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Titlebook: Artificial Intelligence Oceanography; Xiaofeng Li,Fan Wang Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author(s) 2023 Open Acc

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發(fā)表于 2025-3-21 17:57:20 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence Oceanography
影響因子2023Xiaofeng Li,Fan Wang
視頻videohttp://file.papertrans.cn/163/162131/162131.mp4
發(fā)行地址This book is open access, which means that you have free and unlimited access.Includes abundant case studies to guide the readers on how to utilize AI in assisting oceanography research.Reveals multi-
圖書封面Titlebook: Artificial Intelligence Oceanography;  Xiaofeng Li,Fan Wang Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author(s) 2023 Open Acc
影響因子.This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.?.
Pindex Book‘‘‘‘‘‘‘‘ 2023
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沙發(fā)
發(fā)表于 2025-3-21 23:56:49 | 只看該作者
Forecasting Tropical Instability Waves Based on Artificial Intelligence,esearch fields inspire us to develop a deep neural network-based DL ocean forecasting model that is driven only by the time series of gridded sea surface temperature (SST) data. The model forecasted the SST pattern variations in the eastern equatorial Pacific Ocean, where a well-known prevailing oce
板凳
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地板
發(fā)表于 2025-3-22 04:48:02 | 只看該作者
,Satellite Data-Driven Internal Solitary Wave Forecast Based on?Machine Learning Techniques,emand yet with little progress in recent years. Numerical simulations and empirical methods are mainly used to forecast the propagation of ISWs but suffer from different issues, such as computational cost, time inefficiency, or low accuracy. Accumulating satellite imagery has provided a solid and fu
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發(fā)表于 2025-3-22 18:41:04 | 只看該作者
,Detecting Tropical Cyclogenesis Using Broad Learning System from?Satellite Passive Microwave Observe radiometer’s brightness temperature (TB) data, a tropical cyclogenesis prediction model is trained with the broad learning system (BLS). In contrast to the high computational demand of deep networks, BLS is a flatted one with fast training speed. Meanwhile, the incremental learning ability of BLS
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發(fā)表于 2025-3-23 00:46:14 | 只看該作者
Tropical Cyclone Monitoring Based on Geostationary Satellite Imagery,s and property. Forecasters and emergency responders both rely on accurate TC location and intensity estimates. In this chapter, two deep convolutional neural networks (CNNs) were designed for locating TC centers (CNN-L) and estimating their intensities (CNN-I) from the brightness temperature data o
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發(fā)表于 2025-3-23 06:52:49 | 只看該作者
Detection and Analysis of Mesoscale Eddies Based on Deep Learning,ddies have signals on sea surface height (SSH) images, sea surface temperature (SST) images. Previous studies developed automatic eddy identification methods based on SSH or SST. However, single remote sensing data cannot adequately characterize mesoscale eddies. To improve the accuracy and efficien
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