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A Review of Application of Machine Learning in Storm Surge Problems 期刊论文
JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2023, 卷号: 11, 期号: 9, 页码: 35
作者:  Qin, Yue;  Su, Changyu;  Chu, Dongdong;  Zhang, Jicai;  Song, Jinbao
收藏  |  浏览/下载：47/0  |  提交时间：2023/11/15
storm surge prediction  machine learning  hybrid methods  physics-informed neural networks  
A comprehensive model of vessel anchoring pressure based on machine learning to support the sustainable management of the marine environments of coastal cities 期刊论文
SUSTAINABLE CITIES AND SOCIETY, 2021, 卷号: 72, 页码: 10
作者:  Liu, Baijing;  Gong, Meng;  Wu, Xiaoqing;  Liu, Xin
浏览  |  Adobe PDF(7810Kb)  |  收藏  |  浏览/下载：617/231  |  提交时间：2021/10/21
Vessel anchoring pressure  Automatic identification system  Machine learning  Illegal anchoring area  Sustainable marine management  
Advances in Water Quality Monitoring and Assessment in Marine and Coastal Regions 期刊论文
WATER, 2021, 卷号: 13, 期号: 14, 页码: 4
作者:  Bergamasco, Alessandro;  Nguyen, Hong Quan;  Caruso, Gabriella;  Xing, Qianguo;  Carol, Eleonora
浏览  |  Adobe PDF(198Kb)  |  收藏  |  浏览/下载：493/216  |  提交时间：2021/10/21
A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation 期刊论文
BIOSYSTEMS ENGINEERING, 2021, 卷号: 206, 页码: 94-108
作者:  Zhang, Liankuan;  Xia, Chunlei;  Xiao, Deqin;  Weckler, Paul;  Lan, Yubin;  Lee, Jang M.
浏览  |  Adobe PDF(3215Kb)  |  收藏  |  浏览/下载：705/241  |  提交时间：2021/06/22
Leaf extraction  Leaf pose measurement  Leaf shape estimation  Occlusion detection  Active shape model  Plant image analysis  
Are oil spills enhancing outbreaks of red tides in the Chinese coastal waters from 1973 to 2017? 期刊论文
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2021, 页码: 7
作者:  Liu, Xin;  Zhang, Chunchang;  Geng, Ruiying;  Lv, Xin
浏览  |  Adobe PDF(370Kb)  |  收藏  |  浏览/下载：574/209  |  提交时间：2021/10/20
Red tide  Oil spill  Correlation  Time series  Regression model  Shuffled surrogates  
Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters 期刊论文
WATER, 2021, 卷号: 13, 期号: 5, 页码: 13
作者:  Jiang, Bo;  Liu, Hailong;  Xing, Qianguo;  Cai, Jiannan;  Zheng, Xiangyang;  Li, Lin;  Liu, Sisi;  Zheng, Zhiming;  Xu, Huiyan;  Meng, Ling
浏览  |  Adobe PDF(3145Kb)  |  收藏  |  浏览/下载：626/188  |  提交时间：2021/06/07
in situ reflectance  retrieval models  chlorophyll-a  total suspended particulate  eutrophic and turbid water  the Pearl River Delta  
Relating anomalous large-scale atmospheric circulation patterns to temperature and precipitation anomalies in the East Asian monsoon region 期刊论文
ATMOSPHERIC RESEARCH, 2020, 卷号: 232, 页码: UNSP 104679
作者:  Yang, Y;  Gao, M;  Xie, NR;  Gao, ZQ
浏览  |  Adobe PDF(9761Kb)  |  收藏  |  浏览/下载：597/267  |  提交时间：2020/07/08
Atmospheric circulation patterns  Self-organizing map  Temperature and precipitation anomalies  East Asian Monsoon  
Data Fusion and Accuracy Analysis of Multi-Source Land Use/Land Cover Datasets along Coastal Areas of the Maritime Silk Road 期刊论文
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 卷号: 8, 期号: 12, 页码: 20
作者:  Hou, Wan;  Hou, Xiyong
浏览  |  Adobe PDF(1916Kb)  |  收藏  |  浏览/下载：457/167  |  提交时间：2021/06/22
remote sensing  land use  land cover  data fusion  agreement analysis  fuzzy-set theory  accuracy analysis  the coastal areas of the Maritime Silk Road  
The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review 期刊论文
WATER, 2019, 卷号: 11, 期号: 8, 页码: 1615
作者:  Jiang, DJ;  Wang, K
浏览  |  Adobe PDF(338Kb)  |  收藏  |  浏览/下载：448/189  |  提交时间：2020/07/08
satellite-based remote sensing  streamflow simulation  hydrological model  data assimilation  
Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China 期刊论文
EARTH SCIENCE INFORMATICS, 2018, 卷号: 11, 期号: 4, 页码: 553-566
作者:  Zhang, Nannan;  Zhou, Kefa;  Li, Dong
浏览  |  Adobe PDF(5138Kb)  |  收藏  |  浏览/下载：449/228  |  提交时间：2020/07/08
Mineral prospectivity mapping  Back propagation neural network  Support vector machines  Weight of evidence