Estimation of the Meteorological Forest Fire Risk in a Mountainous Region by Using Remote Air Temperature and Relative Humidity Data

2018 ◽  
Vol 67 ◽  
pp. 1-8 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Athanasios Kamoutsis ◽  
Kostas Chronopoulos
Keyword(s):  
Relative Humidity ◽  
Forest Fire ◽  
Air Temperature ◽  
Risk Index ◽  
Fire Risk ◽  
Urban Region ◽  
Mountainous Region ◽  
Reliable Estimation ◽  
Forest Fire Risk ◽  
Relative Humidity Data

The occurrence of forest fires is frequent phenomenon in Greece, especially during the warmest period of the year, the summer. Timely and reliable estimation of the meteorological risk for their onset is of crucial importance for their prevention. Thus, the purpose of our current work was firstly the estimation of the values of a suitable relevant index for Greece, meteorological forest fire risk index (MKs,t), derived from actual air temperature (T) and relative humidity data (RH) as well as from regressed T and RH, in a mountainous region (MR) for the most dangerous period of the year (July-August) and day (11:00 h-16:00 h), for five successive years (2006-2010) and secondly the comparison of the two ways ofMKs,tvalues estimation (from actual and regressed T and RH), based onMKs,tclasses. Regressed T and RH data were estimated with the aid of simple linear regression models from remote T and RH data, respectively, of an urban region, 175 Km away from MR, taking into account firstly the warmest (2007) and the coldest (2006) year of the examined year period. It was confirmed thatMKs,tvalues (based on regressed T and RH data) coincided in their classification to the respective ones resulted from actual T and RH data, that is, there was absolute success (100%). Using common regression lines and applying them to estimate separately T and RH at MR, for the most dangerous period of year and day concerning the whole examined year period, it was found that almost all the estimatedMKs,tvalues coincided, regarding their classification, with those estimated from actual T and RH data (97% success), which was considered very satisfactory. Therefore, our research methodology contributes a new perspective to a reliable estimation ofMKs,tfrom remote T and RH data using simple statistical models.

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 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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