Wildland fire risk or danger evalua

Wildland fire risk or danger evaluation is an integration of weather, topography, vegetative fuel and socioeconomic input variables to produce indices of fire potential outputs (Andrews et al., 2008 and Pyne et al., 1996). Various quantitative methods have been explored for the correlation of the input variables to fire danger assessment; most of these methods include the importance of the input variable as a direct or indirect output based on subjective weighting methods (Alcázar et al., 1998 and Salas and Chuvieco, 1994). Modern methods are based on either a statistical approach (Chou, 1992, Kalabokidis et al., 2007 and Vasconcelos et al., 2001) or a stochastic approach, i.e. artificial neural networks (ANN) (Chuvieco et al., 1999, Vasconcelos et al., 2001, Vasilakos et al., 2007 and Vasilakos et al., 2009).

Virtual Fire provides computational and geographical representation of the fire ignition probability and identification of the high-risk areas that is valid for the next day at noon time. It integrates models used for predicting forest fire ignition based on forecasted meteorological data and geographical data (Table 1). In our previously published research (Vasilakos et al., 2007 and Vasilakos et al., 2009), three different neural networks were developed and trained to calculate three intermediate outcomes of the Fire Ignition Index (FII); i.e. the Fire Weather Index (FWI), the Fire Hazard Index (FHI), and the Fire Risk Index (FRI). The FWI examines the wind, humidity, temperature and rainfall variables that are generally considered to determine fire danger potential (Schroeder and Buck, 1970), and how these parameters influence the fire ignition potential; FWI is different from the Canadian Fire Weather Index (Lee et al., 2002). The FHI includes the biophysical factors such as vegetation and topography. The FRI refers to the probability that a wildland fire will start as determined by the proximity of causative human activities, through the appropriate tools incorporated into ArcGIS.

ผลลัพธ์ (อังกฤษ) 2:

Wildland fire risk or danger evaluation is an integration of weather, topography, vegetative fuel and socioeconomic input variables to produce indices of fire potential outputs (Andrews et al., 2008 and Pyne et al., 1996). Various quantitative methods have been explored for the correlation of the input variables to fire danger assessment; most of these methods include the importance of the input variable as a direct or indirect output based on subjective weighting methods (Alcázar et al., 1998 and Salas and Chuvieco, 1994). Modern methods are based on either a statistical approach (Chou, 1992, Kalabokidis et al., 2007 and Vasconcelos et al., 2001) or a stochastic approach, ie artificial neural networks (ANN) (Chuvieco et al., 1999, Vasconcelos et. AL., 2001, Vasilakos et AL., 2,007th and Vasilakos et AL., 2009). Virtual Fire provides Computational and geographical Representation of The Fire ignition Probability and Identification of The High-risk areas that is valid for The next Day at Noon time. . It integrates models used for predicting forest fire ignition based on forecasted meteorological data and geographical data (Table 1). In our previously published research (Vasilakos et al., 2007 and Vasilakos et al., 2009), three different neural networks were developed and trained to calculate three intermediate outcomes of the Fire Ignition Index (FII); ie the Fire Weather Index (FWI), the Fire Hazard Index (FHI), and the Fire Risk Index (FRI). The FWI examines the wind, humidity, temperature and rainfall variables that are generally considered to determine fire danger potential (Schroeder and Buck, 1970), and how these parameters influence the fire ignition potential; FWI is different from the Canadian Fire Weather Index (Lee et al., 2002). The FHI includes the biophysical factors such as vegetation and topography. The FRI refers to the probability that a wildland fire will start as determined by the proximity of causative human activities, through the appropriate tools incorporated into ArcGIS.

ผลลัพธ์ (อังกฤษ) 3:

Wildland fire risk or danger evaluation is an integration, of weather topography vegetative fuel, and socioeconomic input. Variables to produce indices of fire potential outputs (Andrews et al, 2008 and Pyne et al, 1996). Various quantitative. Methods have been explored for the correlation of the input variables to fire danger assessment;Most of these methods include the importance of the input variable as a direct or indirect output based on subjective weighting. Methods (Alc and Zar et al, 1998 and Salas, and Chuvieco 1994). Modern methods are based on either a statistical approach (Chou 1992 Kalabokidis,,, Et al, 2007 and Vasconcelos et al, 2001) or a, stochastic approach i.e. Artificial neural networks (ANN) (Chuvieco et. Al.1999 Vasconcelos et, Al, 2001 Vasilakos et, Al, 2007 and Vasilakos et al, 2009).

Virtual Fire provides computational. And geographical representation of the fire ignition probability and identification of the high-risk areas that is valid. For the next day at noon time.It integrates models used for predicting forest fire ignition based on forecasted meteorological data and geographical. Data (Table 1). In our previously published research (Vasilakos et al, 2007 and Vasilakos et al, 2009), three different. Neural networks were developed and trained to calculate three intermediate outcomes of the Fire Ignition Index (FII); i.e.? The Fire Weather Index (FWI),The Fire Hazard Index (FHI), and the Fire Risk Index (FRI). The FWI examines the wind humidity temperature and rainfall,,, Variables that are generally considered to determine fire danger potential (Schroeder, and Buck 1970), and how these parameters. Influence the fire ignition potential; FWI is different from the Canadian Fire Weather Index (Lee et al, 2002).The FHI includes the biophysical factors such as vegetation and topography. The FRI refers to the probability that a wildland. Fire will start as determined by the proximity of causative, human activities through the appropriate tools incorporated. Into ArcGIS.

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