scholarly journals Modeling Ground Firefighting Resource Activities to Manage Risk Given Uncertain Weather

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1077 ◽  
Author(s):  
Erin J. Belval ◽  
Yu Wei ◽  
Michael Bevers
Keyword(s):  
Fire Behavior ◽  
Mixed Integer ◽  
Test Cases ◽  
Control Line ◽  
Fire Growth ◽  
Complex Decision ◽  
Line Construction ◽  
Wildland Firefighting ◽  
And Behavior ◽  
Decision Environments

Wildland firefighting requires managers to make decisions in complex decision environments that hold many uncertainties; these decisions need to be adapted dynamically over time as fire behavior evolves. Models used in firefighting decisions should also have the capability to adapt to changing conditions. In this paper, detailed line construction constraints are presented for use with a stochastic mixed integer fire growth and behavior program. These constraints allow suppression actions to interact dynamically with stochastic predicted fire behavior and account for many of the detailed line construction considerations. Such considerations include spatial restrictions for fire crew travel and operations. Crew safety is also addressed; crews must keep a variable safety buffer between themselves and the fire. Fireline quality issues are accounted for by comparing control line capacity with fireline intensity to determine when a fireline will hold. The model assumes crews may work at varying production rates throughout their shifts, providing flexibility to fit work assignments with the predicted fire behavior. Nonanticipativity is enforced to ensure solutions are feasible for all modeled weather scenarios. Test cases demonstrate the model’s utility and capability on a raster landscape.

A stochastic mixed integer program to model spatial wildfire behavior and suppression placement decisions with uncertain weather

2016 ◽  
Vol 46 (2) ◽  
pp. 234-248 ◽  
Author(s):  
Erin J. Belval ◽  
Yu Wei ◽  
Michael Bevers
Keyword(s):  
Wildland Fire ◽  
Fire Behavior ◽  
Integer Program ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Weather Forecasts ◽  
Placement Decisions ◽  
Weather Events ◽  
Weather Changes ◽  
Nonanticipativity Constraints

Wildfire behavior is a complex and stochastic phenomenon that can present unique tactical management challenges. This paper investigates a multistage stochastic mixed integer program with full recourse to model spatially explicit fire behavior and to select suppression locations for a wildland fire. Simplified suppression decisions take the form of “suppression nodes”, which are placed on a raster landscape for multiple decision stages. Weather scenarios are used to represent a distribution of probable changes in fire behavior in response to random weather changes, modeled using probabilistic weather trees. Multistage suppression decisions and fire behavior respond to these weather events and to each other. Nonanticipativity constraints ensure that suppression decisions account for uncertainty in weather forecasts. Test cases for this model provide examples of fire behavior interacting with suppression to achieve a minimum expected area impacted by fire and suppression.

scholarly journals Stochastic Constraint Propagation for Mining Probabilistic Networks

2019 ◽  
Author(s):  
Anna Louise D. Latour ◽  
Behrouz Babaki ◽  
Siegfried Nijssen
Keyword(s):  
Data Mining ◽  
Probability Distributions ◽  
Constraint Propagation ◽  
Mixed Integer ◽  
Test Cases ◽  
Decision Diagrams ◽  
Probabilistic Networks ◽  
Ordered Binary Decision Diagrams ◽  
Binary Decision ◽  
Domain Consistency

A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.

scholarly journals The Survival of Pinus ponderosa Saplings Subjected to Increasing Levels of Fire Behavior and Impacts on Post-Fire Growth

Fire ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 23 ◽  
Author(s):  
Wade D. Steady ◽  
Raquel Partelli Feltrin ◽  
Daniel M. Johnson ◽  
Aaron M. Sparks ◽  
Crystal A. Kolden ◽  
...  
Keyword(s):  
Dose Response ◽  
Pinus Ponderosa ◽  
Ponderosa Pine ◽  
Fire Hazard ◽  
Fire Behavior ◽  
Forest Growth ◽  
Growth And Yield ◽  
Radiative Energy ◽  
Fire Growth ◽  
Growth Metrics

Improved predictions of tree species mortality and growth metrics following fires are important to assess fire impacts on forest succession, and ultimately forest growth and yield. Recent studies have shown that North American conifers exhibit a ‘toxicological dose-response’ relationship between fire behavior and the resultant mortality or recovery of the trees. Prior studies have not been conclusive due to potential pseudo-replication in the experimental design and time-limited observations. We explored whether dose-response relationships are observed in ponderosa pine (Pinus ponderosa) saplings exposed to surface fires of increasing fire behavior (as quantified by Fire Radiative Energy—FRE). We confirmed equivalent dose-response relationships to the prior studies that were focused on other conifer species. The post-fire growth in the saplings that survived the fires decreased with increasing FRE dosages, while the percentage mortality in the sapling dosage groups increased with the amount of FRE applied. Furthermore, as with lodgepole pine (Pinus contorta), a low FRE dosage could be applied that did not yield mortality in any of the replicates (r = 10). These results suggest that land management agencies could use planned burns to reduce fire hazard while still maintaining a crop of young saplings. Incorporation of these results into earth-system models and growth and yield models could help reduce uncertainties associated with the impacts of fire on timber growth, forest resilience, carbon dynamics, and ecosystem economics.

