en, we give the formula for calculating the total forest fire risk weather index as follows,

H T Z = A + B + C + D + E , (2)

where, A is the forest fire risk weather index based on the highest temperature (Table 5), B is the forest fire risk weather index based on the minimum (14 o’clock) relative humidity (Table 6), and C is the forest fire risk weather index based on the number of consecutive no precipitation days after the precipitation day (Table 7), D is the forest fire risk weather index based on the maximum wind power (Table 8), and E is the forest fire risk weather index based on the small amount of evaporation the next day (Table 9).

Finally, we also need to correct the fire risk rating. Farmers burning straw and ritual fires such as the Ching Ming Festival and the winter solstice may cause forest fires. The above-mentioned human factors should also be considered, and we give an additional level of correction. For example, in the period from February to March and September to October of each year, when the forest fire risk

Table 4. Forest fire risk rating specific division.

Notes: HTZ is total forest fire risk weather index.

Table 5. The forest fire risk weather index based on the highest temperature.

Table 6. The forest fire risk weather index based on the minimum (14 o’clock) relative humidity.

Table 7. The forest fire risk weather index based on the number of consecutive no precipitation days after the precipitation day.

Notes: The precipitation is less than 0.3 mm as no precipitation calculation.

Table 8. The forest fire risk weather index based on the maximum wind power.

Table 9. The forest fire risk weather index based on the small amount of evaporation the next day.

rating is less than or equal to 4, we should added by one level to make a revised forecast. In addition, during the Ching Ming Festival and the winter solstice, the people sacrificed and burned fire. Since the specific date has not been determined, when the forest fire risk rating is less than or equal to level 4, it should be manually increased by one level.

3.3. Forecast Service Documentation and Platform

When the next day’s forecast has no precipitation (including local precipitation or sporadic precipitation), the forest fire risk level ≥3, the system will automatically generate a word document. The forecast service contents of each level are as follows: When the forest fire risk is level 3, it is expected that the weather in this city (county, district) will be dryer from today to tomorrow tomorrow, and the forest fire risk meteorological level will reach three levels, which is more likely to cause forest fires, etc. Do a good job in forest fire prevention. When the forest fire insurance is level 4, it is expected that the weather in this city (county, district) will be very dry from today to tomorrow. The weather level of forest fire danger will reach four levels, which is easy to cause forest and other fires. Please do forest fire prevention work. When the forest fire risk is 5, it is expected that the weather in the city (county, district) will be extremely dry from today to tomorrow. The weather level of forest fire danger will reach five grades, which is very likely to cause forest and other fires. Please do forest fire prevention work.

In order to achieve the specific application of the forest fire risk meteorological grade, the specific application, the forest fire danger meteorological grade, the formation of the corresponding forecast service product, the project development supporting platform, the technical support for the meteorological service personnel, and the decision meteorological service for the purpose of disaster prevention and reduction Provide more data support.

In response to the above requirements, the platform functions were analyzed in detail, and the requirements for the interface and the setting of the window were unified, and the preliminary design ideas were gradually completed. Under the premise of stable and accurate operation of the platform, in order to enable the operator to use the platform easily and conveniently, the research group is committed to simplifying the system configuration and operation of the platform and simplifying the interface. The platform has been extensively tested, modified, and repeatedly validated to continuously improve its stability, accuracy, and operability.

The daily maximum temperature, the daily minimum relative humidity, the previous precipitation and the number of consecutive sunny days, the wind, the temperature difference, the sunshine hours, etc. are input, and the corresponding daily evaporation amount and the forest fire risk weather index value of each element are automatically calculated. Click on “Fire Insurance Forecast” to automatically calculate the forest fire risk rating. When the forest fire risk level reaches the third level, the warning icon is displayed. After selecting the corresponding template and drafting, proofreading, reviewing, issuing, transmitting and other relevant information on the right side of the platform, click “Generate Document”, the platform can form the corresponding forest fire risk rating according to the template service product.

4. Conclusion and Discussion

In this paper, the multi-linear regression analysis method is used to calculate the daily evaporation amount in the more advanced SPSS16.0 software (Fan et al., 2017; Yu et al., 2017), and the data of the last 5 years of each site are selected and fitted. After repeated comparison and verification, the closest linearity to the reality is established. The regression equation makes the predicted evaporation amount basically consistent with the annual historical data.

Compared with the perennial, this paper adds weather factors such as daily maximum wind speed, daily average wind speed, daily maximum temperature, and recent precipitation level, which are closely related to evaporation, making the forest fire risk rating more accurate.

Cite this paper
Zhuang, G. (2019) Study on Forecasting Method of Forest Fire Risk Grade in Putian City, China. Journal of Geoscience and Environment Protection, 7, 198-205. doi: 10.4236/gep.2019.712014.
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