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西北高原生物研究所 [9]
国家空间科学中心 [5]
兰州理工大学 [4]
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期刊论文 [38]
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Enhancing ENSO predictions with self-attention ConvLSTM and temporal embeddings
期刊论文
Frontiers in Marine Science, 2024, 卷号: 11, 页码: 1334210
作者:
Rui, Chuang
;
Sun, Zhengya
;
Zhang, Wensheng
;
Liu, Anan
;
Wei, Zhiqiang
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  |  
浏览/下载:0/0
  |  
提交时间:2024/05/28
El Niño-Southern Oscillation (ENSO)
deep learning for ENSO prediction
self-attention ConvLSTM
temporal embeddings
spring prediction barrier
Watering on porous pavement for improvement of environmental human thermal comfort in an ecological community in arid area: A case study in Lanzhou, China
期刊论文
Sustainable Cities and Society, 2022, 卷号: 85
作者:
Liu, Yu
;
Ma, Hongqiang
;
Zhang, Chune
;
Luo, Xinmei
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  |  
浏览/下载:28/0
  |  
提交时间:2022/09/22
Atmospheric temperature
Ecosystems
Evaporation
Linear regression
Pavements
Air temperature
Arid area
Climate index
Environmental parameter
Human thermal comfort
Lanzhou
Porous pavement
Thermal
Universal thermal climate index
Water evaporation
ADST: Forecasting Metro Flow Using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning
期刊论文
SENSORS, 2020, 卷号: 20, 期号: 16, 页码: 23
作者:
Jia, Hongwei
;
Luo, Haiyong
;
Wang, Hao
;
Zhao, Fang
;
Ke, Qixue
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2020/12/10
forecasting passenger flow
spatiotemporal networks
multi-task learning
attention mechanism
spatiotemporal dependency
Natural environment and childhood obesity: A systematic review
期刊论文
OBESITY REVIEWS, 2020, 卷号: 22, 页码: 1-8
作者:
Jia, Peng
;
Dai, Shaoqing
;
Rohli, Kristen E.
;
Rohli, Robert, V
;
Ma, Yanan
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  |  
浏览/下载:14/0
  |  
提交时间:2021/08/31
child
natural environment
obesity
physical activity
Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network
期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 9, 页码: 3283-3293
作者:
Pang, Junbiao
;
Huang, Jing
;
Du, Yong
;
Yu, Haitao
;
Huang, Qingming
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  |  
浏览/下载:75/0
  |  
提交时间:2019/12/10
Bus arriving time prediction
recurrent neural network
heterogenous measurement
long-range dependencies
multi-step-ahead prediction
Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
期刊论文
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2019, 卷号: 26, 期号: 17, 页码: 16825-16834
作者:
Su, Yana
;
Sha, Yongzhong
;
Zhai, Guangyu
;
Zong, Shengliang
;
Jia, Jiehua
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  |  
浏览/下载:14/0
  |  
提交时间:2022/03/01
Air pollution index
Wavelet transform
Time series
Change period
Timescale
Shanghai
Lanzhou
Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform
期刊论文
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2019, 卷号: 26, 期号: 17, 页码: 16825-16834
作者:
Su, Yana
;
Sha, Yongzhong
;
Zhai, Guangyu
;
Zong, Shengliang
;
Jia, Jiehua
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2019/11/15
Air pollution index
Wavelet transform
Time series
Change period
Timescale
Shanghai
Lanzhou
New data-driven method of simulating coronal mass ejections
期刊论文
ASTRONOMY & ASTROPHYSICS, 2019, 卷号: 626, 页码: A91
作者:
Liu, Cheng'Ao
;
Chen, Tao
;
Zhao, Xinhua
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  |  
浏览/下载:33/0
  |  
提交时间:2019/08/26
magnetohydrodynamics (MHD)
methods: numerical
Sun: coronal mass ejections (CMEs)
Influence of atmospheric PM2.5, PM10, O-3, CO, NO2, SO2, and meteorological factors on the concentration of airborne pollen in Guangzhou, China
期刊论文
ATMOSPHERIC ENVIRONMENT, 2019, 卷号: 212, 页码: 290, 304
作者:
Kumar, Sazal
;
Rahman, Ananna
;
Luo, Chuanxiu
;
Khan, Md Hafijur Rahaman
;
Ke, Jinzhao
收藏
  |  
浏览/下载:61/0
  |  
提交时间:2019/09/09
Atmospheric pollutants
Meteorological parameters
Airborne pollen
Statistical analysis
Guangzhou
Comprehensive prediction method for failure rate of transmission line based on multi-dimensional cloud model
期刊论文
IET GENERATION TRANSMISSION & DISTRIBUTION, 2019, 卷号: 13, 期号: 9
作者:
Lei, Jiazhi
;
Gong, Qingwu
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2019/12/05
environmental factors
clouds
grey systems
weather forecasting
power transmission lines
failure rate
transmission line
multidimensional cloud model
multiple external environmental factors
Cholesky decomposition
meteorological early warning system
icing forecasting system
comprehensive prediction model
prediction method
Guizhou Power Grid
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