WO2022085870A1 - Device and method for providing forest fire risk index - Google Patents

Abstract

A device and a method for providing a forest fire risk index, according to a preferred embodiment of the present invention, generate a forest fire risk index model optimized for the Korean Peninsula, and forecast a forest fire risk index for a forecast date by using the generated forest fire risk index model, and thus minimize damage through efficient on-site forest fire preparations (forward deployment of fire-extinguishing resources, and the like) and, further, can reduce the frequency of forest fires by providing necessary information for forest fire prevention.

Description

Device and method for providing wildfire risk index

The present invention relates to an apparatus and method for providing a forest fire risk index, and more particularly, an apparatus for generating a forest fire risk index model optimized for the Korean peninsula, and predicting a forest fire risk index for a forecast date using the generated forest fire risk index model and methods. This study is related to the establishment of an integrated forest fire risk forecasting system (No. 1405004074) using meteorological big data conducted under the supervision of the National Institute of Forest Science with the funds of the Korea Forest Service for 2018-2020.

As the number of dry days in the Korean Peninsula increases due to climate change, the risk of forest fires is increasing, such as a longer period of wildfires throughout the year.

An object of the present invention is to provide an apparatus and method for providing a forest fire risk index for generating a forest fire risk index model optimized for the Korean Peninsula, and predicting a forest fire risk index for a forecast date using the generated forest fire risk index model. there is.

Other objects not specified in the present invention may be additionally considered within the scope that can be easily inferred from the following detailed description and effects thereof.

The device for providing a forest fire risk index according to a preferred embodiment of the present invention for achieving the above object obtains a forest fire risk index using a forest fire frequent area map, a modified Fine Fuel Moisture Code (FFMC), a drought index, and a monthly weight. Forest Fire Risk Index Acquisition Department; And, based on the forest fire risk index obtained through the forest fire risk index acquisition unit, a forecasting unit for obtaining a forest fire risk index value for a forecast date; includes, wherein the monthly weight is obtained based on all wildfires that have occurred in the past Using the ratio of the number of forest fires per month, the greater the number of forest fires per month, the greater the weight.

Here, the forest fire risk index acquisition unit may obtain the forest fire risk index through the formula (the wildfire frequent area map + 0.5) * (the modified FFMC) * (1.5 - the drought index) * (the monthly weight). there is.

Here, the modified FFMC is, in the conventional FFMC that calculates the FFMC based on the equilibrium moisture content, drying rate and minimum moisture content of the day calculated using relative humidity, precipitation measured at noon, temperature, and wind speed, FFMC initial value , the coefficient of the equation for calculating the precipitation reference value, the FFMC reference value, and the drying rate is corrected, the coefficient of the equation for calculating the moisture content of the previous day is corrected, the coefficient of the equation for calculating the FFMC value using the minimum moisture content is corrected, and at noon The accumulated precipitation per day is used instead of the measured precipitation, and the precipitation reference value may be larger than that of the conventional FFMC.

Here, a drought index obtaining unit for obtaining the drought index by using the soil moisture index downscaled to a grid size of 1 km, a Normalized Different Water Index (NDWI), and a Temperature Condition Index (TCI); may further include.

Here, the drought index obtaining unit may obtain the drought index through the formula 0.4 * (the downscaled soil moisture index) + 0.3 * (the NDWI) + 0.3 * (the TCI).

Here, using data upscaled to a grid size of 25 km as training data, based on a machine learning algorithm, TRMM (Tropical Rainfall Measuring Mission) precipitation data, ASCAT (Advanced SCATterometter) soil moisture data, NDVI (Normalized Difference Vegetation Index) ), Land Surface Temperature (LST), and Digital Elevation Model (DEM) as input variables, and Global Land Data Assimilation System (GLDAS) soil moisture data as output variables, a soil moisture index downscaling model is created, It may further include; a soil moisture index acquisition unit for obtaining the downscaled soil moisture index by inputting the input variable converted to the grid size into the soil moisture index downscaling model.

Here, the soil moisture index acquisition unit, using the data upscaled to a grid size of 25 km as training data, the TRMM precipitation data, the ASCAT soil moisture data, the NDVI, the LST, and the DEM as input variables, Using the GLDAS soil moisture data as an output variable, a first soil moisture index downscaling model is created, and the TRMM precipitation data, the NDVI, the LST and A second soil moisture index downscaling model is generated using the DEM as an input variable and the GLDAS soil moisture data as an output variable, and the input variable converted to a grid size of 1 km is used as the first soil moisture index downscaling model to obtain a first soil moisture index downscaled to a grid size of 1 km by inputting into A second soil moisture index may be obtained, and the part missing from the first soil moisture index may be replaced with the second soil moisture index to obtain the downscaled soil moisture index.

A method for providing a forest fire risk index according to a preferred embodiment of the present invention for achieving the above object is a method for providing a forest fire risk index performed by a device for providing a forest fire risk index, a forest fire frequent area map, a modified FFMC (Fine Fuel Moisture) Code), obtaining a forest fire risk index using the drought index and monthly weight; and, based on the wildfire risk index, obtaining a forest fire risk index value for a forecast date, wherein the monthly weight is based on all forest fires that have occurred in the past. The higher the number of forest fires per month, the greater the weight.

Here, the forest fire risk index acquisition step is to obtain the forest fire risk index through the formula (the wildfire frequent area map + 0.5) * (the modified FFMC) * (1.5 - the drought index) * (the monthly weight) can be made of

Here, the modified FFMC is, in the conventional FFMC that calculates the FFMC based on the equilibrium moisture content, drying rate and minimum moisture content of the day calculated using relative humidity, precipitation measured at noon, temperature, and wind speed, FFMC initial value , the coefficient of the equation for calculating the precipitation reference value, the FFMC reference value, and the drying rate is corrected, the coefficient of the equation for calculating the moisture content of the previous day is corrected, the coefficient of the equation for calculating the FFMC value using the minimum moisture content is corrected, and at noon The accumulated precipitation per day is used instead of the measured precipitation, and the precipitation reference value may be larger than that of the conventional FFMC.

Here, the method may further include; obtaining the drought index using a soil moisture index downscaled to a grid size of 1 km, a Normalized Different Water Index (NDWI), and a Temperature Condition Index (TCI).

