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科研机构
兰州理工大学 [2]
大连理工大学 [2]
地理科学与资源研究所 [1]
内容类型
期刊论文 [3]
会议论文 [2]
发表日期
2019 [5]
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Comparison of IDW, cokriging and ARMA for predicting spatiotemporal variability of soil salinity in a gravel-sand mulched jujube orchard
期刊论文
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2019, 卷号: 191, 期号: 6
作者:
Zhao, Wenju
;
Cao, Taohong
;
Li, Zongli
;
Sheng, Jie
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浏览/下载:0/0
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提交时间:2019/11/15
Spatial variability
IDW
Cokriging
Temporal variability
ARMA time series
Comparison of IDW, cokriging and ARMA for predicting spatiotemporal variability of soil salinity in a gravel–sand mulched jujube orchard
期刊论文
Environmental Monitoring and Assessment, 2019, 卷号: 191, 期号: 6
作者:
Zhao, Wenju
;
Cao, Taohong
;
Li, Zongli
;
Sheng, Jie
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2020/11/14
Forecasting
Gravel
Inverse problems
Sand
Soils
Time series
Autoregressive moving average model
Co-Kriging
Inverse distance weighting
Sampling efficiency
Spatial distribution map
Spatial variability
Spatiotemporal variability
Temporal variability
A new forecasting model for groundwater quality based on short time series monitoring data
期刊论文
IOP Conference Series: Earth and Environmental Science, 2019, 卷号: 227, 期号: 6
作者:
Yao,Ling
;
Zhu,Yunqiang
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  |  
浏览/下载:12/0
  |  
提交时间:2019/05/22
A filter algorithm based on ARMA model to suppress the influence of atmospheric disturbance in laser straightness measurement
会议论文
TENTH INTERNATIONAL SYMPOSIUM ON PRECISION ENGINEERING MEASUREMENTS AND INSTRUMENTATION, 2019-01-01
作者:
Wang, Yayong
;
Liu, Shujie
;
Zhang, Shixin
;
Huang, Yubin
;
Fan, Kuangchao
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  |  
浏览/下载:6/0
  |  
提交时间:2019/12/02
Atmospheric disturbance
time series analysis
ARMA
laser beam drift
A novel Lasso-ARMA model for time series prediction
会议论文
2018 Chinese Automation Congress, CAC 2018, Xi'an, China, 2018-11-30
作者:
Xu, Meiling
;
Zhang, Jiadong
;
Li, Zhihui
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浏览/下载:12/0
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提交时间:2019/12/02
Autocorrelation
Forecasting
Learning systems
Random processes
Regression analysis
Stochastic systems, ARMA
Autocorrelation functions
Estimation methods
Lasso regressions
Machine learning methods
Model specifications
Real-world datasets
Time series prediction, Time series
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