A Note on Fuelbeds and Fire Behavior in Alang-Alang (Imperata Cylindrica)

1992 ◽  
Vol 2 (1) ◽  
pp. 41 ◽  
Author(s):  
S Pickford ◽  
M Suharti ◽  
A Wibowo
Keyword(s):  
Relative Humidity ◽  
Fire Behavior ◽  
Acacia Mangium ◽  
Imperata Cylindrica ◽  
High Relative Humidity ◽  
Prediction System ◽  
Behavior Prediction ◽  
Spread Rate ◽  
Physical Evidence ◽  
Made In

Fire behavior on a 2 ha fire, inferred from physical evidence observed one week after the fire, was compared with fire behavior estimates obtained using the BEHAVE fire behavior prediction system and fuel measurements in Imperata cylindrica (Alang-alang) made in the same area. This fire probably burned under light winds (3-5 km), high relative humidity, and spread slowly with moderate flame lengths (approximately 100 m hr-1 spread rate, 0.5 - 0.7 m flame lengths). Although appar- ently killed by lethal crown and bole scorch, the young Acacia mangium overstory through which the fire burned resprouted vigorously and apparently survived.

An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction

2018 ◽  
Vol 33 (1) ◽  
pp. 301-315 ◽  
Author(s):  
Wesley G. Page ◽  
Natalie S. Wagenbrenner ◽  
Bret W. Butler ◽  
Jason M. Forthofer ◽  
Chris Gibson
Keyword(s):  
United States ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Wildland Fire ◽  
Fire Behavior ◽  
The United States ◽  
Mean Bias Error ◽  
Bias Error ◽  
Behavior Prediction

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.

Modelling logic and the Canadian Forest Fire Behavior Prediction System

1998 ◽  
Vol 74 (1) ◽  
pp. 50-52 ◽  
Author(s):  
C. E. Van Wagner
Keyword(s):  
Forest Fire ◽  
Fire Behavior ◽  
Fire Spread ◽  
Fire Research ◽  
Fire Intensity ◽  
Prediction System ◽  
Behavior Prediction ◽  
Crown Fire ◽  
Fire Modelling ◽  
Semi Empirical

This article outlines the flexible semi-empirical philosophy used throughout six decades of fire research by the Canadian Forest Service, culminating in the development of the Forest Fire Behavior Prediction System. It then describes the principles involved when spread rate and fuel consumption are estimated separately to yield fire intensity, and the anomaly that has resulted from the omission of a foliar-moisture effect on crown-fire spread. Judged on its results so far, this Canadian approach has held its own against any other, and holds full promise for the future as well. Key words: forest fire behavior, Canadian FBP System, fire modelling, crown-fire theory, fire research philosophy

Testing the Effect of Fuel Consumption on Fire Spread Rate

1995 ◽  
Vol 5 (3) ◽  
pp. 143 ◽  
Author(s):  
RS McAlpine
Keyword(s):  
Fuel Consumption ◽  
Fire Behavior ◽  
Fire Spread ◽  
Data Sets ◽  
Behavior Prediction ◽  
Spread Rate ◽  
Data Set ◽  
Weak Relationship ◽  
Rate Of Spread ◽  
Fire Front

It has been theorized that the amount of fuel involved in a fire front can influence the rate of spread of the fire. Three data sets are examined in an attempt to prove this relationship. The first, a Canadian Forest Service database of over 400 experimental, wild, and prescribed fires showed a weak relationship in some fuel complexes. The second, a series of field experimental fires conducted to isolate the relationship, showed a small effect. The final data set, from a series of 47 small plots (3m x 3m) burned with a variety of fuel loadings, also show a weak relationship. While a relationship was shown to exist, it is debatable whether it should be included in a fire behavior prediction system. Inherent errors associated with predicting fuel consumption can be compounded, causing additional, more critical, errors with the derived fire spread rate.

scholarly journals Fuel Model Selection for BEHAVE in Midwestern Oak Savannas

2001 ◽  
Vol 18 (3) ◽  
pp. 74-80 ◽  
Author(s):  
Keith W. Grabner ◽  
John P. Dwyer ◽  
Bruce E. Cutter
Keyword(s):  
Prescribed Fire ◽  
Fire Behavior ◽  
Prediction System ◽  
Behavior Prediction ◽  
Management Scenarios ◽  
Tall Grass ◽  
Fuel Model ◽  
Oak Savannas ◽  
Fuel Models ◽  
Selection For

Abstract BEHAVE, a fire behavior prediction system, can be a useful tool for managing areas with prescribed fire. However, the proper choice of fuel models can be critical in developing management scenarios. BEHAVE predictions were evaluated using four standardized fuel models that partially described oak savanna fuel conditions: Fuel Model 1 (Short Grass), 2 (Timber and Grass), 3 (Tall Grass), and 9 (Hardwood Litter). Although all four models yielded regressions with R2 in excess of 0.8, Fuel Model 2 produced the most reliable fire behavior predictions. North. J. Appl. For. 18(3):74–80.

Persistence of nosocomial bacteria on 2 biocidal fabrics based on silver under conditions of high relative humidity

2014 ◽  
Vol 42 (8) ◽  
pp. 879-884 ◽  
Author(s):  
Rosa López-Gigosos ◽  
Alberto Mariscal ◽  
Mario Gutierrez-Bedmar ◽  
Eloisa Mariscal-Lopez ◽  
Joaquín Fernández-Crehuet
Keyword(s):  
Relative Humidity ◽  
High Relative Humidity

scholarly journals A Lightweight Motional Object Behavior Prediction System Harnessing Deep Learning Technology for Embedded ADAS Applications

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 692
Author(s):  
Wen-Chia Tsai ◽  
Jhih-Sheng Lai ◽  
Kuan-Chou Chen ◽  
Vinay M.Shivanna ◽  
Jiun-In Guo
Keyword(s):  
Embedded System ◽  
Moving Objects ◽  
Object Localization ◽  
Crossing Over ◽  
Learning Technology ◽  
Prediction System ◽  
Behavior Prediction ◽  
Detection Model ◽  
Pedestrian Crossing ◽  
Object Behavior

This paper proposes a lightweight moving object prediction system to detect and recognize pedestrian crossings, vehicles cutting-in, and vehicles ahead applying emergency brakes based on a 3D Convolution network for behavior prediction. The proposed design significantly improves the performance of the conventional 3D convolution network (C3D) adapted to predict the behaviors employing behavior recognition network capable of performing object localization, which is pivotal in detecting the numerous moving objects’ behaviors, combining and verifying the detected objects with the results of the YOLO v3 detection model with that of the proposed C3D model. Since the proposed system is a lightweight CNN model requiring far lesser parameters, it can be efficiently realized on an embedded system for real-time applications. The proposed lightweight C3D model achieves 10 frames per second (FPS) on a NVIDIA Jetson AGX Xavier and yields over 92.8% accuracy in recognizing pedestrian crossing, over 94.3% accuracy in detecting vehicle cutting-in behavior, and over 95% accuracy for vehicles applying emergency brakes.

scholarly journals Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

2009 ◽  
Vol 48 (9) ◽  
pp. 1790-1802 ◽  
Author(s):  
David P. Duda ◽  
Patrick Minnis
Keyword(s):  
Relative Humidity ◽  
Wind Direction ◽  
Meteorological Data ◽  
Logistic Models ◽  
Satellite Observations ◽  
Lapse Rate ◽  
Prediction System ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Surface Models

Abstract A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

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