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Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
自动化研究所 [4]
地理科学与资源研究所 [3]
生态环境研究中心 [2]
力学研究所 [1]
长春光学精密机械与物... [1]
遥感与数字地球研究所 [1]
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OAI收割 [15]
内容类型
期刊论文 [14]
会议论文 [1]
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2025 [1]
2023 [2]
2022 [4]
2021 [2]
2020 [2]
2018 [2]
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A framework for learning symbolic turbulence models from indirect observation data via neural networks and feature importance analysis
期刊论文
OAI收割
JOURNAL OF COMPUTATIONAL PHYSICS, 2025, 卷号: 537, 页码: 22
作者:
Wu CT(吴楚畋)
;
Zhang XL(张鑫磊)
;
Xu D(徐多)
;
He GW(何国威)
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收藏
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浏览/下载:1/0
  |  
提交时间:2025/08/18
Turbulence model
Ensemble Kalman method
Symbolic regression
Feature importance analysis
Neural networks
Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China
期刊论文
OAI收割
REMOTE SENSING, 2023, 卷号: 15, 期号: 6, 页码: 1513
作者:
Dai, Xiaoliang
;
Zhu, Yunqiang
;
Sun, Kai
;
Zou, Qiang
;
Zhao, Shen
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收藏
  |  
浏览/下载:25/0
  |  
提交时间:2023/05/06
landslide susceptibility
geographical random forest
spatial heterogeneity
local feature importance
spatial cross validation
Performance prediction of disc and doughnut extraction columns using bayes optimization algorithm-based machine learning models
期刊论文
OAI收割
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION, 2023, 卷号: 183, 页码: 11
作者:
Su, Zhenning
;
Wang, Yong
;
Tan, Boren
;
Cheng, Quanzhong
;
Duan, Xiaofei
  |  
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2023/05/19
Pulsed disk and doughnut column
Machine learning
Modeling
Feature importance
Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method
期刊论文
OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 21
作者:
Liu, Yuanyuan
;
Wang, Shaoqiang
;
Chen, Jinghua
;
Chen, Bin
;
Wang, Xiaobo
  |  
收藏
  |  
浏览/下载:57/0
  |  
提交时间:2022/11/09
crop yield prediction
remote sensing
deep learning
feature importance
attention
Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method
期刊论文
OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 21
作者:
Liu, Yuanyuan
;
Wang, Shaoqiang
  |  
收藏
  |  
浏览/下载:52/0
  |  
提交时间:2022/11/09
crop yield prediction
remote sensing
deep learning
feature importance
attention
Rice Yield Prediction and Model Interpretation Based on Satellite and Climatic Indicators Using a Transformer Method
期刊论文
OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 21
作者:
Liu, Yuanyuan
;
Wang, Shaoqiang
;
Chen, Jinghua
;
Chen, Bin
;
Wang, Xiaobo
  |  
收藏
  |  
浏览/下载:54/0
  |  
提交时间:2022/11/14
crop yield prediction
remote sensing
deep learning
feature importance
attention
Application of Multi-Source Data for Mapping Plantation Based on Random Forest Algorithm in North China
期刊论文
OAI收割
REMOTE SENSING, 2022, 卷号: 14, 期号: 19, 页码: 4946-1-19
作者:
Wu, Fan
;
Ren, Yufen
;
Wang, Xiaoke
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收藏
  |  
浏览/下载:35/0
  |  
提交时间:2023/02/02
plantation
forest classification
random forest
feature importance
multi-source data
Profiles, spatial distributions and inventory of brominated dioxin and furan emissions from secondary nonferrous smelting industries in China
期刊论文
OAI收割
JOURNAL OF HAZARDOUS MATERIALS, 2021, 卷号: 55, 期号: 19, 页码: 12741-12754
作者:
Yang, Yuanping
;
Zheng, Minghui
;
Yang, Lili
;
Jin, Rong
;
Li, Cui
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收藏
  |  
浏览/下载:26/0
  |  
提交时间:2022/01/04
applicability domain
artificial intelligence
best practices
feature importance
machine learning modeling
model applications
model interpretation
predictive modeling
Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A
期刊论文
OAI收割
Plasma Physics and Controlled Fusion, 2021, 卷号: 63
作者:
Zhong,Y
;
Zheng,W
;
Chen,Z Y
;
Xia,F
;
Yu,L M
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收藏
  |  
浏览/下载:37/0
  |  
提交时间:2021/06/21
disruption prediction
machine learning
LightGBM
feature importance
Road Safety Performance Function Analysis With Visual Feature Importance of Deep Neural Nets
期刊论文
OAI收割
IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 3, 页码: 735-744
作者:
Guangyuan Pan
;
Liping Fu
;
Qili Chen
;
Ming Yu
;
Matthew Muresan
  |  
收藏
  |  
浏览/下载:37/0
  |  
提交时间:2021/03/11
Deep learning
deep neural network (DNN)
feature importance
road safety performance function