基于模糊数据挖掘技术的林火行为预测研究

在预测区域复杂的自然条件下，利用模糊数据挖掘技术（FDM）寻找与之相匹配的林火蔓延模型来预测林火行为。以广州市为例，构建了火灾数据样本集，确定了可燃物水分含量、可燃物负荷量、易燃程度、坡度等4个因子为影响林火行为的指标，利用FDM的模式的发现与预测功能，归纳了3类模式，并依照择近原则匹配各模式所适用的林火蔓延模型。此模型应用时可以由火场指挥者实时提供的火灾因子资料，根据FDM最大贴近度原则，选择合适的林火蔓延模型预测林火行为，提高了林火行为预测的可靠性。

Predicting Forest Fire Behavior Based on Fuzzy Data Mining Technique

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.