Enhancing S4 with Guidance from the Features of Other Behavior Modeling Systems

2010 ◽  
Author(s):  
Daniel N. Cassenti ◽  
Charneta L. Samms
Keyword(s):  
Behavior Modeling ◽  
Modeling Systems

scholarly journals Observed versus predicted fire behavior in an Alaskan black spruce forest ecosystem: an experimental fire case study

Fire Ecology ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Stacy A. Drury
Keyword(s):  
Black Spruce ◽  
Fire Behavior ◽  
Flame Length ◽  
Behavior Modeling ◽  
Fire Intensity ◽  
Modeling Tools ◽  
Rate Of Spread ◽  
Fire Behavior Modeling ◽  
Modeling Systems ◽  
Hazard And Risk

Abstract Background Fire managers tasked with assessing the hazard and risk of wildfire in Alaska, USA, tend to have more confidence in fire behavior prediction modeling systems developed in Canada than similar systems developed in the US. In 1992, Canadian fire behavior systems were adopted for modeling fire hazard and risk in Alaska and are used by fire suppression specialists and fire planners working within the state. However, as new US-based fire behavior modeling tools are developed, Alaskan fire managers are encouraged to adopt the use of US-based systems. Few studies exist in the scientific literature that inform fire managers as to the efficacy of fire behavior modeling tools in Alaska. In this study, I provide information to aid fire managers when tasked with deciding which system for modeling fire behavior is most appropriate for their use. On the Magitchlie Creek Fire in Alaska, I systematically collected fire behavior characteristics within a black spruce (Picea mariana [Mill.] Britton, Sterns & Poggenb.) ecosystem under head fire conditions. I compared my fire behavior observations including flame length, rate of spread, and head fire intensity with fire behavior predictions from the US fire modeling system BehavePlus, and three Canadian systems: RedAPP, CanFIRE, and the Crown Fire Initiation and Spread system (CFIS). Results All four modeling systems produced reasonable rate of spread predictions although the Canadian systems provided predictions slightly closer to the observed fire behavior. The Canadian fire behavior prediction modeling systems RedAPP and CanFIRE provided more accurate predictions of head fire intensity and fire type than BehavePlus or CFIS. Conclusions The most appropriate fire behavior modeling system for use in Alaskan black spruce ecosystems depends on what type of questions are being asked. For determining the rate of fire movement across a landscape, REDapp, CanFIRE, CFIS, or BehavePlus can all be expected to provide reasonably accurate estimates of rate of spread. If fire managers are interested in using predicted flame length or energy produced for informing decisions such as which firefighting tactics will be successful, or for evaluating the ecological impacts due to burning, then the Canadian fire modeling systems outperformed BehavePlus in this case study.

scholarly journals firebehavioR: An R Package for Fire Behavior and Danger Analysis

Fire ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 41
Author(s):  
Justin P. Ziegler ◽  
Chad M. Hoffman ◽  
William Mell
Keyword(s):  
Programming Language ◽  
Wildland Fire ◽  
Fire Behavior ◽  
R Package ◽  
Technical Note ◽  
Empirical Modeling ◽  
Behavior Modeling ◽  
Modeling Systems ◽  
R Programming ◽  
Data Objects

Wildland fire and ecological researchers use empirical and semi-empirical modeling systems to assess fire behavior and danger. This technical note describes the firebehavioR package, a porting of two fire behavior modeling systems, Crown Fire Initiation and Spread and a Rothermel-based framework, to the R programming language. We also highlight supporting data objects and functions to predict inputs required for fire behavior estimation. Last, this package contains functions for fifteen indices to express fire danger using weather and/or fuels observations. Specific advantages of predicting fire behavior using R, a free-and-open-source programming language, include freedom to adapt calculations to suit users’ needs, transparency of source code, and reduction of workflow inefficiencies, thereby aiding in sophisticated fire behavior analyses.

MODELING SYSTEMS DIRECT TORQUE CONTROL USING А THREE-PHASE MATHEMATICAL MODEL

2016 ◽  
Vol 22 (98) ◽  
pp. 56-61
Author(s):  
Vladislav A. Kosenko ◽  
◽  
Valeriy O. Kvashnin ◽  
Keyword(s):  
Mathematical Model ◽  
Direct Torque Control ◽  
Torque Control ◽  
Modeling Systems ◽  
Three Phase

Human Behavior Modeling Method Based on the Causality Between the Situation and the Behavior

2011 ◽  
Vol 131 (3) ◽  
pp. 635-643 ◽  
Author(s):  
Kohjiro Hashimoto ◽  
Kae Doki ◽  
Shinji Doki ◽  
Shigeru Okuma ◽  
Akihiro Torii
Keyword(s):  
Human Behavior ◽  
Modeling Method ◽  
Behavior Modeling ◽  
Human Behavior Modeling

Recommendations for deploying multidimensio

2017 ◽  
Vol 11 (1) ◽  
pp. 68-80
Author(s):  
E. E. Akimkina
Keyword(s):  
Comparative Analysis ◽  
Management System ◽  
Analytical Data ◽  
Enterprise Management ◽  
Multidimensional Data ◽  
Dimensional Approach ◽  
Process Data ◽  
Modeling Systems ◽  
Enterprise Management System ◽  
Practical Recommendations

A comparative analysis of different approaches to analytical data and shows that the most ample opportunities has a multi-dimensional approach, implemented with the help of OLAP technology. Presented multidimensional OLAP-cube model with the measurements for the analysis and processing of process data. Practical recommendations for the deployment of a multidimensional data modeling systems with regard to their integration into existing enterprise management system.

Exploring the Possibilities of Co-Simulation with CATIA V6 Dynamic Behavior Modeling

2013 ◽  
Vol 23 (3-4) ◽  
Author(s):  
Peter Smolek ◽  
Bernhard Heinzl ◽  
Horst Ecker ◽  
Felix Breitenecker
Keyword(s):  
Dynamic Behavior ◽  
Behavior Modeling
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