globalchange  > 过去全球变化的重建
DOI: 10.1007/s10661-019-7542-9
WOS记录号: WOS:000470677200005
论文题名:
Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data
作者: Zewdie, Gebreab K.1; Lary, David J.1; Liu, Xun1; Wu, Daji1; Levetin, Estelle2
通讯作者: Zewdie, Gebreab K.
刊名: ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN: 0167-6369
EISSN: 1573-2959
出版年: 2019
卷: 191, 期:7
语种: 英语
英文关键词: Pollen ; NEXRAD ; Weather ; Machine learning ; Neural network ; Random forest ; Environmental public health
WOS关键词: CLIMATE-CHANGE ; ALLERGENIC POLLEN ; AMBROSIA POLLEN ; RANDOM FORESTS ; RAGWEED
WOS学科分类: Environmental Sciences
WOS研究方向: Environmental Sciences & Ecology
英文摘要:

Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen depend on the ambient weather conditions. The temperature, rainfall, humidity, cloud cover, and wind are known to affect the amount of pollen in the atmosphere. In the past, various regression techniques have been applied to estimate and forecast the daily pollen concentration in the atmosphere based on the weather conditions. In this research, machine learning methods were applied to the Next Generation Weather Radar (NEXRAD) data to estimate the daily Ambrosia pollen over a 300 km x 300 km region centered on a NEXRAD weather radar. The Neural Network and Random Forest machine learning methods have been employed to develop separate models to estimate Ambrosia pollen over the region. A feasible way of estimating the daily pollen concentration using only the NEXRAD radar data and machine learning methods would lay the foundation to forecast daily pollen at a fine spatial resolution nationally.


Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/141109
Appears in Collections:过去全球变化的重建

Files in This Item:

There are no files associated with this item.


作者单位: 1.Univ Texas Dallas, William B Hanson Ctr Space Sci, Richardson, TX 75080 USA
2.Univ Tulsa, Tulsa, OK 74104 USA

Recommended Citation:
Zewdie, Gebreab K.,Lary, David J.,Liu, Xun,et al. Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data[J]. ENVIRONMENTAL MONITORING AND ASSESSMENT,2019-01-01,191(7)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Zewdie, Gebreab K.]'s Articles
[Lary, David J.]'s Articles
[Liu, Xun]'s Articles
百度学术
Similar articles in Baidu Scholar
[Zewdie, Gebreab K.]'s Articles
[Lary, David J.]'s Articles
[Liu, Xun]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Zewdie, Gebreab K.]‘s Articles
[Lary, David J.]‘s Articles
[Liu, Xun]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.