The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and prediction of fire spread. Moisture distribution is important to determine wild fire rating. However, it is often difficult to predict moisture distribution because of a complex terrain, changeable environments and low cover of commercial communication signals inside the forest. This study proposes a moisture content prediction system composed of environmental data collected using a long range radio frequency band 433 MHz wireless sensor network and data processing for moisture prediction based on a BP (back-propagation) neural network. In the fall of 2019, twenty nodes for the collection of environmental data were placed in four forest stands of Maoershan National Forest for a month; 7440 sets of data including temperature, humidity, wind speed and air pressure were obtained. Half the data were used as a training set, the other as a testing set for a BP neural network. The results show that the average absolute error between the predicted value and the real value of moisture content of fuels of Larix gmelini, Betula platyphylla, Juglans mandshurica, and Quercus mongolica stands was 0.94%, 0.21%, 0.86%, 0.97%, respectively. The prediction accuracy was relatively high. The proposed distributed moisture content prediction method has the advantages of wide coverage and good real-time performance; at the same time, it is not limited by commercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.

死亡森林燃料的水分含量是森林火灾风险等级和预测火灾蔓延的重要指标。水分分布对于确定野火等级很重要。然而，由于复杂的地形、多变的环境和森林内商业通信信号的低覆盖率，通常很难预测水分分布。本研究提出了一种水分含量预测系统，该系统由使用远程射频频段 433 MHz 无线传感器网络收集的环境数据和基于 BP（反向传播）神经网络的水分预测数据处理组成。2019年秋季，20个环境数据采集节点在帽儿山国家森林4个林分放置一个月；7440组数据包括温度、湿度、获得风速和气压。一半数据用作训练集，另一半用作 BP 神经网络的测试集。结果表明，燃料含水率预测值与实际值之间的平均绝对误差为落叶松、白桦、胡桃、蒙古栎林分率分别为0.94%、0.21%、0.86%、0.97%。预测准确率相对较高。所提出的分布式含水率预测方法具有覆盖面广、实时性好等优点；同时不受商业信号的限制，特别适用于偏远山区的森林火灾预测。