Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan national forest, China using a LoRa wireless network  

作　　者：Bo Peng Jiawei Zhang Jian Xing Jiuqing Liu 

机构地区：[1]Northeast Forestry University,Harbin,150040,People’s Republic of China

出　　处：《Journal of Forestry Research》2022年第3期899-909,共11页林业研究（英文版）

基　　金：This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.2572020AW43;NO.2572019CP19);the National Natural Science Foundation of China(Grant No.31470715);the Natural Science Foundation of Hei-longjiang Province(Grant No.TD2020C001);the project for cultivating excellent doctoral dissertation of forestry engineering(Grant No.LYGCYB202009).

摘　　要：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.

关 键 词：Distributed moisture content prediction Dead fuel BP neural network 

分 类 号：S762[农业科学—森林保护学]