Analysing initial attack on wildland fires using stochastic simulation

2006 ◽  
Vol 15 (1) ◽  
pp. 137 ◽  
Author(s):  
Jeremy S. Fried ◽  
J. Keith Gilless ◽  
James Spero
Keyword(s):  
Stochastic Simulation ◽  
Fire Protection ◽  
Wildland Fire ◽  
Simulation Models ◽  
Stochastic Simulations ◽  
Initial Attack ◽  
Key Issues ◽  
Stochastic Simulation Model ◽  
The Cost ◽  
The Impact

Stochastic simulation models of initial attack on wildland fire can be designed to reflect the complexity of the environmental, administrative, and institutional context in which wildland fire protection agencies operate, but such complexity may come at the cost of a considerable investment in data acquisition and management. This cost may be well justified when it allows for analysis of a wider spectrum of operational problems in wildland fire protection planning. The California Fire Economics Simulator version 2 (CFES2), is a sophisticated stochastic simulation model designed to facilitate quantitative analysis of the potential effects of changes in many key components of most wildland fire systems, e.g. availability and stationing of resources, dispatch rules, criteria for setting fire dispatch level, staff schedules, and deployment and line-building tactics. The CFES2 model can also be used to support strategic planning with respect to vegetation management programs, development at the wildland–urban interface, reallocation of responsibilities among fire protection agencies, and climatic change. The analytical capacity of stochastic simulations models to address such key issues is demonstrated using the CFES2 model in four case studies addressing the impact on initial attack effectiveness of: (1) multiple fire starts; (2) diversion of firefighting resources to structure protection; (3) alternate stationing of firefighting resources; and (4) multi-agency cooperation.

Stochastic simulation for determining the design flood of cascade reservoir systems

2012 ◽  
Vol 43 (1-2) ◽  
pp. 54-63 ◽  
Author(s):  
Baohong Lu ◽  
Huanghe Gu ◽  
Ziyin Xie ◽  
Jiufu Liu ◽  
Lejun Ma ◽  
...  
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
Southern China ◽  
Flow Simulation ◽  
Simulation Method ◽  
Simulation Approach ◽  
Design Flood ◽  
Design Values ◽  
Stochastic Simulation Model ◽  
The Impact

Stochastic simulation is widely applied for estimating the design flood of various hydrosystems. The design flood at a reservoir site should consider the impact of upstream reservoirs, along with any development of hydropower. This paper investigates and applies a stochastic simulation approach for determining the design flood of a complex cascade of reservoirs in the Longtan watershed, southern China. The magnitude of the design flood when the impact of the upstream reservoirs is considered is less than that without considering them. In particular, the stochastic simulation model takes into account both systematic and historical flood records. As the reliability of the frequency analysis increases with more representative samples, it is desirable to incorporate historical flood records, if available, into the stochastic simulation model. This study shows that the design values from the stochastic simulation method with historical flood records are higher than those without historical flood records. The paper demonstrates the advantages of adopting a stochastic flow simulation approach to address design-flood-related issues for a complex cascade reservoir system.

scholarly journals Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava

2019 ◽  
Vol 11 (24) ◽  
pp. 7098 ◽  
Author(s):  
Jiri Horak ◽  
Jan Tesla ◽  
David Fojtik ◽  
Vit Vozenilek
Keyword(s):  
Monte Carlo ◽  
Public Transport ◽  
Simulation Models ◽  
Stochastic Simulations ◽  
Time Interval ◽  
Real World Data ◽  
Transport Modelling ◽  
Transport Accessibility ◽  
Commuting Time ◽  
The Impact

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin–destination (O–D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance–decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.

