Fire-danger rating and observed wildfire behavior in the Northeastern United States.

Haines, Donald A.; Main, William A.; Simard, Albert

doi:10.2737/nc-rp-274

Cited by 8 publications

(6 citation statements)

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“…Indeed, Andrews and Bradshaw (1997), whose work was instrumental in the current implementation of the BI, suggested that the value of a fire danger index be evaluated according to its relationship with fire activity, which may be defined as the incidence of large wildfires. Such empirical relationships have been used as support for the use of such rating systems for predictive purposes [Haines et al (1983); Haines, Main and Simard (1986); Mees and Chase (1991); Ye (1997a, 1997b); Viegas et al (1999); Andrews, Loftsgaarden and Bradshaw (2003)]. The results here suggest that, for the purpose of forecasting wildfire hazard, point process models using RAWS records and previous wildfire activity as covariates may represent a promising alternative to existing indices that use essentially the same information.…”

Section: Resultsmentioning

confidence: 82%

Point process modeling of wildfire hazard in Los Angeles County, California

Xu¹,

Schoenberg²

2011

Ann. Appl. Stat.

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The Burning Index (BI) produced daily by the United States government's National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.

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Section: Resultsmentioning

confidence: 82%

Point process modeling of wildfire hazard in Los Angeles County, California

Xu¹,

Schoenberg²

2011

Ann. Appl. Stat.

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“…Essentially, the FFWI is a simple index based upon equilibrium moisture content and wind speed (Fosberg, 1978). Haines et al (1983) showed that the FFWI was highly correlated with fire occurrence in the north-east United States. Anecdotal evidence also suggests that the FFWI is a good indicator of fire activity in the south-west United States; for example, major fires of the late 1980s and 1990s have been associated with anomalously high occurrences of the FFWI (Roads et al, 1997).…”

Section: Fosberg Fire Weather Indexmentioning

confidence: 99%

A simple index for assessing fire danger rating

Sharples

McRae

Weber

et al. 2009

Environmental Modelling & Software

118

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“…The most representative indices frequently used are the FWI [22][23][24] and the Normalized Difference Vegetation Index (NDVI), which can provide direct and indirect information about vegetation attributes as well as fuel characteristics. Another frequently used Fire Index in the Mediterranean [25][26][27] and other ecosystems constitute the Fosberg Fire Weather Index (FWI). All the mentioned indices-despite their effectiveness in fire danger classification-are not directly correlated with fire occurrence itself due to the randomness associated with the phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Remotely-Sensed Fire-Related Indices for Wildfire Prediction through the Contribution of Artificial Intelligence

et al. 2023

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Wildfires are a natural phenomenon, which nowadays, due to the synergistic effect of increased human intervention and the escalation of climate change, are displaying an ever-increasing intensity and frequency. The underlying mechanisms present increased complexity, with the phenomenon itself being characterized by a significant degree of stochasticity. For the above reasons, machine learning models and neural networks are being implemented. In the current study, two types of neural networks are implemented, namely, Artificial Neural Networks (ANN) and Radial Basis Function Networks (RBF). These neural networks utilize information from the Fire Weather Index (FWI), Fosberg Fire Weather Index (FFWI), Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), aiming to predict ignitions in a region of Greece. All indices have been developed through the Google Earth Engine platform (GEE). In addition, a new index is proposed named “Vegetation-Enhanced FWI” (FWIveg) in order to enhance the FWI with vegetation information from the NDVI. To increase the robustness of the methodology, a genetic algorithm-based approach was used in order to obtain algorithms for the calculation of the new index. Finally, an artificial neural network was implemented in order to predict the Mati wildfire in Attica, Greece (23 July 2018) by applying the new index FWIveg, aiming to assess both the effectiveness of the new index as well as the ability to predict ignition events using neural networks. Results highlight the effectiveness of the two indices in providing joint information for fire prediction through artificial intelligence-based approaches.

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Fire-danger rating and observed wildfire behavior in the Northeastern United States.

Cited by 8 publications

References 0 publications

Point process modeling of wildfire hazard in Los Angeles County, California

Point process modeling of wildfire hazard in Los Angeles County, California

A simple index for assessing fire danger rating

Fusion of Remotely-Sensed Fire-Related Indices for Wildfire Prediction through the Contribution of Artificial Intelligence

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