A new method for the analysis of fire spread modeling errors

2002 ◽  
Vol 11 (4) ◽  
pp. 193 ◽  
Author(s):  
Francis M. Fujioka
Keyword(s):  
Probability Model ◽  
Weather Conditions ◽  
Fire Spread ◽  
New Method ◽  
Ratio Error ◽  
Spread Model ◽  
San Bernardino ◽  
Modeling Errors ◽  
Operational Tool

Fire spread models have a long history, and their use will continue to grow as they evolve from a research tool to an operational tool. This paper describes a new method to analyse two-dimensional fire spread modeling errors, particularly to quantify the uncertainties of fire spread predictions. Measures of error are defined from the respective spread distances of the actual and simulated fires at specified points around their perimeters. A ratio error provides a correction factor for the spread model bias. The characteristics of the error are defined by a probability model, which is used to construct error bounds on fire spread predictions. The method is applied to the Bee Fire, which burned 3848 ha on the San Bernardino National Forest, California, in summer 1996. The study focused on the early, presuppression stages of the fire. A mesoscale spectral model was used to simulate weather conditions on a grid interval of 2 km. The FARSITE system was used to simulate fire growth during the first 105 min of the fire. The case study showed how difficult fire spread modeling is under the conditions presented by the Bee Fire.

scholarly journals IMPROVING THE INTEROPERABILITY OF DISASTER MODELS: A CASE STUDY OF PROPOSING FIREML FOR FOREST FIRE MODEL

2018 ◽  
Vol XLII-3 ◽  
pp. 673-677
Author(s):  
W. Jiang ◽  
F. Wang ◽  
Q. Meng ◽  
Z. Li ◽  
B. Liu ◽  
...  
Keyword(s):  
Disaster Management ◽  
Forest Fire ◽  
Fire Hazard ◽  
Fire Spread ◽  
Markup Language ◽  
Fire Model ◽  
Geography Markup Language ◽  
Spread Model ◽  
Forest Fire Model

This paper presents a new standardized data format named Fire Markup Language (FireML), extended by the Geography Markup Language (GML) of OGC, to elaborate upon the fire hazard model. The proposed FireML is able to standardize the input and output documents of a fire model for effectively communicating with different disaster management systems to ensure a good interoperability. To demonstrate the usage of FireML and testify its feasibility, an adopted forest fire spread model being compatible with FireML is described. And a 3DGIS disaster management system is developed to simulate the dynamic procedure of forest fire spread with the defined FireML documents. The proposed approach will enlighten ones who work on other disaster models' standardization work.

Using a statistical model of past wildfire spread to quantify and map the likelihood of fire reaching assets and prioritise fuel treatments

2020 ◽  
Vol 29 (5) ◽  
pp. 401 ◽  
Author(s):  
Owen F. Price ◽  
Michael Bedward
Keyword(s):  
Fire Spread ◽  
R Package ◽  
Wildland Urban Interface ◽  
Ignition Probability ◽  
Spread Model ◽  
Eastern Australia ◽  
Asset Location ◽  
Wildfire Spread ◽  
Fire Spreading

We present a method to quantify and map the probability of fires reaching the vicinity of assets in a wildfire-prone region, by extending a statistical fire spread model developed on historical fire patterns in the Sydney region, Australia. It calculates the mean probability of fire spreading along sample lines around assets, weights the probability according to ignition probability and also estimates the change in spread probability that fuel reduction in treatment blocks would achieve. We have developed an R package WildfireRisk to implement the analysis and demonstrate it with two case studies in forested eastern Australia. The probability of a fire reaching the vicinity of an asset was highest in the heavily forested parts of each case study, but when weighted for ignition probability, the high probability shifted to the wildland–urban interface. Further, when weighted by asset location, high-priority areas for treatment were in blocks next to the wildland–urban interface. This method is objective, fast and based on the behaviour of real historical fires. We recommend its use in wildfire risk planning, as an adjunct to heuristic methods and simulations. Additional functionality can be incorporated into our method, for instance via a function for building impact.

scholarly journals APPLICATION OF U-NET CONVOLUTIONAL NEURAL NETWORK TO BUSHFIRE MONITORING IN AUSTRALIA WITH SENTINEL-1/-2 DATA

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 573-578
Author(s):  
I. K. Lee ◽  
J. C. Trinder ◽  
A. Sowmya
Keyword(s):  
Model Development ◽  
South Australia ◽  
Weather Conditions ◽  
Accurate Method ◽  
Fire Spread ◽  
Data Cube ◽  
Pipeline Architecture ◽  
Spread Model ◽  
Adverse Weather ◽  
Burnt Areas