Here, using data upscaled to a grid size of 25 km as training data, based on a machine learning algorithm, TRMM (Tropical Rainfall Measuring Mission) precipitation data, ASCAT (Advanced SCATterometter) soil moisture data, NDVI (Normalized Difference Vegetation Index) ), Land Surface Temperature (LST), and Digital Elevation Model (DEM) as input variables, and Global Land Data Assimilation System (GLDAS) soil moisture data as output variables, a soil moisture index downscaling model is created, The method may further include inputting the input variable converted into a grid size into the soil moisture index downscaling model to obtain the downscaled soil moisture index.

The computer program according to a preferred embodiment of the present invention for achieving the above technical problem is stored in a computer-readable recording medium and executes any one of the methods for providing the forest fire risk index described above on the computer.

According to the apparatus and method for providing a forest fire risk index according to a preferred embodiment of the present invention, by generating a forest fire risk index model optimized for the Korean Peninsula, and predicting the forest fire risk index for the forecast date using the generated forest fire risk index model, Damage can be minimized through efficient on-site forest fire preparation (forward deployment of firefighting resources, etc.)

Effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram illustrating an apparatus for providing a forest fire risk index according to a preferred embodiment of the present invention.

2 is a view for explaining a soil moisture index downscaling model according to a preferred embodiment of the present invention.

3 is a view for explaining the results of the soil moisture index downscaling model according to a preferred embodiment of the present invention.

4 is a view for explaining the results of the soil moisture index downscaling model excluding the ASCAT soil moisture data according to a preferred embodiment of the present invention.

5 is a view for explaining the final downscaled soil moisture index according to a preferred embodiment of the present invention. The left side of FIG. 5 shows the results of the first soil moisture index downscaling model, and the right side of FIG. 5 shows the first It shows that the missing part in the result of the soil moisture index downscaling model is replaced with the result of the second soil moisture index downscaling model.

6 is a view for explaining a drought factor according to a preferred embodiment of the present invention.

7 is a diagram for explaining a result of a drought index model to which weights are applied according to a preferred embodiment of the present invention.

8 is a view for explaining a map of a forest fire frequent area according to a preferred embodiment of the present invention.

9 is a view for explaining the reason for using the FFMC as a factor of the forest fire risk index according to a preferred embodiment of the present invention. (b) compares FFMC with the number of wildfires by 10 days in 2015.

10 is a view for explaining a result of comparing the spatial distribution of a modified FFMC according to a preferred embodiment of the present invention and a conventional FFMC.

11 is a view for explaining a result of comparing the number of forest fires per month between the modified FFMC and the conventional FFMC according to a preferred embodiment of the present invention.

12 is a view for explaining a drought index used to obtain a forest fire risk index according to a preferred embodiment of the present invention.

13 is a view for explaining a first monthly weight used to obtain a forest fire risk index according to a preferred embodiment of the present invention.

14 is a diagram for explaining a second monthly weight used to obtain a forest fire risk index according to a preferred embodiment of the present invention.

15 is a flowchart for explaining a method for providing a forest fire risk index according to a preferred embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. Advantages and features of the present invention, and a method for achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments published below, but may be implemented in various different forms, and only these embodiments make the publication of the present invention complete, and common knowledge in the art to which the present invention pertains It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used with the meaning commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly defined in particular.

In the present specification, terms such as “first” and “second” are for distinguishing one component from other components, and the scope of rights should not be limited by these terms. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

In the present specification, identification symbols (eg, a, b, c, etc.) in each step are used for convenience of description, and identification symbols do not describe the order of each step, and each step is clearly Unless a specific order is specified, the order may differ from the specified order. That is, each step may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

In this specification, expressions such as “have”, “may have”, “include” or “may include” indicate the existence of a corresponding feature (eg, a numerical value, function, operation, or component such as a part). and does not exclude the presence of additional features.

In addition, the term '~ unit' as used herein means software or a hardware component such as a field-programmable gate array (FPGA) or ASIC, and '~ unit' performs certain roles. However, '-part' is not limited to software or hardware. '~' may be configured to reside on an addressable storage medium or may be configured to refresh one or more processors. Accordingly, as an example, '~' indicates components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, and procedures. , subroutines, segments of program code, drivers, firmware, microcode, circuitry, data structures and variables. The functions provided in the components and '~ units' may be combined into a smaller number of components and '~ units' or further separated into additional components and '~ units'.

Hereinafter, a preferred embodiment of the apparatus and method for providing a forest fire risk index according to the present invention will be described in detail with reference to the accompanying drawings.

First, an apparatus for providing a forest fire risk index according to a preferred embodiment of the present invention will be described with reference to FIG. 1 .

1 is a block diagram illustrating an apparatus for providing a forest fire risk index according to a preferred embodiment of the present invention.

Referring to FIG. 1 , the apparatus 100 for providing a forest fire risk index according to a preferred embodiment of the present invention generates a forest fire risk index model optimized for the Korean Peninsula, and uses the generated forest fire risk index model to predict forest fire risk for a forecast date. predict the index.

To this end, the forest fire risk index providing apparatus 100 may include a soil moisture index obtaining unit 110 , a drought index obtaining unit 130 , a forest fire risk index obtaining unit 150 , and a forecasting unit 170 .

The soil moisture index acquisition unit 110 is based on a machine learning algorithm, TRMM (Tropical Rainfall Measuring Mission) precipitation data, ASCAT (Advanced SCATterometter) soil moisture data, NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature) and A soil moisture index downscaling model is generated using a digital elevation model (DEM) as an input variable and global land data assimilation system (GLDAS) soil moisture data as an output variable.

Here, the machine learning algorithm may use one of a random forest (RF), support vector regression (SVR), and an artificial neural network (ANN). In particular, the present invention can generate a soil moisture index downscaling model using a random forest (RF).

At this time, the soil moisture index acquisition unit 110 uses, as training data, the upscaled data to a grid size of 25 km, which is the same as the grid size of the GLDAS soil moisture data, as training data for the unity of spatial resolution between the data. create

Then, the soil moisture index acquisition unit 110 obtains the soil moisture index downscaled to the grid size of 1 km by inputting the input variable converted to the 1km grid size to the soil moisture index downscaling model.

In more detail, the soil moisture index acquisition unit 110 uses the data upscaled to a grid size of 25 km as training data, and uses TRMM precipitation data, ASCAT soil moisture data, NDVI, LST and DEM as input variables, Using the GLDAS soil moisture data as an output variable, a first soil moisture index downscaling model may be generated.

