Short-term fire front spread prediction using inverse modelling and airborne infrared images

A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.Peer ReviewedPostprint (author's final draft

Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters

Demands on the disaster response capacity of the European Union are likely to increase, as the impacts of disasters continue to grow both in size and frequency. This has resulted in intensive research on issues concerning spatially-explicit information and modelling and their multiple sources of uncertainty. Geospatial support is one of the forms of assistance frequently required by emergency response centres along with hazard forecast and event management assessment. Robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources. Uncertainty is associated with meteorological forecast and calibration of the model parameters. Software uncertainty also derives from the data transformation models (D-TM) needed for predicting hazard behaviour and its consequences. On the other hand, social contributions have recently been recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. Here an architecture overview is proposed for adaptive and robust modelling of natural hazards, following the Semantic Array Programming paradigm to also include the distributed array of social contributors called Citizen Sensor in a semantically-enhanced strategy for D-TM modelling. The modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema and complementing more traditional and accurate a-posteriori assessment. We discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on High Performance Computing (HPC) platforms, in order for the implications of urgency to be introduced into the systems (Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua

Tools for Assessing Climate Impacts on Fish and Wildlife

Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management

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

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Data Assimilation for Spatial Temporal Simulations Using Localized Particle Filtering

As sensor data becomes more and more available, there is an increasing interest in assimilating real time sensor data into spatial temporal simulations to achieve more accurate simulation or prediction results. Particle Filters (PFs), also known as Sequential Monte Carlo methods, hold great promise in this area as they use Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from some given observations. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. As the state space dimension increases, the number of particles must increase exponentially in order to converge to the true system state. The purpose of this dissertation work is to develop localized particle filtering to support PFs-based data assimilation for large-scale spatial temporal simulations. We develop a spatially dependent particle-filtering framework that breaks the system state and observation data into sub-regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, we propose a two-level automated spatial partitioning method to provide optimized and balanced spatial partitions with less boundary sensors. We also consider different types of data to effectively support data assimilation for spatial temporal simulations. These data include both hard data, which are measurements from physical devices, and soft data, which are information from messages, reports, and social network. The developed framework and methods are applied to large-scale wildfire spread simulations and achieved improved results. Furthermore, we compare the proposed framework to existing particle filtering based data assimilation frameworks and evaluate the performance for each of them

Data Assimilation Based on Sequential Monte Carlo Methods for Dynamic Data Driven Simulation

Simulation models are widely used for studying and predicting dynamic behaviors of complex systems. Inaccurate simulation results are often inevitable due to imperfect model and inaccurate inputs. With the advances of sensor technology, it is possible to collect large amount of real time observation data from real systems during simulations. This gives rise to a new paradigm of Dynamic Data Driven Simulation (DDDS) where a simulation system dynamically assimilates real time observation data into a running model to improve simulation results. Data assimilation for DDDS is a challenging task because sophisticated simulation models often have: 1) nonlinear non-Gaussian behavior 2) non-analytical expressions of involved probability density functions 3) high dimensional state space 4) high computation cost. Due to these properties, most existing data assimilation methods fail to effectively support data assimilation for DDDS in one way or another. This work develops algorithms and software to perform data assimilation for dynamic data driven simulation through non-parametric statistic inference based on sequential Monte Carlo (SMC) methods (also called particle filters). A bootstrap particle filter based data assimilation framework is firstly developed, where the proposal distribution is constructed from simulation models and statistical cores of noises. The bootstrap particle filter-based framework is relatively easy to implement. However, it is ineffective when the uncertainty of simulation models is much larger than the observation model (i.e. peaked likelihood) or when rare events happen. To improve the effectiveness of data assimilation, a new data assimilation framework, named as the SenSim framework, is then proposed, which has a more advanced proposal distribution that uses knowledge from both simulation models and sensor readings. Both the bootstrap particle filter-based framework and the SenSim framework are applied and evaluated in two case studies: wildfire spread simulation, and lane-based traffic simulation. Experimental results demonstrate the effectiveness of the proposed data assimilation methods. A software package is also created to encapsulate the different components of SMC methods for supporting data assimilation of general simulation models