森林火灾发生概率预测涉及气象、树种、地理条件与人类活动等诸因素，气象参数对火灾的影响一直是森林火灾研究的重点之一。选用日平均湿度、降水量、平均风速、平均温度和日照时间等5种典型气象参数，利用BP神经网络分析了它们对火灾发生概率的综合影响；研究了多种气象参数综合作用下火灾发生概率的变化规律。结果表明，气象参数与火灾发生概率之间存在稳定的关联，神经网络在处理多参数综合影响方面具有较好的泛化能力，可以作为预测林火概率的可靠方法，为森林火灾研究提供了基础数据。

Calculation of forest fire probability is a complex issue concerning weather, tree species, geography conditions and human activities. The impact of weather parameters on fire has been one of the hot spots in the forest fire study. In this paper, the effect of 5 daily weather parameters on forest fire probability is investigtated. The 5 parameters are average humidity, precipitation, average wind speed, average temperature and sunshine time. Firstly, the correlation between each single parameter and fire probability is analyzed with a 24ayer BP neural network. The little value of MSE indicates ANN method gets close results with actual correlation. Secondly, the multi-correlaiton between fire probability and the 5 weather parameters is studied with a 3-layer BP network. From the 466 samples, 26 ones are randomly selected as test set, others as training set. The training MSE of BP network becomes smaller than 10^-6 after 5 000 epochs. For the test set, the relative error is less than 9.9%. It is indicated from the results that there are steady correlaiton between fire probability and weather parameters, and the BP network is a practical method in fire risk analysis. The study has practical implicaitons for forest fire risk prediction and the results can act as a basic data in forest fire proteciton.