Then, the soil moisture index acquisition unit 110 uses the upscaled data to a grid size of 25 km as training data, TRMM precipitation data, NDVI, LST, and DEM as input variables, and GLDAS soil moisture data as output variables. , it is possible to generate a second soil moisture index downscaling model. That is, the soil moisture index acquisition unit 110 may generate the second soil moisture index downscaling model by using the remaining input variables except for the ASCAT soil moisture data among the input variables of the first soil moisture index downscaling model.

Then, the soil moisture index acquisition unit 110 inputs the input variable converted to a grid size of 1 km into the first soil moisture index downscaling model to obtain a first soil moisture index downscaled to a grid size of 1 km, The input variable converted to a grid size of 1 km is input to the second soil moisture index downscaling model to obtain a second soil moisture index downscaled to a grid size of 1 km, and the missing part in the first soil moisture index is the second By substituting the soil moisture index, the final soil moisture index downscaled to a grid size of 1 km can be obtained.

The drought index acquisition unit 130 uses the soil moisture index downscaled to a grid size of 1 km obtained through the soil moisture index acquisition unit 110, NDWI (Normalized Different Water Index), and TCI (Temperature Condition Index) during drought get an index

That is, the drought index obtaining unit 130 may obtain the drought index through the following equation.

Drought Index = 0.4 * (Downscaled Soil Moisture Index) + 0.3 * (NDWI) + 0.3 * (TCI)

In more detail, the drought index acquisition unit 130 may acquire the drought index by using the downscaled soil moisture index, NDWI, and TCI, which are factors showing a high correlation with drought among the following drought-related factors.

- Soil moisture index downscaled to a grid size of 1 km

- NDDI (Normalized Different Drought Index)

- NDWI (Normalized Different Water Index)

- NMDI (Normalized Multi-band Drought Index)

- TCI (Temperature Condition Index)

- VCI (Vegetation Condition Index)

- TRMM (Tropical Rainfall Measuring Mission) 1 (accumulated precipitation per week) / TRMM 2 (accumulated precipitation in 2 weeks)

Of course, the drought index acquisition unit 130 may obtain three drought indices through the following three equations, and obtain an average value of the obtained three drought indices as the final drought index.

- Equation 1: The drought exponential equation described above

- Equation 2: Drought index = 0.4 * (downscaled soil moisture index) + 0.2 * (NDWI) + 0.4 * (TRMM)

- Equation 3: Drought index = 0.3 * (downscaled soil moisture index) + 0.2 * (NDWI) + 0.3 * (TRMM) + 0.1 * (TCI)

On the other hand, the drought index obtaining unit 130 may generate a drought index model using the One-Class SVM, and may obtain the drought index using the generated drought index model, but the drought index obtaining unit 130 according to the present invention ) obtains the drought index through the above equation.

Of course, the drought index acquisition unit 130 obtains the first drought index through Equation 1 above using the downscaled soil moisture index, NDWI, and TCI, and uses the One-Class SVM to generate the drought index model. A second drought index may be obtained through the process, and an average value of the obtained first and second drought indexes may be obtained as a final drought index. In this case, the drought index acquisition unit 130 may use the average value of the three drought indices obtained through Equations 1 to 3 above as the first drought index.

The forest fire risk index acquisition unit 150 obtains a forest fire risk index using the drought index and monthly weights obtained through the forest fire frequent area map, the modified Fine Fuel Moisture Code (FFMC), and the drought index acquisition unit 130 .

Here, the monthly weight may be given a greater weight as the number of monthly forest fires increases, using the ratio of the number of monthly wildfires obtained based on all forest fires that have occurred in the past. For example, the monthly weight is the first monthly weight that gives a greater weight as the number of monthly wildfires increases according to the ratio of the number of monthly wildfires based on all forest fires that have occurred in the past It may be a second monthly weight that weights only a specific month (March-May) with the highest number of cases. In particular, the present invention can obtain a forest fire risk index by using the first monthly weight.

And, the modified FFMC is the conventional FFMC that calculates the FFMC based on the equilibrium moisture content, drying rate and minimum moisture content of the day calculated using the relative humidity, precipitation measured at noon, temperature, and wind speed. say FFMC. That is, in the modified FFMC, the coefficient of the formula for calculating the FFMC initial value, the precipitation reference value, the FFMC reference value and the drying rate is modified in the conventional FFMC, the coefficient of the formula calculating the moisture content of the previous day is modified, and the minimum moisture content is used It refers to an FFMC in which the coefficient of the equation for calculating the FFMC value is modified, the accumulated precipitation per day is used instead of the precipitation measured at noon, and the precipitation reference value is larger than that of the conventional FFMC.

That is, the forest fire risk index acquisition unit 150 may acquire the forest fire risk index through the following equation.

Wildfire Risk Index = (Wildfire Hotspot Map + 0.5) * (Adjusted FFMC) * (1.5 - Drought Index) * (Monthly Weighted)

At this time, the forest fire risk index acquisition unit 150 obtains the first forest fire risk index through the above formula using the “first monthly weight” as the monthly weight, and uses the “second monthly weight” as the monthly weight to obtain the above The second wildfire risk index is obtained through the ceremony, and the average value of the obtained first wildfire risk index and the second wildfire risk index may be obtained as the final forest fire risk index.

The forecasting unit 170 acquires a forest fire risk index value for the forecast date based on the forest fire risk index obtained through the forest fire risk index obtaining unit 150 .

Then, a soil moisture index downscaling model according to a preferred embodiment of the present invention will be described in more detail with reference to FIGS. 2 to 5 .

In the present invention, GLDAS (Global Land Data Assimilation System) soil moisture data, ASCAT (Advanced SCATterometter) soil moisture data, NDVI (Normalized Difference Vegetation Index), Field observation data of soil moisture after constructing a soil moisture index downscaling model using 5-day and 7-day cumulative precipitation data using LST (Land Surface Temperature), DEM (Digital Elevation Model), and daily TRMM (Tropical Rainfall Measuring Mission) precipitation data to verify the model.

