fire modeling
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2021 ◽  
Author(s):  
Steven Prescott ◽  
Robby Christian ◽  
Kurt Vedros ◽  
Svetlana Lawrence

Author(s):  
David Icove ◽  
Thomas May

Computer fire modeling can be a two-edged tool in forensic fire engineering investigations. Professional standards of care recommend that fire modeling’s primary use is in examining multiple hypotheses for a fire as opposed to determining its origin. This paper covers the current acceptable benefits of computer fire models, historical and pending legal case law, and methods to use modeling results within expert reports and testimony. Particular issues reviewed are the use of animations versus simulations, evidentiary guidelines, and authentication using verification and validation studies.


2021 ◽  
Author(s):  
KOJI SHIRAI ◽  
Koji Tasaka ◽  
Toshiko Udagawa
Keyword(s):  

2021 ◽  
pp. 1-27
Author(s):  
Tomas Van Pottelbergh ◽  
Guillaume Drion ◽  
Rodolphe Sepulchre

This article proposes a methodology to extract a low-dimensional integrate-and-fire model from an arbitrarily detailed single-compartment biophysical model. The method aims at relating the modulation of maximal conductance parameters in the biophysical model to the modulation of parameters in the proposed integrate-and-fire model. The approach is illustrated on two well-documented examples of cellular neuromodulation: the transition between type I and type II excitability and the transition between spiking and bursting.


Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 982
Author(s):  
HoonTaek Lee ◽  
Myoungsoo Won ◽  
Sukhee Yoon ◽  
Keunchang Jang

Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-h FMC models based on three different approaches, including regression (MREG), machine learning algorithms (MML) with random forest and support vector machine, and a process-based model (MFSMM), were compared. In addition, whole-year models of each type were compared with their respective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-h FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-h FMC models. In the assessments, MML showed the best performance (R2 = 0.77–0.82 and root mean squared error [RMSE] = 2.05–2.84%). The introduction of the correction coefficient into MREG improved its estimates (R2 improved from 0.56–0.58 to 0.68–0.70 and RMSE improved from 3.13–3.85% to 2.64–3.27%) by reducing the errors associated with high 10-h FMC values. MFSMM showed the worst performance (R2 = 0.41–0.43 and RMSE = 3.70–4.39%), which could possibly be attributed to the lack of radiation input from the study sites as well as the particular fuel moisture stick sensor that was used. Whole-year models and seasonal models showed almost equal performance because 10-h FMC varied in response to atmospheric moisture conditions rather than specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine-learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-h FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those involving fire danger ratings.


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