基于BP神经网络的森林火环境预测方法

The Research of Using BP Neural Network in Prediction of Woods Fire Insurance Grade

提出了一种基于BP神经网络的森林火环境预测新方法。该方法研究大气环流指数与森林火险的关系,将已有大气环流指数和森林火险等级数据对BP神经网络进行训练,利用BP网络反传学习,通过抑制性反馈和兴奋性前馈作用实现自组织学习,完成大气环流指数与森林火环境指数关系的非线性映射,对大量数据实现特征选择和森林火环境预测。它对春季和夏季的预测准确率分别为 89%和 82%,试验结果表明该方法的正确性和可行性。

The relation between circumfluence index and woods fire.index is described. BP neural network is trained by the known circumfluence index and woods fire index. The restricted feedback and stimulant feed forwardcalculation are to realize self-organizational learningof BP Network.Thereby the nonlinear mapping between circumfluence index and woods fire.index isperformed. With this network, characteristic choice can be carriedout and the accurate prediction of woods fire insurance grade can be given. Its accurate prediction rate is 89%in spring and 82%in summer. The correctnessand feasibilityare illustratedwith the simulate results.