First, GLDAS used as soil moisture data is a data assimilation data system based on three surface models of Mosaic, Noah, and Community Land Model. In the present invention, Noah model was used among the three surface models. The Noah model provided the moisture data of the 1 cm ~ 10 cm soil layer with a spatial resolution of 25 km 4 times a day (03, 09, 15, and 12:00), and the corresponding soil moisture data (m ³ /m ³ ) was averaged daily and used. For soil moisture data calculated by ASCAT, daily ascending and descending pass data were averaged and used. As auxiliary variables, MOD13A2, which is 16-day synthetic NDVI data with 1 km spatial resolution calculated from MODIS (Moderate Resolution Imaging Spectroradiometer) of Terra satellite, and MOD11A2 output, which is 8-day synthetic LST data with 1 km spatial resolution, was used. In addition, the DEM used the global DEM data of the Shuttle Radar Topography Mission (SRTM) with a spatial resolution of 90 m. In the case of precipitation data, TRMM 3B42 daily data with a spatial resolution of 25 km were accumulated over a period of 5 and 7 days, respectively. The soil moisture field observation data (%) provided by the Agricultural Promotion Agency was used to validate the model after unit conversion to m ³ /m ³ . All data except field observation data were used after masking in the range of the study area.

2 is a view for explaining a soil moisture index downscaling model according to a preferred embodiment of the present invention.

Referring to FIG. 2 , in the present invention, a downscaling model for soil moisture detailing was developed using various machine learning and artificial intelligence techniques. As the machine learning and artificial intelligence methods used, three techniques were used, respectively: random forest (RF), support vector regression analysis (SVR), and artificial neural network (ANN). The downscaling model was developed for each technique by setting the GLDAS soil moisture data as the dependent variable and setting a total of five data except the soil moisture field observation data as independent variables. For the unity of spatial resolution between data, all input data were applied to model building after upscaling to a spatial resolution of 25 km, the same grid size as GLDAS. After that, the 5 input data converted to the 1km grid size is applied to the developed model to finally obtain the downscaling soil moisture result of the 1km grid. In the present invention, the model was constructed by dividing the training data and the validation data by dividing the data from 2013 to 2014 at a ratio of 8:2, and additionally 10-fold cross-validation was performed with data for the same period. In addition, the soil moisture downscaling result produced through each model was verified by comparing it with the field observation soil moisture data of the Agricultural Promotion Administration.

3 is a view for explaining the results of the soil moisture index downscaling model according to a preferred embodiment of the present invention.

To find the optimal method for the soil moisture index downscaling model in the Korean Peninsula, three methods (RF, SVR, and ANN) were used to develop and compare the downscaling model. 3 is a scatter plot showing the soil moisture downscaling results of each model. In the order of RF, ANN, and SVR, the correlation coefficient value between the actual variable value and the model predicted value was large, and the RMSE (Root Mean Square Error) also showed a small value. In particular, the distribution of RF results was the narrowest in the scatterplot related to cross-validation. All three models tended to predict lower-than-actual values for high soil moisture values and high for low soil moisture values overall. Accordingly, the soil moisture index downscaling model according to the present invention was generated using RF.

Figure 4 is a view for explaining the results of the soil moisture index downscaling model excluding the ASCAT soil moisture data according to a preferred embodiment of the present invention, Figure 5 is the final downscaled soil moisture index according to a preferred embodiment of the present invention As a diagram for explaining , the left side of FIG. 5 shows the results of the first soil moisture index downscaling model, and the right side of FIG. 5 shows the missing part from the results of the first soil moisture index downscaling model with the second soil moisture index It represents substitution with the result of the downscaling model.

It can be seen that the results of the soil moisture index downscaling model are missing for the Jeju Island area and near some coastlines. This depends on the value of ASCAT soil moisture data with a spatial resolution of 25 km used as an input variable. In order to solve the omission problem, a soil moisture index downscaling model was additionally built with a total of four input variables excluding ASCAT from the input variables in the soil moisture downscaling method mentioned above, and the part missing due to ASCAT data in the original model was to be replaced with the result of the additional model. 4 and 5 show examples of the resultant performance and soil moisture map of the additional model, respectively.

To verify the results of each soil moisture index downscaling model, comparison verification was performed with soil moisture data based on field observations provided by the Agricultural Promotion Administration. Time series analysis was performed to verify the soil moisture data of the observatory used for verification with the results of the soil moisture index downscaling model. As a result of time series analysis, it was confirmed that the soil moisture increase/decrease pattern was similar at all observation stations.

Then, the drought index according to a preferred embodiment of the present invention will be described in more detail with reference to FIGS. 6 and 7 .

The main factors that cause wildfires in Korea include misfires of mountain climbers and cigarette butts, and the occurrence of wildfires due to drought is extremely rare. However, if a forest fire occurs during a drought, the damage (area) may be increased. Therefore, in the present invention, the correlation between drought and forest fires was analyzed using data on actual wildfire occurrence of 1 ha or more from 2013 to 2018. Later, satellite data available in real time were used to develop the forest fire risk index. The drought-related factors used include the above-mentioned soil moisture downscaling data, NDDI (Normalized Different Drought Index), NDWI (Normalized Different Water Index) 5, 6, 7 (Divided into 5, 6, 7 depending on the use of SWIR band) , NMDI (Normalized Multi-band Drought Index), TCI (Temperature Condition Index), VCI (Vegetation Condition Index), TRMM 1 and 2 (accumulated precipitation for one and two weeks) data. The formula below is expressed based on MODIS (based on MODIS band).

NDDI = (NDVI - NDWI) / (NDVI + NDWI)

NDWI = (band2 - SWIR) / (band2 + SWIR)

NMDI = (band2 - (band6 - band7)) / (band2 + (band6-band7))

6 is a view for explaining a drought factor according to a preferred embodiment of the present invention.

As a result of analyzing the value distribution of each factor and index based on a forest fire occurrence area of 1 ha or more (left side of FIG. 6) and 10 ha or more (right side of FIG. 6), the same results as those of FIG. 6 were obtained. A value closer to 1 means a non-drought state, and a value closer to 0 means more severe drought. Above 1 ha, the range of 1-week cumulative precipitation (TRMM 1) and 2-week cumulative precipitation (TRMM 2) values showed the most drought phenomenon, and soil moisture (SM) and NDWI showed a correlation distribution in that order. NDDI, NMDI, and VCI showed values close to non-drought, indicating that the correlation was poor with the occurrence of forest fires (left side of FIG. 6). It showed the same correlation as 1 ha even at more than 10 ha, and relatively few cases of non-drought (right side of FIG. 6). For forest fire area of 50 ha or more (13 cases), the average value for each factor is soil moisture 0.37, NDDI 0.49, NDWI 5 0.32, NDWI 6 0.28, NDWI 7 0.38, NMDI 0.41, TCI 0.45, VCI 0.56, TRMM 1 0.01, TRMM 2 showed a greater correlation with drought, such as 0.02.