Abstract. This paper aims to define a pipeline architecture for near real-time identification of bushfire impact areas using Geoscience Australia Data Cube (AGDC). A series of catastrophic bushfires from late 2019 to early 2020 have captured international attention with their scale of devastation across four of the most populous states across Australia; New South Wales, Queensland, Victoria and South Australia. The extraction of burned areas using multispectral Sentinel-2 observations are straightforward when no cloud or haze obstruction are present. Without clear-sky observations, precisely locating the bushfire affected regions are difficult to achieve. Sentinel-1 C-band dual-polarized (VH/VV) Synthetic Aperture Radar (SAR) data is introduced to effectively elicit and analyse useful information based on backscattering coefficients, unaffected by adverse weather conditions and lack of sunlight. Burned vegetation results in significant volume scattering; co-/cross-polarised response decreases due to leafless trees, as well as coherence change over fire-disturbed areas; two sensors acquired images in a shortened revisit time over the same effected areas; all of which provided discriminative features for identifying burnt areas. Moreover, applying U-Net deep learning framework to train the recent and historical satellite data leads to an effective pre-trained segmentation model of burnt and non-burnt areas, enabling more timely emergency response, more efficient hazard reduction activities and evacuation planning during severe bushfire events. The advantages of this approach could have profound significance for a more robust, timely and accurate method of bushfire detection, utilising a scalable big data processing framework, to predict the bushfire footprint and fire spread model development.

STRATEGI KOMUNIKASI CROWDFUNDING PADA KOLASE.COM

2019 ◽  
Vol 2 (2) ◽  
pp. 177-187
Author(s):  
Venessa Agusta Gogali ◽  
Fajar Muharam ◽  
Syarif Fitri
Keyword(s):  
Social Media ◽  
Communication Strategies ◽  
Communication Strategy ◽  
New Method ◽  
Media Communication ◽  
Social Activists ◽  
Social Media Communication

Crowdfunding is a new method in fundraising activities based online. Moreover, the level of penetration of social media to the community is increasingly high. This makes social activists and academics realize that it is important to study social media communication strategies in crowdfunding activities. There is encouragement to provide an overview of crowdfunding activities. So the author conducted a research on "Crowdfunding Communication Strategy Through Kolase.com Through Case Study on the #BikinNyata Program Through the Kolase.com Website that successfully achieved the target. Keywords: Strategic of Communication, Crowdfunding, Social Media.

Sediment transport under dry weather conditions in a small sewer system

1998 ◽  
Vol 37 (1) ◽  
pp. 155-162
Author(s):  
Flemming Schlütter ◽  
Kjeld Schaarup-Jensen
Keyword(s):  
Sediment Transport ◽  
Field Data ◽  
Field Measurements ◽  
Weather Conditions ◽  
Sewer System ◽  
Transport Models ◽  
Sewer Systems ◽  
Combined Sewer ◽  
Initial Results

Increased knowledge of the processes which govern the transport of solids in sewers is necessary in order to develop more reliable and applicable sediment transport models for sewer systems. Proper validation of these are essential. For that purpose thorough field measurements are imperative. This paper renders initial results obtained in an ongoing case study of a Danish combined sewer system in Frejlev, a small town southwest of Aalborg, Denmark. Field data are presented concerning estimation of the sediment transport during dry weather. Finally, considerations on how to approach numerical modelling is made based on numerical simulations using MOUSE TRAP (DHI 1993).

scholarly journals Rain Erosion Maps for Wind Turbines Based on Geographical Locations: A Case Study in Ireland and Britain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
K. Pugh ◽  
M. M. Stack
Keyword(s):  
Wind Turbine ◽  
Western Europe ◽  
Meteorological Data ◽  
Turbine Blades ◽  
Weather Conditions ◽  
Annual Rainfall ◽  
Wind Turbine Blades ◽  
Weather Patterns ◽  
The Uk

AbstractErosion rates of wind turbine blades are not constant, and they depend on many external factors including meteorological differences relating to global weather patterns. In order to track the degradation of the turbine blades, it is important to analyse the distribution and change in weather conditions across the country. This case study addresses rainfall in Western Europe using the UK and Ireland data to create a relationship between the erosion rate of wind turbine blades and rainfall for both countries. In order to match the appropriate erosion data to the meteorological data, 2 months of the annual rainfall were chosen, and the differences were analysed. The month of highest rain, January and month of least rain, May were selected for the study. The two variables were then combined with other data including hailstorm events and locations of wind turbine farms to create a general overview of erosion with relation to wind turbine blades.

A new method to improve the NMR log interpretation in drilling mud-invaded zones: A case study from the Brazilian Pre-salt

2021 ◽  
pp. 108692
Author(s):  
Bruno Valle ◽  
Patrick Führ Dal’ Bó ◽  
Jeferson Santos ◽  
Lucas Aguiar ◽  
Pedro Coelho ◽  
...  
Keyword(s):  
New Method ◽  
Drilling Mud ◽  
Log Interpretation
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