A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

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

Forest fire propagation prediction based on overlapping DDDAS forecasts

International Conference on Computational Science, ICCS 2015 – Computational Science at the Gates of NatureForest fire devastate every year thousand of hectares of forest around the world. Fire behavior prediction is a useful tool to aid coordination and management of human and mitigation resources when fighting against these kind of hazards. Any fire spread forecast system requires to be fitted with different kind of data with a high degree of uncertainty, such as for example, me- teorological data and vegetation map among others. The dynamics of this kind of phenomena requires to develop a forecast system with the ability to adapt to changing conditions. In this work two different fire spread forecast systems based on the Dynamic Data Driven Application paradigm are applied and an alternative approach based on the combination of both predictions is presented. This new method uses the computational power provided by high performance computing systems to deliver the predictions under strict real time constraints.This research has been supported by the Ministerio de Economía y Competitividad (MECSpain) under contract TIN2011-28689-C02-01 and the Catalan government under grant 2014- SGR-576

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

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Cloud computing application model for online recommendation through fuzzy logic system

Cloud computing can offer us different distance services over the internet. We propose an online application model for health care systems that works by use of cloud computing. It can provide a higher quality of services remotely and along with that, it decreases the cost of chronic patient. This model is composed of two sub-model that each one uses a different service, one of these is software as a service (SaaS) which is user related and another one is Platform as a service (PaaS) that is engineer related. Doctors classify the chronic diseases into different stages according to their symptoms. As the clinical data has a non-numeric value, we use the fuzzy logic system in Paas model to design this online application model. Based on this classification, patienst can receive the proper recommendation through smart devices (SaaS model).Facultad de Informátic

Cloud computing application model for online recommendation through fuzzy logic system

Cloud computing can offer us different distance services over the internet. We propose an online application model for health care systems that works by use of cloud computing. It can provide a higher quality of services remotely and along with that, it decreases the cost of chronic patient. This model is composed of two sub-model that each one uses a different service, one of these is software as a service (SaaS) which is user related and another one is Platform as a service (PaaS) that is engineer related. Doctors classify the chronic diseases into different stages according to their symptoms. As the clinical data has a non-numeric value, we use the fuzzy logic system in Paas model to design this online application model. Based on this classification, patienst can receive the proper recommendation through smart devices (SaaS model).Facultad de Informátic

Cloud computing application model for online recommendation through fuzzy logic system

Cloud computing can offer us different distance services over the internet. We propose an online application model for health care systems that works by use of cloud computing. It can provide a higher quality of services remotely and along with that, it decreases the cost of chronic patient. This model is composed of two sub-model that each one uses a different service, one of these is software as a service (SaaS) which is user related and another one is Platform as a service (PaaS) that is engineer related. Doctors classify the chronic diseases into different stages according to their symptoms. As the clinical data has a non-numeric value, we use the fuzzy logic system in Paas model to design this online application model. Based on this classification, patienst can receive the proper recommendation through smart devices (SaaS model).Facultad de Informátic