In the present invention, a drought index model was developed by applying a weight to the drought factor selected through correlation analysis and by applying One-Class SVM, one of the types of machine learning. Indices were developed using soil moisture, NDWI, TCI, NMDI, and TRMM, which showed a high correlation.

(1) weighting

Through correlation analysis, it was found that the precipitation factor had the greatest influence. However, in the case of precipitation, the drought index shows an extreme value depending on whether or not precipitation occurs, and drought or non-drought in all regions spatially, so it was judged not to be necessarily used for the development of the drought index. Therefore, as shown in [Table 1] below, soil moisture and NDWI, which had the highest correlation after precipitation, were combined with other factors.

Scheme	Equation
One	0.7 * Soil Moisture Index + 0.3 * NDWI
2	0.4 * Soil Moisture Index + 0.3 * NDWI + 0.3 * TCI
3	0.4 * Soil Moisture Index + 0.3 * NDWI + 0.3 * NMDI
4	0.4 * Soil Moisture Index + 0.2 * NDWI + 0.4 * TRMM
5	0.3 * Soil Moisture Index + 0.2 * NDWI + 0.3 * TRMM + 0.1 * TCI

7 is a diagram for explaining a result of a drought index model to which weights are applied according to a preferred embodiment of the present invention. 7 shows the results of each scheme for actual wildfire occurrence, Schemes 4 and 5 including precipitation factors showed a severe drought state compared to other schemes, and showed a drought state irrespective of the area damaged by the forest fire. However, even in areas where wildfires did not occur, drought conditions were indicated. In addition, since TCI has a relatively large correlation compared to NMDI, Scheme 2 without precipitation factors and Scheme 4 and 5 with precipitation factors were used for forest fire risk index development.

(2) One-Class SVM application

One-Class SVM uses a hyperplane to classify one class and outliers in the same way as the existing binary classification and multi-classification SVMs. Based on the margin support vectors, internal vectors are assigned a class, and external vectors are identified as outliers.

When developing a machine learning model using the existing binary classification, the developer's subjective opinion on the sample extraction of non-existing forest fires is included, so in the present invention, we tried to develop a drought index related to the occurrence of forest fires through One-Class SVM. The kernel function used Gaussian, and the result was derived by normalizing the score calculated through the model. The results were compared by dividing the narrow range normalization (One-Class SVM 1, min: 0, max: 0.5) and wide range normalization (One-Class SVM 2, min: 0, max: 0.1). As a result of comparison, the smaller the area damaged by forest fires, the more non-drought in the One-Class SVM model, and the area of non-drought was higher than when weight was applied. However, as mentioned above, the occurrence of forest fires in Korea is largely due to human ignition, so wildfires can occur in the absence of drought. Therefore, the results of the One-Class SVM were also tested in the development of a forest fire risk index.

Then, the forest fire risk index according to a preferred embodiment of the present invention will be described in more detail with reference to FIGS. 8 to 14 .

8 is a view for explaining a map of a forest fire frequent area according to a preferred embodiment of the present invention.

In order to add information on places vulnerable to wildfires, a map of areas prone to forest fires provided by the National Academy of Forestry and Science was used. The map of areas prone to forest fires is a numerical map of location information for all wildfires (10,560 cases) that occurred from 1991 to 2015. Based on this location information, density analysis is performed to identify areas of concern where wildfires may occur frequently. This is the selected map. In order to select an area with a high incidence of forest fires, the average distance method and nearest neighbor analysis were performed to estimate the average distance between forest fires, and the density function first suggested by Kernel was used to finally select the area with a high incidence of forest fires. The causes of forest fires were divided into six categories: true stories of mountain climbers, incineration of paddy fields/field headlands, incineration of garbage, true stories of cemetery guests, true stories of cigarette fires, and others. From 1991 to 2015, an annual average of 422 wildfires occurred, and 2,102 ha of forest was burned every year. The region with the highest number of wildfires was Busan Metropolitan City (477 cases), followed by Seoul (412 cases), Incheon (391 cases), Ulsan (350 cases), Daegu (285 cases), Daejeon (263 cases), and Gwangju (203 cases). case), the incidence of wildfires was high in metropolitan areas (see Fig. 8). The period when the most wildfires occurred by month was April, with 3,250 wildfires taking place, accounting for 30.8% of the total. Looking at the statistical status of forest fires by cause, 4,392 cases of wildfires caused by mishaps by mountain climbers accounted for 41.6% of the total forest fires, followed by incineration of paddy fields/bedheads at 1,952 cases (18.5%), garbage incineration (830 cases (7.9%), and cigarette fires). was followed by 813 cases (7.7%).

9 is a view for explaining the reason for using the FFMC as a factor of the forest fire risk index according to a preferred embodiment of the present invention. (b) compares FFMC with the number of wildfires by 10 days in 2015.

The Canada Fire Weather Index (CFWI) is calculated using weather information such as temperature, relative humidity, wind speed, and precipitation. , ISI (Initial Spread Index), and BUI (Build Up Index) information is a meteorological index calculated by synthesizing information. FFMC predicts the moisture content of the fine fuel in the forest above ground, and DMC predicts the humidity of the surface fuel layer in the forest. DC predicts the likelihood of seasonal drought and geological formation by predicting the moisture of deep organic matter layers and coarse fuel in the ground, and ISI is calculated by combining FFMC and wind speed factors. Among them, FFMC predicts the moisture content of fine fuel using temperature, relative humidity, wind speed, and precipitation information, and the range is 0 to 99. A higher number means a higher probability of ignition. Among existing studies, when CFWI is applied to Korea, FFMC has a higher correlation than CFWI, which is the final index, and as a result of predicting the probability of forest fire with a regression analysis model using FFMC, it has a statistical significance of 5% ( Park Heung-seok et al., 2009). When CFWI and FFMC were compared with DWI (Daily Weather Index), an existing forest fire index in Korea, based on the 2015 forest fire (see FIG. 9), it was found that FFMC was more suitable for Korea than CFWI, and FFMC was used in the present invention. decided to do

10 is a view for explaining the result of comparing the spatial distribution of the modified FFMC according to the preferred embodiment of the present invention and the conventional FFMC, and FIG. 11 is the modified FFMC according to the preferred embodiment of the present invention and the conventional FFMC It is a diagram to explain the result of comparing with the number of forest fires per month.

