A Stochastic Programming Model for Fuel Treatment Management

This work considers a two-stage stochastic integer programming (SIP) approach for optimizing fuel treatment planning under uncertainty in weather and fire occurrence for rural forests. Given a set of areas for potentially performing fuel treatment, the problem is to decide the best treatment option for each area under uncertainty in future weather and fire occurrence. A two-stage SIP model is devised whose objective is to minimize the here-and-now cost of fuel treatment in the first-stage, plus the expected future costs due to uncertain impact from potential fires in the second-stage calculated as ecosystem services losses. The model considers four fuel treatment options: no treatment, mechanical thinning, prescribed fire, and grazing. Several constraints such as budgetary and labor constraints are included in the model and a standard fire behavior model is used to estimate some of the parameters of the model such as fuel levels at the beginning of the fire season. The SIP model was applied to data for a study area in East Texas with 15 treatment areas under different weather scenarios. The results of the study show, for example, that unless the expected ecosystem services values for an area outweigh fuel treatment costs, no treatment is the best choice for the area. Thus the valuation of the area together with the probability of fire occurrence and behavior strongly drive fuel treatment choices

Stochastic Programming Model for Fuel Treatment Management

Due to the increased number and intensity of wild fires, the need for solutions that minimize the impact of fire are needed. Fuel treatment is one of the methods used to mitigate the effects of fire at a certain area. In this thesis, a two-stage stochastic programming model for fuel treatment management is constructed. The model optimizes the selection of areas for fuel treatment under budget and man-hour constraints. The process makes use of simulation tools like PHYGROW, which mimics the growth of vegetation after treatment, and FARSITE, which simulates the behavior of fire. The model minimizes the costs of fuel treatment as well as the potential losses when fire occurs. Texas Wild re Risk Assessment Model (TWRA) used by Texas Forest Service (TFS) is used to quantify risk at each area. The model is applied at TX 12, which is a re planning unit under the administration of TFS. Results show that the total of the expenditures on fuel treatment and the expected impact justify the efforts of fuel treatment

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

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

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit

Development of Multi-objective Optimization Model of Community Resilience on Mitigation Planning

Mitigation planning in many disaster-prone areas has shown success in helping the community to withstand hazardous events, reducing the recovery time and costs, and preventing life losses. This research proposes a multi-objective optimization framework to enhance decision-making to mitigate risk from potential hazards in an integrated and quantitative manner. First, this study introduces an optimization framework that can integrate different dimensions of community resilience in one model as competing objectives to measure the potential impacts and damage from hazard events. To the best of our knowledge, this framework is the only framework that can provide flexibility on some major components. The decision makers can apply the proposed framework to various hazards without changing the mathematical formulation. The framework's objectives can be determined by the people who are involved in decision-making. Moreover, the number of objectives also can vary according to the actual needs of decision makers. Second, the proposed framework is applied to tornado mitigation in the city of Joplin, Missouri, USA, to demonstrate how the retrofitting strategies reduce the potential impacts of direct economic loss (economic dimension), population dislocation (social dimension), and building functionality (physical infrastructure). The results analyses illustrate how the decision makers can utilize the information from the optimal solutions to determine the appropriate retrofitting solution for the community. Finally, a machine learning (ML) model is developed to predict potential economic damage on domestic supply, employment, migration, and household income by using input data of the computable general equilibrium (CGE) model. This ML model can act as a surrogate model to help the non-CGE expert to interpret the relationship between the capital shock by sector and economic impact from hazards shock on capitals. The predicted impact on domestical supply, employment, migration, and household income from this ML model can act as coefficients of objectives functions (domestical supply, employment, migration, and household income) of the proposed multi-objective optimization model

Control of spatio-temporal pattern formation governed by geometrical models of interface evolution

Numerous natural phenomena are characterized by spatio-temporal dynamics which give rise to time evolving spatial patterns. Although studies that address the problem of modelling these complex dynamics exist, a model based control approach for such systems is still a challenging task. The work in this thesis is concerned with the development of control methods for such spatio-temporal systems, where interface growth is represented using a geometric evolution law. In particular, the focus is set on the control of dendritic crystal growth and wind-aided wildfire sprea

The global tree carrying capacity (keynote)

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