典型文献
Machine learning model for snow depth estimation using a multisensory ubiquitous platform
文献摘要：
Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction.Despite advances in remote sensing technology and enhanced satellite observations,the estimation of snow depth at local scale still requires improved accuracy and flexibility.The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge.In this paper,a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward.Significantly,the results of Random Forest classifier showed an accuracy of 94％,indicating a promising alternative in snow depth measurement compared to in situ,LiDAR,or expensive large-scale wireless sensor network,which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.
文献关键词：
中图分类号：
作者姓名：
Sofeem NASIM;Mourad OUSSALAH;Bj?rn KL?VE;Ali Torabi HAGHIGHI
作者机构：
Department of Computer Science and Engineering,University of Oulu,Oulu 90570,Finland;Water,Energy and Environmental Engineering,University of Oulu,Oulu 90570,Finland
文献出处：
引用格式：
[1]Sofeem NASIM;Mourad OUSSALAH;Bj?rn KL?VE;Ali Torabi HAGHIGHI-.Machine learning model for snow depth estimation using a multisensory ubiquitous platform)[J].山地科学学报（英文版）,2022(09):2506-2527
A类：
multisensory
B类：
Machine,learning,snow,depth,estimation,using,ubiquitous,platform,Snow,important,parameter,that,guides,several,hydrological,applications,climate,change,prediction,Despite,advances,remote,sensing,technology,enhanced,satellite,observations,local,scale,still,requires,improved,accuracy,flexibility,wearable,promote,new,prospects,tackling,this,challenge,In,paper,IoT,exploits,pressure,acoustic,readings,estimate,classify,classes,some,machine,models,have,been,put,forward,Significantly,results,Random,Forest,classifier,showed,indicating,promising,alternative,measurement,compared,situ,LiDAR,expensive,large,wireless,network,which,may,foster,development,further,affordable,ecological,monitoring,systems,cheap,sensors
AB值：
0.682856
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