Through FIG. 9, it was determined that FFMC is more suitable for Korea than CFWI, so that FFMC is used in the present invention. FFMC was developed in the 1970s and has been continuously updated since then, and in order to create a more accurate forest fire risk index model, the process of optimizing the existing FFMC for the Korean environment was carried out. FFMC calculates the equilibrium moisture content, drying rate, and minimum moisture content of the day using relative humidity, precipitation, temperature, and wind speed data, and finally calculates the FFMC. Accordingly, in the present invention, FFMC was optimized by modifying some coefficients according to the Korean environment. That is, the formula coefficients for calculating the FFMC initial value, the precipitation standard value, the FFMC standard value, and the drying rate were modified, and the coefficients of the formula for calculating the moisture content of the previous day and the formula for calculating the FFMC using the minimum amount were modified. After determining the range of coefficients by comparing the first formula developed in the 1970s with the formula currently used, the coefficients were modified within the range of each coefficient, and the optimal coefficients were found by comparing with the number and area of wildfires in Korea. The existing FFMC used precipitation measured at noon, but in the present invention, daily accumulated precipitation was used to make a daily FFMC. The precipitation reference value was increased by changing the precipitation data from instantaneous precipitation to cumulative precipitation. 10 is a result of comparing the spatial distribution of the existing FFMC and the optimized FFMC (that is, the modified FFMC according to the present invention), and since precipitation determines the degree of drying and is an important variable in FFMC calculation, the FFMC most simulates the pattern of precipitation. I could see that a lot was followed. The FFMC was modified to suit the Korean environment, and the existing FFMC, which was heavily biased in the amount of precipitation, was modified to affect it more gently.

11 is a result of comparing the existing FFMC and the optimized FFMC with the number of wildfires in Korea, and it can be seen that the optimized FFMC shows the time series better than the existing FFMC (refer to the purple arrow).

As shown in [Table 2] below, the CDF (Cumulative Distribution Function) value of the wildfire pixels was calculated after calculating the FFMC index of all wildfire pixels and non-fired pixels for each wildfire day for the existing FFMC and the optimized FFMC. The average was derived and then compared. CDF is the probability that a random variable is less than or equal to a certain value for a certain probability distribution, for example, if the CDF is 0.95, it is a high index corresponding to the top 5%. Since the distribution of the existing FFMC and the optimized FFMC value is different, each index value was converted into a CDF and the accuracy was analyzed for comparison.

	wildfire area	>= 50ha	< 50 ha	< 10ha	< 1 ha
existing FFMC	FFMC average	87.0244	86.0494	83.8506	80.9751
	CDF average	0.769	0.791	0.575	0.429
optimized FFMC	FFMC average	60.9135	58.9741	51.8598	49.2367
	CDF average	0.972	0.978	0.960	0.924

Compared to the existing FFMC, the average CDF value of the optimized FFMC was higher than 0.9, and the larger the damage area, the higher the average CDF value. Through this, it can be seen that the optimized FFMC is more suitable for Korea than the existing FFMC. The FFMC, modified for the Korean environment, was fused with the map of areas prone to forest fires and the drought index to develop a Fire Risk Index (FRI) suitable for the Korean environment. The existing Canadian Forest Fire Hazard Index (CFWI) calculation method is generated by multiplying a function for wind and FFMC, a function for DMC and DC, and a coefficient as follows.

CFWI = coefficient * f(wind,FFMC) * f(DMC,DC)

Therefore, the forest fire risk index (FRI) according to the present invention was developed using the revised FFMC (revised FFMC), drought idx, and forest fire frequent area map (frequency) as coefficients as follows, and considering the seasonal characteristics A process for giving a different weight (temporal weight) for each month has been added.

FRI = (frequency + 0.5) * (revised FFMC) * (1.5 - drought idx) * (temporal weight)

Here, the constant added to the forest fire frequency and drought idx is to prevent the situation in which FRI becomes 0 when multiplied when each indices has a minimum value of 0. Through analysis and analysis of how many percent of all pixels on the same day belong to, the constant determined to best simulate the occurrence of a forest fire was finally used.

12 is a view for explaining a drought index used to obtain a forest fire risk index according to a preferred embodiment of the present invention.

12 is a comparison of the distribution of the previously developed forest fire-related drought indices for wildfire and nonfire pixels, and various previously developed drought indices were tested to develop a forest fire risk index, Among them, the distribution of drought indices that showed the most significant results is shown. After deriving the forest fire risk index using these drought indices, as a result of analyzing the CDF and the top percent, Scheme 2 among the drought indices was judged to be the most suitable for the development of the forest fire risk index. Accordingly, the drought index used to obtain the forest fire risk index according to the present invention was a drought index according to Scheme 2 among drought indices to which a weight was applied.

The method of giving the monthly weight (temporal weight) is the method of giving weight so that the larger the number of monthly occurrences, the greater the FRI value (FRI_M1) and the monthly forest fire Two methods were tested (FRI_M2), in which only March to May with the highest number of occurrences were weighted.

To determine the suitability of FRI_M1 and FRI_M2, which were multiplied by monthly weights to consider seasonal characteristics, CDF analysis was performed similarly to comparing the existing FFMC and the modified FFMC. A probability that is less than or equal to a certain value. For comparison, the FFMC and FRI of all wildfire pixels and non-wildfire pixels were calculated for the days of wildfires from 2014 to 2017, and then the CDF values of the wildfire pixels were averaged and derived as shown in [Table 3] below.

	wildfire area	>= 50ha	< 50 ha	< 10ha	< 1 ha
existing FFMC	CDF average	0.769	0.791	0.575	0.429
Modified FFMC	CDF average	0.972	0.978	0.960	0.924
FRI_M1	CDF average	0.945	0.994	0.953	0.895
FRI_M2	CDF average	0.959	0.979	0.974	0.928

As a result, it was confirmed that the value was significantly improved compared to the existing FFMC, and when compared with the modified FFMC, the FRI showed similar or higher values to the modified FFMC, so it was found that the FRI value of the wildfire pixels was higher than that of the non-wildfire pixels. can be interpreted In order to understand the spatial simulation accuracy only on the day of the wildfire, not during the entire study period, the FFMC and FRI of each forest fire were analyzed to be among the top percent of the national forest fire risk index. As a result, as shown in [Table 4] below, compared to the existing FFMC, the proportion of forest fires within the top 5% of FRIs has increased by nearly double, and the forest fires within the top 40% increased from 58.55% to an average of 67.91%. did

difference%	<= 5%	<= 10%	<= 20%	<= 30%	<= 40%	<= 50%	> 50%	sum
existing FFMC	12.134	10.434	14.448	12.181	9.348	8.593	32.861	100
FRI_M1	20.608	10.161	16.144	11.728	9.497	7.028	24.834	100
FRI_M2	20.560	10.446	15.907	11.728	9.544	7.028	24.786	100

