Research On Prediction Of Latent Forest-Fire Harm Degree In Heilongjiang

Heilongjiang province has obvious advantage at forest resource in china, but oftencatches forest-fire which brings enormous breakages. Thus Heilongjiang has the largest forest-fire area in China annually, and is the seriousest area on fire disservice. So reseach on latentforest-fire harm prediction is very important to the prediction of forest-fire of Heilongjiangprovince.The main content and results of this paper are as below:(1) Based on the characteristics of vegetation and climate in Heilongjiang, Heilongjiangwas partitioned into two research regions: Frigid temperate coniferous forest region andTemperate broadleaved and conifer mixed forest region; research periods: they are from Aprilto June and October in Frigid temperate coniferous forest region, they are from March to Juneand October in Temperate broadleaved and conifer mixed forest region, these periods are themost frequent periods of forest-fire. Different districts and different periods lead to largedifferences in characteristics of forest vergetation. So the occuring and spreading of forest-fireare different. These showed that it is logical to research separately according to differentdistuicts and different periods.(2) By the statistical data of forest-fire and the weather from 1980 to 2003 in Heilongjiang, using pluralistic regression theory a prediction model of forest-fire area was establishedThe model was non-linear.The independent variables were intraday average wind speed,relative humidity and intraday average temperature. The prediction model was effectual whenthe rainfall was 0 mm. In the model the dependent variable was not the forest-fire area, butthe grade of forest-fire area, by which we could know the forest-fire harm degree. Checkingup the model using the statistical data of forest-fire from 2001 to 2003 in Heilongjiang, theaccurate rate was 63.3%. This showed that it was feasible to predict the forest-fire harm degreeby the grade of forest-fire area, so the model could be used to predict the latent forest-fireharm grade.(3) The concept of Forest-fire harm intensity was defined ; Using differential equationstheory a prediction model of forest-fire harm intensity was established by the statistical data offorest-fire and the weather from 1980 to 2003 in Heilongiiang. The prediction model waseffectual when the rainfall was 0 mm.The independent variable were intraday average windspeed, relative humidity and intraday average temperature. Forest-fire harm intensity couldfactually reflect forest-fire harm degree. By proof-test, the accurate rate was 65% in Frigidtemperate coniferous forest region, 72.7% in Temperate broadleaved and conifer mixed forestregion, the divisioal standard of forest-fire harm intensity grade was also given, so the model of forest-fire harm intensity prediction could predict short-term latent forest-fire harm degreein Heilongjiang.(4) Using BP Neural Network (short for NN) a prediction model of forest-fire harmintensity was established. Its import tier had four nerve cells. Its hiddent tier had nine nervecells. Its export tier had one nerve cell. The network simulated preferablely the interconnectionbetween forest-fire area and intraday average wind speed, relative humidity, intradayaverage temperature, time of burning, and thence gave out forest-fire harm intensity basis onthe BP NN. This showed that NN technique was an important tool on prediction of forest-fire.(5)By statistic analysis we knew that there existed some pertinency between forest-fireharm intensity of the prophrase springtime fireproofing and the winter average wind speed,snow fall, average relative humidity and average temperature in last winter. This paper usingpluralistic regression theory established the linear relational model, in which the winter ofFrigid temperate coniferous forest region it chose time from Oct. to Mar. and the winter ofTemperate broadleaved and conifer mixed forest region it chose time from Nov. to Feb.therelation model between months was also established in spring-fireproofing-time. All thesemodels constitute the model of latent forest-fire harm intensity in spring, and the proof-testingshowed that the prediction result approximated to actual value. So the variables and model thatthis paper chose were logical, which could used in prediction of long-term latent forest-fireharm intensity.(6) In this paper, "The forest-fire forecast system of Heilongjiang province" was settedup by using the teetmology of geography information system. This system connected weatherdata-base and forest-fire harm intensity and established identification model. By inputting theintraday weather data of all prefectural districts in fireproofing period, the intraday latentforest-fire harm grade could be forecastedand be made visuable.