The impact of male circumcision on HIV incidence and cost per infection prevented: a stochastic simulation model from Rakai, Uganda

AIDS ◽  
2007 ◽  
Vol 21 (7) ◽  
pp. 845-850 ◽  
Author(s):  
Ronald H Gray ◽  
Xianbin Li ◽  
Godfrey Kigozi ◽  
David Serwadda ◽  
Fred Nalugoda ◽  
...  
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
Male Circumcision ◽  
Hiv Incidence ◽  
Stochastic Simulation Model ◽  
The Impact

scholarly journals Factors influencing fire suppression success in the province of Quebec (Canada)

2019 ◽  
Vol 49 (5) ◽  
pp. 531-542 ◽  
Author(s):  
A. Cardil ◽  
M. Lorente ◽  
D. Boucher ◽  
J. Boucher ◽  
S. Gauthier
Keyword(s):  
Forest Fire ◽  
Fire Protection ◽  
Fire Regime ◽  
Fire Suppression ◽  
Fire Intensity ◽  
Fuel Type ◽  
Initial Attack ◽  
Fire Size ◽  
The Impact ◽  
Forest Fire Protection

In the managed forest of Canada, forest fires are actively suppressed through efficient initial attack capability; however, the impact of different factors on the suppression success remains to be understood. The aim of this paper was to analyze the influence of operational suppression objectives (fire detection, initial attack, and fire control) along with fire intensity, fuel type, fire ignition cause, year, workload, and homogeneous fire regime zones on the achievement of the fire suppression objective (fire < 3 ha) using the Forest Fire Protection Agency of Quebec (SOPFEU) as a case study. The overall success of the suppression objective was very high (88%) over the study period (1994–2015). Both detection and control had significant effects on the suppression success through their interaction with fuel type, ignition cause, fire intensity, and zone variables. When the suppression objective was not achieved, final fire size was influenced by control, fuel type, fire intensity, and zone. The paper highlights the importance of the operational objectives and of regional differences for both fire suppression success and final fire size. Our results can help forest fire protection agencies to better understand their wildland fire suppression systems for a better adaptation to the upcoming fire regime changes.

scholarly journals Topographic uncertainty quantification for flow-like landslide models via stochastic simulations

2020 ◽  
Vol 20 (5) ◽  
pp. 1441-1461
Author(s):  
Hu Zhao ◽  
Julia Kowalski
Keyword(s):  
Stochastic Simulation ◽  
Computational Models ◽  
Extreme Values ◽  
Stochastic Simulations ◽  
Systematic Bias ◽  
Potential Hazard ◽  
Landslide Event ◽  
Dem Uncertainty ◽  
The Impact ◽  
Conditional Stochastic Simulation

Abstract. Digital elevation models (DEMs) representing topography are an essential input for computational models capable of simulating the run-out of flow-like landslides. Yet, DEMs are often subject to error, a fact that is mostly overlooked in landslide modeling. We address this research gap and investigate the impact of topographic uncertainty on landslide run-out models. In particular, we will describe two different approaches to account for DEM uncertainty, namely unconditional and conditional stochastic simulation methods. We investigate and discuss their feasibility, as well as whether DEM uncertainty represented by stochastic simulations critically affects landslide run-out simulations. Based upon a historic flow-like landslide event in Hong Kong, we present a series of computational scenarios to compare both methods using our modular Python-based workflow. Our results show that DEM uncertainty can significantly affect simulation-based landslide run-out analyses, depending on how well the underlying flow path is captured by the DEM, as well as on further topographic characteristics and the DEM error's variability. We further find that, in the absence of systematic bias in the DEM, a performant root-mean-square-error-based unconditional stochastic simulation yields similar results to a computationally intensive conditional stochastic simulation that takes actual DEM error values at reference locations into account. In all other cases the unconditional stochastic simulation overestimates the variability in the DEM error, which leads to an increase in the potential hazard area as well as extreme values of dynamic flow properties.

scholarly journals Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning

2021 ◽  
Vol 4 ◽  
Author(s):  
Cristobal Pais ◽  
Jaime Carrasco ◽  
David L. Martell ◽  
Andres Weintraub ◽  
David L. Woodruff
Keyword(s):  
Forest Fire ◽  
Wildland Fire ◽  
Fire Behavior ◽  
Landscape Management ◽  
Simulation Models ◽  
Fire Growth ◽  
Management Planning ◽  
Spatial And Temporal Scales ◽  
Growth Simulator ◽  
The Impact