13 is a view for explaining a first monthly weight used to obtain a forest fire risk index according to a preferred embodiment of the present invention, Figure 14 is a second used to obtain a forest fire risk index according to a preferred embodiment of the present invention 2 It is a diagram for explaining the monthly weight. FIGS. 13 and 14 show the forest fire risk index (DWI) provided by the existing forest fire risk forecasting system of the Forestry Academy of the Korea Forest Service. The model (FRI_M1) weighted so that FRI has a higher value as the number increases, and the model weighted for March to May when the number of wildfires occurred the most (FRI_M2), and the number of wildfires in Korea from 2014 to 2017 As a result of comparison, both FRI_M1 and FRI_M2 showed good agreement with the rising and falling time series patterns in the number of monthly forest fires compared to DWI. It can be seen that the risk index (FRI) is in better agreement with the time series of monthly wildfire occurrences (see purple box). Therefore, as the monthly weight used to obtain the forest fire risk index according to the present invention, the first monthly weight weighted so that the FRI has a higher value as the number of monthly forest fires increases was used.

Then, a method for providing a forest fire risk index according to a preferred embodiment of the present invention will be described with reference to FIG. 15 .

15 is a flowchart for explaining a method for providing a forest fire risk index according to a preferred embodiment of the present invention.

Referring to FIG. 15 , the apparatus 100 for providing a forest fire risk index may obtain a soil moisture index downscaled to a grid size of 1 km based on a machine learning algorithm ( S110 ).

That is, the forest fire risk index providing device 100 is based on a machine learning algorithm, with TRMM precipitation data, ASCAT soil moisture data, NDVI, LST, and DEM as input variables, and soil moisture with GLDAS soil moisture data as output variables. Create an exponential downscaling model. Here, the machine learning algorithm may use one of random forest (RF), support vector regression (SVR), and artificial neural network (ANN). In particular, the present invention can generate a soil moisture index downscaling model using a random forest (RF).

At this time, the forest fire risk index providing device 100 uses, as training data, the upscaling data to a grid size of 25 km, which is the same as the grid size of the GLDAS soil moisture data, as training data for the unity of spatial resolution between the data, and the soil moisture index downscaling model create Then, the forest fire risk index providing apparatus 100 obtains a soil moisture index downscaled to a grid size of 1 km by inputting an input variable converted to a grid size of 1 km into a soil moisture index downscaling model.

More specifically, the forest fire risk index providing device 100 uses the data upscaled to a grid size of 25 km as training data, TRMM precipitation data, ASCAT soil moisture data, NDVI, LST and DEM as input variables, Using the GLDAS soil moisture data as an output variable, a first soil moisture index downscaling model may be generated. And, the forest fire risk index providing device 100 uses the data upscaled to a grid size of 25 km as training data, TRMM precipitation data, NDVI, LST, and DEM as input variables, and GLDAS soil moisture data as output variables. , it is possible to generate a second soil moisture index downscaling model. Then, the forest fire risk index providing device 100 inputs the input variable converted to the grid size of 1 km into the first soil moisture index downscaling model to obtain the first soil moisture index downscaled to the grid size of 1 km, The input variable converted to a grid size of 1 km is input to the second soil moisture index downscaling model to obtain a second soil moisture index downscaled to a grid size of 1 km, and the missing part in the first soil moisture index is the second By substituting the soil moisture index, the final soil moisture index downscaled to a grid size of 1 km can be obtained.

Then, the forest fire risk index providing apparatus 100 may obtain a drought index by using the soil moisture index, NDWI, and TCI downscaled to a grid size of 1 km (S130).

That is, the forest fire risk index providing apparatus 100 may obtain the drought index through the equation 0.4 * (downscaled soil moisture index) + 0.3 * (NDWI) + 0.3 * (TCI).

Then, the apparatus 100 for providing the forest fire risk index may obtain a forest fire risk index by using the wildfire frequent area map, the modified FFMC, the drought index, and the monthly weight (S150).

That is, the apparatus 100 for providing the forest fire risk index may obtain the forest fire risk index through the equation (wildfire frequent area map + 0.5) * (modified FFMC) * (1.5 - drought index) * (monthly weight).

Here, the monthly weight may be given a greater weight as the number of monthly forest fires increases, using the ratio of the number of monthly wildfires obtained based on all forest fires that have occurred in the past.

And, the modified FFMC is the conventional FFMC that calculates the FFMC based on the equilibrium moisture content, drying rate and minimum moisture content of the day calculated using the relative humidity, precipitation measured at noon, temperature, and wind speed. say FFMC. That is, in the modified FFMC, the coefficient of the formula for calculating the FFMC initial value, the precipitation reference value, the FFMC reference value and the drying rate is modified in the conventional FFMC, the coefficient of the formula calculating the moisture content of the previous day is modified, and the minimum moisture content is used It refers to an FFMC in which the coefficient of the equation for calculating the FFMC value is modified, the accumulated precipitation per day is used instead of the precipitation measured at noon, and the precipitation reference value is larger than that of the conventional FFMC.

Thereafter, the forest fire risk index providing apparatus 100 obtains a forest fire risk index value for the forecast date based on the acquired wildfire risk index (S170).

Even though all the components constituting the embodiment of the present invention described above are described as being combined or operated in combination, the present invention is not necessarily limited to this embodiment. That is, within the scope of the object of the present invention, all the components may operate by selectively combining one or more. In addition, all of the components may be implemented as one independent hardware, but a part or all of each component is selectively combined to perform some or all of the functions combined in one or a plurality of hardware program modules It may be implemented as a computer program having In addition, such a computer program is stored in a computer readable media such as a USB memory, a CD disk, a flash memory, etc., read and executed by a computer, thereby implementing the embodiment of the present invention. The recording medium of the computer program may include a magnetic recording medium, an optical recording medium, and the like.

