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Improved Deep Learning Predictions for Chlorophyll Fluorescence Based on Decomposition Algorithms: The Importance of Data Preprocessing 期刊论文
WATER, 2023, 卷号: 15, 期号: 23, 页码: 19
作者:  Wang, Lan;  Xie, Mingjiang;  Pan, Min;  He, Feng;  Yang, Bing;  Gong, Zhigang;  Wu, Xuke;  Shang, Mingsheng;  Shan, Kun
收藏  |  浏览/下载：198/0  |  提交时间：2024/01/17
chlorophyll fluorescence  deep learning  online monitoring  time-series decomposition  Dianchi Lake  
Combined Physical Process and Deep Learning for Daily Water Level Simulations across Multiple Sites in the Three Gorges Reservoir, China 期刊论文
WATER, 2023, 卷号: 15, 期号: 18, 页码: 16
作者:  Xie, Mingjiang;  Shan, Kun;  Zeng, Sidong;  Wang, Lan;  Gong, Zhigang;  Wu, Xuke;  Yang, Bing;  Shang, Mingsheng
收藏  |  浏览/下载：8/0  |  提交时间：2023/12/25
water level prediction  Three Gorges Reservoir  deep learning  hybrid model  
A comprehensive wind speed forecast correction strategy with an artificial intelligence algorithm 期刊论文
FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 卷号: 10, 页码: 12
作者:  Zhao, Xueliang;  Sun, Qilong;  Tang, Wanru;  Yu, Shuang;  Wang, Boyu
收藏  |  浏览/下载：32/0  |  提交时间：2023/02/07
wind speed  numerical weather prediction  forecast correction  deep learning  artificial intelligence  
Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions 期刊论文
WATER RESOURCES RESEARCH, 2022, 卷号: 58, 期号: 10, 页码: 15
作者:  Huang, Sheng;  Xia, Jun;  Wang, Yueling;  Wang, Wenyucheng;  Zeng, Sidong;  She, Dunxian;  Wang, Gangsheng
收藏  |  浏览/下载：36/0  |  提交时间：2022/12/26
river modeling  machine learning  hydrodynamic models  water exchange  downstream boundary conditions  
Predicting Monthly Runoff of the Upper Yangtze River Based on Multiple Machine Learning Models 期刊论文
SUSTAINABILITY, 2022, 卷号: 14, 期号: 18, 页码: 23
作者:  Li, Xiao;  Zhang, Liping;  Zeng, Sidong;  Tang, Zhenyu;  Liu, Lina;  Zhang, Qin;  Tang, Zhengyang;  Hua, Xiaojun
收藏  |  浏览/下载：39/0  |  提交时间：2022/12/26
monthly runoff prediction  machine learning  copula entropy  stepwise regression  Upper Yangtze River  
Quantitative combination load forecasting model based on forecasting error optimization 期刊论文
COMPUTERS & ELECTRICAL ENGINEERING, 2022, 卷号: 101, 页码: 11
作者:  Deng, Song;  Chen, Fulin;  Wu, Di;  He, Yi;  Ge, Hui;  Ge, Yuan
收藏  |  浏览/下载：40/0  |  提交时间：2022/08/22
Gene expression programming  Data noise reduction  Load forecasting error  Combination load forecasting  
Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network 期刊论文
JOURNAL OF HYDROLOGY, 2022, 卷号: 605, 页码: 12
作者:  Shan, Kun;  Ouyang, Tian;  Wang, Xiaoxiao;  Yang, Hong;  Zhou, Botian;  Wu, Zhongxing;  Shang, Mingsheng
收藏  |  浏览/下载：49/0  |  提交时间：2022/08/22
Harmful algal bloom  Real-time monitoring  Long short-term memory network  Microcystin  Three Gorges Reservoir  
Abundance changes of marsh plant species over 40 years are better explained by niche position water level than functional traits 期刊论文
ECOLOGICAL INDICATORS, 2020, 卷号: 117, 页码: 7
作者:  Lou, Yanjing;  Kapfer, Jutta;  Smith, Pete;  Liu, Ying;  Jiang, Ming;  Lu, Xianguo;  Ashcroft, Michael
收藏  |  浏览/下载：78/0  |  提交时间：2020/10/12
Species abundance  Environmental change  Optimum water level  Leaf traits  Vegetation resurvey  Wetland  
Simultaneous measurements of urban and rural particles in Beijing - Part 2: Case studies of haze events and regional transport 期刊论文
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2020, 卷号: 20, 期号: 15, 页码: 9249-9263
作者:  Chen, Yang;  Shi, Guangming;  Cai, Jing;  Shi, Zongbo;  Wang, Zhichao;  Yao, Xiaojiang;  Tian, Mi;  Peng, Chao;  Han, Yiqun;  Zhu, Tong;  Liu, Yue;  Yang, Xi;  Zheng, Mei;  Yang, Fumo;  Zhang, Qiang;  He, Kebin
收藏  |  浏览/下载：155/0  |  提交时间：2020/10/12
Erosion vulnerability of sandy clay loam soil in Southwest China: Modeling soil detachment capacity by flume simulation 期刊论文
CATENA, 2019, 卷号: 178, 页码: 90-99
作者:  Li, Tianyang;  Li, Siyue;  Liang, Chuan;  He, Binghui;  Bush, Richard T.
Adobe PDF(564Kb)  |  收藏  |  浏览/下载：276/0  |  提交时间：2019/06/11
Rill erodibility  Purple soil  Rill flow  Hydraulic parameter  Land management