewritten in Python. The version we worked with is the 2020 version, but an updated 2022 version is already available. The main aim of this work was to understand the distribution of the wildfire propagation. As can be seen from Propagator input parameters, the propagation depends on different factors: ignition point, wind speed and direction, as well as fuel moisture content and firebreaks-fire fighting strategies. Wind is recognized to be by far the most important factor in the entire problem of forest fire propagation. In this paper, we analyzed four different situations varying initial conditions, in particular we changed wind speed: 0 km/h, 10 km/h, 20 km/h, 30 km/h. However, the phenomenon of fire spotting and firebreaks-fire fighting strategies were not taken into consideration. By modifying the code, it was possible to obtain the output required to achieve the desired result. The conclusion we came to is that the distribution of a wildfire spreading is described by the beta distribution. This allows us, for the first time, to attribute a new application of the beta function: describing the propagation of a process studied using a cellular automaton algorithm. The thesis is organised as follows: In the first chapter, there is an introduction to special functions. In particular, their role in applied mathematics is analyzed, followed by a discussion of the two most commonly used special functions: the Gamma function and the Beta function. • In the second chapter, the PROPAGATOR model was introduced following the article "PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator" by A. Trucchia. • The third chapter contains the analysis carried out on the output data. A discussion of the obtained results and suitable observations can be found in the conclusions. • There are three appendixes containing: – Appendix A: the lines of code we wrote to carry out the analysis. – Appendix B: explanation of the software, apps and routines used, with particular reference to the Hypathia server. – Appendix C: discussion on stochastic processes carried out as an approach and preparation for the subsequent work with Propagator

Predictive analytics applied to firefighter response, a practical approach

Time is a crucial factor for the outcome of emergencies, especially those that involve human lives. This paper looks at Lisbon’s firefighter’s occurrences and presents a model,based on city characteristics and climacteric data, to predict whether there will be an occurrence at a certain location, according to the weather forecasts. In this study three algorithms were considered, Logistic Regression, Decision Tree and Random Forest.Measured by the AUC, the best performant modelwasa random forestwith random under-sampling at 0.68. This model was well adjusted across the city and showed that precipitation and size of the subsection are themost relevant featuresin predicting firefighter’s occurrences.The work presented here has clear implications on the firefighter’s decision-makingregarding vehicle allocation, as now they can make an informed decision considering the predicted occurrences