scholarly journals A Model for Compartment Fire Behavior Incorporating Fire Growth and Vitiation

2011 ◽  
Vol 10 ◽  
pp. 1249-1261
Author(s):  
D. Goto ◽  
Yoshifumi Ohmiya ◽  
M. Delichatsios
Keyword(s):  
Fire Behavior ◽  
Compartment Fire ◽  
Fire Growth

Structural Fire Calculations

1997 ◽  
Author(s):  
David G. Lilley
Keyword(s):  
Heat Release ◽  
Fire Behavior ◽  
Empirical Equations ◽  
Fire Growth ◽  
Release Rates ◽  
Structural Fire ◽  
Structural Fire Behavior ◽  
Structural Fires

Abstract Information and calculations are given for estimating fire growth in structural fires. Heat release rates from flames, fire growth, ignition of nearby items, and the possibility of flashover are all topics of concern. Empirical equations are given and calculations are exhibited to illustrated these aspects of structural fire behavior.

Flashover: A Study of Parameter Effects on the Time to Reach Flashover Conditions

1997 ◽  
Author(s):  
Hyeong-Jin Kim ◽  
David G. Lilley
Keyword(s):  
Growth Rate ◽  
Fire Behavior ◽  
Fire Growth ◽  
Present Contribution ◽  
Single Room ◽  
Rapid Transition ◽  
Time Required ◽  
In Fire

Abstract Flashover is characterized by the rapid transition in fire behavior from localized burning of fuel to the involvement of all combustibles in the enclosure. The objective of the present contribution is to calculate the development of flashover in a typical single room fire, and show the effect of ten key parameters on the time required to reach flashover conditions. It is found that the major parameters affecting flashover are fire growth rate, ventilation opening area, and room area.

Optimization of Test Cases in Object-Oriented Systems Using Fractional-SMO

2021 ◽  
Vol 12 (1) ◽  
pp. 41-59
Author(s):  
Satya Sobhan Panigrahi ◽  
Ajay Kumar Jena
Keyword(s):  
Unified Modeling Language ◽  
Boundary Structure ◽  
Test Case ◽  
Test Cases ◽  
Unified Modeling ◽  
Efficient Test ◽  
Fitness Value ◽  
Fitness Parameters ◽  
And Behavior ◽  
Object Oriented Systems

This paper introduces the technique to select the test cases from the unified modeling language (UML) behavioral diagram. The UML behavioral diagram describes the boundary, structure, and behavior of the system that is fed as input for generating the graph. The graph is constructed by assigning the weights, nodes, and edges. Then, test case sequences are created from the graph with minimal fitness value. Then, the optimal sequences are selected from the proposed fractional-spider monkey optimization (fractional-SMO). The developed fractional-SMO is designed by integrating fractional calculus and SMO. Thus, the efficient test cases are selected based on the optimization algorithm that uses fitness parameters, like coverage and fault. Simulations are performed via five synthetic UML diagrams taken from the dataset. The performance of the proposed technique is computed using coverage and the number of test cases. The maximal coverage of 49 and the minimal number of test cases as 2,562 indicate the superiority of the proposed technique.

scholarly journals Weather, Risk, and Resource Orders on Large Wildland Fires in the Western US

Forests ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 169 ◽  
Author(s):  
Jude Bayham ◽  
Erin J. Belval ◽  
Matthew P. Thompson ◽  
Christopher Dunn ◽  
Crystal S. Stonesifer ◽  
...  
Keyword(s):  
At Risk ◽  
Fire Behavior ◽  
Weather Conditions ◽  
Primary Objective ◽  
Management Teams ◽  
Incident Management ◽  
Additional Risk Factor ◽  
Fire Growth ◽  
Landscape Characteristics ◽  
Western Us

Research Highlights: Our results suggest that weather is a primary driver of resource orders over the course of extended attack efforts on large fires. Incident Management Teams (IMTs) synthesize information about weather, fuels, and order resources based on expected fire growth rather than simply reacting to observed fire growth. Background and Objectives: Weather conditions are a well-known determinant of fire behavior and are likely to become more erratic under climate change. Yet, there is little empirical evidence demonstrating how IMTs respond to observed or expected weather conditions. An understanding of weather-driven resource ordering patterns may aid in resource prepositioning as well as forecasting suppression costs. Our primary objective is to understand how changing weather conditions influence resource ordering patterns. Our secondary objective is to test how an additional risk factor, evacuation, as well as a constructed risk metric combining fire growth and evacuation, influences resource ordering. Materials and Methods: We compile a novel dataset on over 1100 wildfires in the western US from 2007–2013, integrating data on resource requests, detailed weather conditions, fuel and landscape characteristics, values at risk, fire behavior, and IMT expectations about future fire behavior and values at risk. We develop a two-step regression framework to investigate the extent to which IMTs respond to realized or expected weather-driven fire behavior and risks. Results: We find that IMTs’ expectations about future fire growth are influenced by observed weather and that these expectations influence resource ordering patterns. IMTs order nearly twice as many resources when weather conditions are expected to drive growth events in the near future. However, we find little evidence that our other risk metrics influence resource ordering behavior (all else being equal). Conclusion: Our analysis shows that incident management teams are generally forward-looking and respond to expected rather than recently observed weather-driven fire behavior. These results may have important implications for forecasting resource needs and costs in a changing climate.

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