The above description is merely illustrative of the technical idea of the present invention, and those of ordinary skill in the art to which the present invention pertains may make various modifications, changes and substitutions within the scope without departing from the essential characteristics of the present invention. will be. Accordingly, the embodiments disclosed in the present invention and the accompanying drawings are for explaining, not limiting, the technical spirit of the present invention, and the scope of the technical spirit of the present invention is not limited by these embodiments and the accompanying drawings . The protection scope of the present invention should be construed by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

< Explanation of symbols >

100: forest fire risk index providing device;

110: soil moisture index acquisition unit,

130: drought index acquisition unit,

150: forest fire risk index acquisition unit,

170: forecast department

Claims (13)

1. a forest fire risk index acquisition unit that acquires a forest fire risk index using a map of a wildfire-prone area, a revised Fine Fuel Moisture Code (FFMC), a drought index, and a monthly weight; and

   a forecasting unit for obtaining a forest fire risk index value for a forecast date based on the forest fire risk index obtained through the forest fire risk index obtaining unit;

   includes,

   The monthly weight, using the ratio of the number of monthly wildfires obtained based on all forest fires that have occurred in the past, is given a greater weight as the number of monthly forest fires increases,

   A device that provides a wildfire risk index.
2. In claim 1,

   The forest fire risk index acquisition unit,

   Obtaining the forest fire risk index through the formula (the wildfire hot zone map + 0.5) * (the modified FFMC) * (1.5 - the drought index) * (the monthly weight),

   A device that provides a wildfire risk index.
3. In claim 1,

   The modified FFMC is,

   In the conventional FFMC that calculates the FFMC based on the equilibrium moisture content, drying rate and minimum moisture content for the day calculated using relative humidity, precipitation measured at noon, temperature, and wind speed, FFMC initial value, precipitation reference value, FFMC reference value and dry The coefficient of the equation for calculating the tuning is corrected, the coefficient of the equation for calculating the moisture content of the previous day is corrected, the coefficient of the equation for calculating the FFMC value using the minimum moisture content is corrected, and the cumulative precipitation per day, not the precipitation measured at noon is used, and the precipitation reference value is larger than that of the conventional FFMC,

   A device that provides a wildfire risk index.
4. In claim 1,

   a drought index acquisition unit configured to acquire the drought index using a soil moisture index downscaled to a grid size of 1 km, a Normalized Different Water Index (NDWI), and a Temperature Condition Index (TCI);

   A device for providing a forest fire risk index further comprising a.
5. In claim 4,

   The drought index acquisition unit,

   Obtaining the drought index through the formula 0.4 * (the downscaled soil moisture index) + 0.3 * (the NDWI) + 0.3 * (the TCI),

   A device that provides a wildfire risk index.
6. In claim 4,

   Using the data upscaled to a grid size of 25 km as training data, based on a machine learning algorithm, TRMM (Tropical Rainfall Measuring Mission) precipitation data, ASCAT (Advanced SCATterometter) soil moisture data, NDVI (Normalized Difference Vegetation Index), A Soil Moisture Index downscaling model is created using Land Surface Temperature (LST) and Digital Elevation Model (DEM) as input variables and Global Land Data Assimilation System (GLDAS) soil moisture data as output variables, and a grid size of 1 km a soil moisture index acquisition unit configured to obtain the downscaled soil moisture index by inputting the input variable converted to , into the soil moisture index downscaling model;

   A device for providing a forest fire risk index further comprising a.
7. In claim 6,

   The soil moisture index acquisition unit,

   Using the data upscaled to a grid size of 25 km as training data, the TRMM precipitation data, the ASCAT soil moisture data, the NDVI, the LST, and the DEM are input variables, and the GLDAS soil moisture data are output variables. to create a first soil moisture index downscaling model,

   Using the data upscaled to a grid size of 25 km as training data, the TRMM precipitation data, the NDVI, the LST, and the DEM are input variables, and the GLDAS soil moisture data are output variables, second soil moisture create an exponential downscaling model,

   The input variable converted to a grid size of 1 km is input to the first soil moisture index downscaling model to obtain a first soil moisture index downscaled to a grid size of 1 km, and the input variable converted to a grid size of 1 km is the input variable. A second soil moisture index downscaled to a grid size of 1 km is obtained by inputting the second soil moisture index downscaling model, and the missing part in the first soil moisture index is replaced with the second soil moisture index to perform the downscaling. to obtain the soil moisture index,

   A device that provides a wildfire risk index.
8. A method for providing a forest fire risk index performed by a device for providing a forest fire risk index, comprising:

   obtaining a forest fire risk index using a forest fire-prone area map, a revised Fine Fuel Moisture Code (FFMC), a drought index, and a monthly weight; and

   obtaining a forest fire risk index value for a forecast date based on the forest fire risk index;

   includes,

   The monthly weight, using the ratio of the number of monthly wildfires obtained based on all forest fires that have occurred in the past, is given a greater weight as the number of monthly forest fires increases,

   A method of providing a wildfire risk index, including
9. In claim 8,

   The forest fire risk index acquisition step is,

   Consists of obtaining the forest fire risk index through the formula (the wildfire hot zone map + 0.5) * (the modified FFMC) * (1.5 - the drought index) * (the monthly weight),

   How to provide a wildfire risk index.
10. In claim 8,

    The modified FFMC is,

    In the conventional FFMC that calculates the FFMC based on the equilibrium moisture content, drying rate and minimum moisture content for the day calculated using relative humidity, precipitation measured at noon, temperature, and wind speed, FFMC initial value, precipitation reference value, FFMC reference value and dry The coefficient of the equation for calculating the tuning is corrected, the coefficient of the equation for calculating the moisture content of the previous day is corrected, the coefficient of the equation for calculating the FFMC value using the minimum moisture content is corrected, and the cumulative precipitation per day, not the precipitation measured at noon is used, and the precipitation reference value is larger than that of the conventional FFMC,

    How to provide a wildfire risk index.
11. In claim 8,

    obtaining the drought index using a soil moisture index downscaled to a grid size of 1 km, a Normalized Different Water Index (NDWI), and a Temperature Condition Index (TCI);

    A method of providing a forest fire risk index further comprising:
12. In claim 11,

    Using the data upscaled to a grid size of 25 km as training data, based on a machine learning algorithm, TRMM (Tropical Rainfall Measuring Mission) precipitation data, ASCAT (Advanced SCATterometter) soil moisture data, NDVI (Normalized Difference Vegetation Index), A Soil Moisture Index downscaling model is created using Land Surface Temperature (LST) and Digital Elevation Model (DEM) as input variables and Global Land Data Assimilation System (GLDAS) soil moisture data as output variables, and a grid size of 1 km obtaining the downscaled soil moisture index by inputting the input variable converted to , into the soil moisture index downscaling model;

    A method of providing a forest fire risk index further comprising:
13. A computer program stored in a computer-readable recording medium in order to execute the method for providing a forest fire risk index according to any one of claims 8 to 12 on a computer.

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