Cell2Fire is a new cell-based wildland fire growth simulator designed to integrate data-driven landscape management planning models. The fire environment is modeled by partitioning the landscape into cells characterized by fuel, weather, moisture content, and topographic attributes. The model can use existing fire spread models such as the Canadian Forest Fire Behavior Prediction System to model fire growth. Cell2Fire is structured to facilitate its use for predicting the growth of individual fires or by embedding it in landscape management simulation models. Decision-making models such as fuel treatment/harvesting plans can be easily integrated and evaluated. It incorporates a series of out-of-the-box planning heuristics that provide benchmarks for comparison. We illustrate their use by applying and evaluating a series of harvesting plans for forest landscapes in Canada. We validated Cell2Fire by using it to predict the growth of both real and hypothetical fires, comparing our predictions with the fire scars produced by a validated fire growth simulator (Prometheus). Cell2Fire is implemented as an open-source project that exploits parallelism to efficiently support the modeling of fire growth across large spatial and temporal scales. Our experiments indicate that Cell2Fire is able to efficiently simulate wildfires (up to 30x faster) under different conditions with similar accuracy as state-of-the-art simulators (above 90% of accuracy). We demonstrate its effectiveness as part of a harvest planning optimization framework, identifying relevant metrics to capture and actions to mitigate the impact of wildfire uncertainty.

The Impact of Burn-In on IC Reliability

1986 ◽  
Vol 29 (1) ◽  
pp. 19-23
Author(s):  
Donald Denton ◽  
Donald Blythe
Keyword(s):  
Cost Effectiveness ◽  
Integrated Circuits ◽  
Failure Rate ◽  
User Satisfaction ◽  
End User ◽  
Equipment Manufacturer ◽  
Rate Improvement ◽  
Key Issues ◽  
The Cost ◽  
The Impact

In today's highly competitive electronic equipment market, one of the key issues facing equipment manufacturers is whether or not to burn-in integrated circuits. This decision has both reliability and financial implications and may well affect end user satisfaction and repeat business. Unfortunately, there is no simple answer to the question of burn-in. To help develop guidelines for equipment manufacturers to use, 1982-1984 Tl reliability data has been analyzed to provide failure rate improvement factors for burn-in. Using this data, an approach is developed to help the equipment manufacturer make a decision regarding the cost effectiveness of component burn-in.

The Impact of Asynchronous GPGPU Behaviors on Stochastic Simulation

2013 ◽  
Author(s):  
John C. Steuben ◽  
Cameron J. Turner
Keyword(s):  
Stochastic Simulation ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Computing Methods ◽  
Stochastic Simulations ◽  
Processing Unit ◽  
Robot Swarm ◽  
Gpgpu Computing ◽  
Global Clock ◽  
The Impact

This work examines the effect of one key aspect of General Purpose Graphics Processing Unit (GPGPU) computing on the realism and fidelity of stochastic simulations. In particular it is shown that the asynchronous nature of GPGPU computing can be leveraged to produce increased fidelity and realism, compared to conventional computing methods, when applied to probabilistic or stochastic simulations. This is a multifaceted argument that shows: 1) Asynchronous behaviors are essential to produce high computational throughput on GPGPU devices, and thus allow more rigorous sampling, which in turn enables a deeper understanding of the underlying stochastic processes. 2) Asynchronous GPGPU computing can eliminate the “global clock” present in simulations and potentially produce a better representation of the underlying process. This paper also attempts to give a working introduction to GPGPU computing, and to the applications of this technology in the field of stochastic simulation. A range of literature regarding these simulations is also surveyed, in order to provide context. A demonstration of synchronous versus asynchronous algorithms for robot swarm path planning is used to illustrate this discussion. Several notes on the limitations of GPGPU computing in this field are also made, along with remarks regarding future development of GPGPU-accelerated stochastic simulations.

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