Bayesian Classification of a Meteorological Risk Index for Forest Fires: DSR

2008 ◽  
pp. 283-294 ◽  
Author(s):  
R.M. Durão ◽  
A. Soares ◽  
J.M.C. Pereira ◽  
J.A. Corte-Real ◽  
M.F.E.S. Coelho
Keyword(s):  
Forest Fires ◽  
Risk Index ◽  
Bayesian Classification

scholarly journals A weather regime characterisation of winter biomass aerosol transport from southern Africa

2021 ◽  
Author(s):  
Marco Gaetani ◽  
Benjamin Pohl ◽  
Maria del Carmen Alvarez Castro ◽  
Cyrille Flamant ◽  
Paola Formenti
Keyword(s):  
Spatial Distribution ◽  
Southern Africa ◽  
Forest Fires ◽  
South Atlantic ◽  
Austral Winter ◽  
The South ◽  
Predictive Tool ◽  
Weather Regime ◽  
Synoptic Variability

Abstract. During austral winter, a compact low cloud deck over South Atlantic contrasts with clear sky over southern Africa, where forest fires triggered by dry conditions emit large amount of biomass burning aerosols (BBA) in the free troposphere. Most of the BBA burden crosses South Atlantic embedded in the tropical easterly flow. However, midlatitude synoptic disturbances can deflect part of the aerosol from the main transport path towards southern extratropics. In this study, a characterisation of the synoptic variability controlling the spatial distribution of BBA in southern Africa and South Atlantic during austral winter (August to October) is presented. By analysing atmospheric circulation data from reanalysis products, a 6-class weather regime (WR) classification of the region is constructed. The classification reveals that the synoptic variability is composed by four WRs representing disturbances travelling at midlatitudes, and two WRs accounting for pressure anomalies in the South Atlantic. The WR classification is then successfully used to characterise the aerosol spatial distribution in the region in the period 2003–2017, in both reanalysis products and station data. Results show that the BBA transport towards southern extratropics is controlled by weather regimes associated with midlatitude synoptic disturbances. In particular, depending on the relative position of the pressure anomalies along the midlatitude westerly flow, the BBA transport is deflected from the main tropical route towards southern Africa or the South Atlantic. This paper presents the first objective classification of the winter synoptic circulation over South Atlantic and southern Africa. The classification shows skills in characterising the BBA transport, indicating the potential for using it as a diagnostic/predictive tool for aerosol dynamics, which is a key component for the full understanding and modelling of the complex radiation-aerosol-cloud interactions controlling the atmospheric radiative budget in the region.

scholarly journals Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values

2007 ◽  
Vol 18 (4) ◽  
pp. 605-612 ◽  
Author(s):  
Jan‐Philip M. Witte ◽  
Rafał B. Wójcik ◽  
Paul J.J.F. Torfs ◽  
Martin W.H. Haan ◽  
Stephan Hennekens
Keyword(s):  
Gaussian Mixture ◽  
Bayesian Classification ◽  
Vegetation Types ◽  
Indicator Values ◽  
Mixture Density ◽  
Density Fitting

scholarly journals Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

2020 ◽  
Vol 10 (22) ◽  
pp. 8213
Author(s):  
Yoojin Kang ◽  
Eunna Jang ◽  
Jungho Im ◽  
Chungeun Kwon ◽  
Sungyong Kim
Keyword(s):  
South Korea ◽  
Forest Fire ◽  
Forest Fires ◽  
Risk Index ◽  
Fire Risk ◽  
Ensemble Model ◽  
Weather Factors ◽  
Meteorological Model ◽  
Forest Fire Risk ◽  
Fire Risk Index

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea’s current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined.

scholarly journals Predicting atmospheric particle formation days by Bayesian classification of the time series features

Tellus B ◽  
2018 ◽  
Vol 70 (1) ◽  
pp. 1-10 ◽  
Author(s):  
M. A. Zaidan ◽  
V. Haapasilta ◽  
R. Relan ◽  
H. Junninen ◽  
P. P. Aalto ◽  
...  
Keyword(s):  
Time Series ◽  
Particle Formation ◽  
Bayesian Classification ◽  
Atmospheric Particle

Bayesian classification of a human custom based on stochastic context-free grammar

2007 ◽  
Vol 38 (9) ◽  
pp. 52-62 ◽  
Author(s):  
Humikazu Mitomi ◽  
Fuyuki Fujiwara ◽  
Masanobu Yamamoto ◽  
Taisuke Sato
Keyword(s):  
Bayesian Classification ◽  
Context Free Grammar ◽  
Context Free

scholarly journals Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 518 ◽  
Author(s):  
Natalia Quintero ◽  
Olga Viedma ◽  
Itziar R. Urbieta ◽  
José M. Moreno
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Fire Hazard ◽  
Sustainable Use ◽  
Google Earth ◽  
Burned Area ◽  
Change Metrics ◽  
Google Earth Engine ◽  
The Stability

Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire perimeters, using a dense Landsat Time Series (LTS) during the 1984–2017 period, and MODIS images. Methods: We implemented a semiautomatic process in the Google Earth Engine (GEE) platform to generate annual imagery free of clouds, cloud shadows, and gaps. We compared LandTrendr (LT) and FormaTrend (FT) algorithms that are widely used in LTS analysis to extract the pixel tendencies and, consequently, assess LULC changes and disturbances such as forest fires. These algorithms allowed us to generate the following change metrics: type, magnitude, direction, and duration of change, as well as the prechange spectral values. Results and conclusions: Our results showed that the FT algorithm was better than the LT algorithm at detecting low-severity changes caused by fires. Likewise, the use of the change metrics’ type, magnitude, and direction of change increased the accuracy of the LULC maps by 4% relative to the ones obtained using only spectral and topographic variables. The most significant hazardous LULC change processes observed were: deforestation and degradation (mainly by fires), encroachment (i.e., invasion by shrublands) due to agriculture abandonment and forest fires, and hazardous densification (from open forests and agroforestry areas). Although the total burned area has decreased significantly since 1985, the landscape fire hazard has increased since the second half of the twentieth century. Therefore, it is necessary to implement fire management plans focused on the sustainable use of shrublands and conifer forests; this is because the stability in these hazardous vegetation types is translated into increasing fuel loads, and thus an elevated landscape fire hazard.

Bayesian classification of protein structure

IEEE Expert ◽  
1992 ◽  
Vol 7 (4) ◽  
pp. 67-75 ◽  
Author(s):  
L. Hunter ◽  
D.J. States
Keyword(s):  
Protein Structure ◽  
Bayesian Classification
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