基于模糊数据挖掘技术的林火行为预测研究

肖化顺,张贵*,蔡学理

南京林业大学学报(自然科学版) ›› 2006, Vol. 30 ›› Issue (04) : 97-100.

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南京林业大学学报(自然科学版) ›› 2006, Vol. 30 ›› Issue (04) : 97-100. DOI: 10.3969/j.jssn.1000-2006.2006.04.023
研究论文

基于模糊数据挖掘技术的林火行为预测研究

  • 肖化顺,张贵*,蔡学理
作者信息 +

Predicting Forest Fire Behavior Based on Fuzzy Data Mining Technique

  • XIAO Hua-shun, ZHANG Gui*, CAI Xue-li
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摘要

<正>经大量解剖川硬吱肿腿蜂。发现该寄生蜂的雌性生殖系统位于其中后胸和整个腹部,由卵巢、侧输卵管、中输卵管及其附腺、受精囊、毒囊、毒腺和外生殖器组成。川硬皮肿腿蜂两侧卵巢的卵巢管总数为6~9根;同一个本两个卵巢的卵巢管教不一定相等。单侧卵巢的卵管数为3~5根;外生殖器由毒针、产卵瓣、产卵管鞘3个管状构造套叠组成,每一部分均由两瓣嵌合而成,其中产卵瓣鞘仅包裹在产卵管端部,且很短。

Abstract

Fire spreading is of an utmost intricate flaming phenomenon, the scientific fire spreading model is the key to forecasting forest fire behaviors. Generally, it is complicated and time-consuming to find improvement and appraise the fire-spreading model, which is applicable to a certain forest area. At present, the method to forecast is a single fire-spreading model or the one selected by commanders. However, based on the Fuzzy Data Mining Technique, indices for fire behavior are established to form fire data warehouse. These are quantity of forest combustible, auriferous quantity of it, inflammability and slope in Guangzhou. So the discovering and forecasting function of Fuzzy Data Mining technique are used on forecasting forest. According to the handy principle, three modes are discovered and the corresponding fire-spreading mode is matched for the certain style, which is induced to form the fire data warehouse. Furthermore, so long as the real-time fire indices are provided by commanders, the close model can be distinguished. Therefore, the suited fire-spreading model can be selected automatically to forecast the forest fire behavior, in this way, dependability of forecasting can be gained.

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导出引用
肖化顺,张贵*,蔡学理. 基于模糊数据挖掘技术的林火行为预测研究[J]. 南京林业大学学报(自然科学版). 2006, 30(04): 97-100 https://doi.org/10.3969/j.jssn.1000-2006.2006.04.023
XIAO Hua-shun, ZHANG Gui*, CAI Xue-li. Predicting Forest Fire Behavior Based on Fuzzy Data Mining Technique[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2006, 30(04): 97-100 https://doi.org/10.3969/j.jssn.1000-2006.2006.04.023
中图分类号: S762   

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