scholarly journals A Hybrid Soft Computing Approach Producing Robust Forest Fire Risk Indices

2016 ◽  
pp. 191-203 ◽  
Author(s):  
Vardis-Dimitris Anezakis ◽  
Konstantinos Demertzis ◽  
Lazaros Iliadis ◽  
Stefanos Spartalis
Keyword(s):  
Soft Computing ◽  
Forest Fire ◽  
Fire Risk ◽  
Risk Indices ◽  
Forest Fire Risk ◽  
Computing Approach

Soft Computing Modeling of Wild Fire Risk Indices

2013 ◽  
pp. 1073-1087
Author(s):  
L. Iliadis ◽  
T. Betsidou
Keyword(s):  
Soft Computing ◽  
Forest Fire ◽  
Forest Fires ◽  
Fuzzy Inference ◽  
Research Effort ◽  
Fire Risk ◽  
Geographic Area ◽  
Burned Area ◽  
Wild Fire ◽  
Risk Indices

It is essential to find ways that can reduce the risk of devastating forest fires which have multiple negative ecological and financial consequences. This preliminary research effort focuses on the implementation of an intelligent rule based fuzzy inference system evaluating wild fire risk in the forest departments of Greece. The system uses soft computing techniques and was built in the Matlab integrated environment. The whole research is related to the wild fires in Greece during the period 1983-1997 with data coming from the general forest management service. It classifies all Greek forest departments (by assigning three labels) according to their forest fire risk due to distinct parameters. The estimation of the risk indices was done by using fuzzy triangular membership functions and Einstein fuzzy conjunction T-Norms. Moreover the system produces the profile of the forest departments located in the geographic area of “Peloponnesus.” This is a region located in the southern part of the country and it has a vast number of annual forest fire breakouts. Meteorological, topographic, and historical (total burned area and intervention time) features were considered for the determination of the risk indices. The system has shown a good performance which can be improved further if more data is gathered and used. Its main advantage is that it offers an innovative and reliable model that can be employed in any part of the world as a basis for natural disasters’ risk estimation.

Soft Computing Modeling of Wild Fire Risk Indices

2012 ◽  
pp. 220-234 ◽  
Author(s):  
L. Iliadis ◽  
T. Betsidou
Keyword(s):  
Soft Computing ◽  
Forest Fire ◽  
Forest Fires ◽  
Fuzzy Inference ◽  
Research Effort ◽  
Fire Risk ◽  
Geographic Area ◽  
Burned Area ◽  
Wild Fire ◽  
Risk Indices

It is essential to find ways that can reduce the risk of devastating forest fires which have multiple negative ecological and financial consequences. This preliminary research effort focuses on the implementation of an intelligent rule based fuzzy inference system evaluating wild fire risk in the forest departments of Greece. The system uses soft computing techniques and was built in the Matlab integrated environment. The whole research is related to the wild fires in Greece during the period 1983-1997 with data coming from the general forest management service. It classifies all Greek forest departments (by assigning three labels) according to their forest fire risk due to distinct parameters. The estimation of the risk indices was done by using fuzzy triangular membership functions and Einstein fuzzy conjunction T-Norms. Moreover the system produces the profile of the forest departments located in the geographic area of “Peloponnesus.” This is a region located in the southern part of the country and it has a vast number of annual forest fire breakouts. Meteorological, topographic, and historical (total burned area and intervention time) features were considered for the determination of the risk indices. The system has shown a good performance which can be improved further if more data is gathered and used. Its main advantage is that it offers an innovative and reliable model that can be employed in any part of the world as a basis for natural disasters’ risk estimation.

Forest fire risk indices and zoning of hazardous areas in Sorocaba, São Paulo state, Brazil

2019 ◽  
Vol 31 (2) ◽  
pp. 581-590 ◽  
Author(s):  
Leonardo Guimarães Ziccardi ◽  
Cláudio Roberto Thiersch ◽  
Aurora Miho Yanai ◽  
Philip Martin Fearnside ◽  
Pedro José Ferreira-Filho
Keyword(s):  
Forest Fire ◽  
Fire Risk ◽  
Sao Paulo ◽  
São Paulo ◽  
Paulo State ◽  
Risk Indices ◽  
São Paulo State ◽  
Forest Fire Risk

scholarly journals MULTICRITERIA ANALYSIS FOR IDENTIFYING FOREST FIRE RISK ZONES IN THE BIOLOGICAL RESERVE OF THE SAMA CORDILLERA, BOLIVIA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 113-118
Author(s):  
S. Mariscal ◽  
M. Ríos ◽  
F. Soria
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Risk Model ◽  
Multicriteria Analysis ◽  
Fire Risk ◽  
Temperature And Precipitation ◽  
Spatial Risk ◽  
Forest Fire Risk ◽  
Very High ◽  
Biological Reserve

Abstract. Forest fires have negative effects on biodiversity, the atmosphere and human health. The paper presents a spatial risk model as a tool to assess them. Risk areas refer to sectors prone to the spread of fire, in addition to the influence of human activity through remote sensing and multi-criteria analysis. The analysis includes information on land cover, land use, topography (aspect, slope and elevation), climate (temperature and precipitation) and socio-economic factors (proximity to settlements and roads). Weights were assigned to each in order to generate the forest fire risk map. The investigation was carried for a Biological Reserve in Bolivia because of the continuous occurrence of forest fires. Five risk categories for forest fires were derived: very high, high, moderate, low and very low. In summary, results suggest that approximately 67% of the protected area presents a moderate to very high risk; in the latter, populated areas are not dense which reduces the actual risk to the type of events analyzed.

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