典型文献
Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan national forest, China using a LoRa wireless network
文献摘要：
The moisture content of dead forest fuel is an important indicator of risk levels of forest fires and predic-tion 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 com-munication signals inside the forest. This study proposes a moisture content prediction system composed of environ-mental 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 pre-dicted 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 com-mercial signals and so it is especially suitable for forest fire prediction in remote mountainous areas.
文献关键词：
中图分类号：
作者姓名：
Bo Peng;Jiawei Zhang;Jian Xing;Jiuqing Liu
作者机构：
Northeast Forestry University,Harbin 150040,People's Republic of China
文献出处：
引用格式：
[1]Bo Peng;Jiawei Zhang;Jian Xing;Jiuqing Liu-.Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan national forest, China using a LoRa wireless network)[J].林业研究（英文版）,2022(03):899-909
A类：
Maoershan,gmelini
B类：
Distribution,prediction,moisture,content,dead,forest,floor,national,China,using,LoRa,wireless,network,important,indicator,risk,levels,fires,spread,Moisture,distribution,determine,wild,rating,However,often,difficult,because,complex,terrain,changeable,environments,low,commercial,munication,signals,inside,This,study,proposes,system,composed,data,collected,long,range,radio,frequency,band,MHz,sensor,processing,back,propagation,neural,In,fall,twenty,nodes,collection,environmental,were,placed,four,stands,National,Forest,month,sets,including,temperature,humidity,wind,speed,air,pressure,obtained,Half,used,training,other,testing,results,show,that,average,absolute,error,between,dicted,value,real,fuels,Larix,Betula,platyphylla,Juglans,mandshurica,Quercus,mongolica,was,respectively,accuracy,relatively,high,proposed,distributed,method,has,advantages,wide,coverage,good,performance,same,not,limited,by,especially,suitable,remote,mountainous,areas
AB值：
0.556786
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