Large-Scale Machine Learning in the Earth Sciences
暫譯: 地球科學中的大規模機器學習
Srivastava, Ashok N., Nemani, Ramakrishna, Steinhaeuser, Karsten
- 出版商: CRC
- 出版日期: 2020-06-30
- 售價: $2,340
- 貴賓價: 9.5 折 $2,223
- 語言: 英文
- 頁數: 238
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0367573237
- ISBN-13: 9780367573232
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
From the Foreword:
"While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok
Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences."
--Vipin Kumar, University of Minnesota
Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science.
Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored.
The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth.
The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.
商品描述(中文翻譯)
從前言：
「雖然大規模機器學習和資料挖掘對多種商業應用產生了重大影響，但它們在地球科學領域的應用仍處於早期階段。本書由 Ashok Srivastava、Ramakrishna Nemani 和 Karsten Steinhaeuser 編輯，是任何對機器學習社群在分析這些資料集以回答社會緊迫問題的機會和挑戰感興趣的人的優秀資源……我希望這本書能激勵更多的計算機科學家專注於環境應用，並促使地球科學家尋求與機器學習和資料挖掘研究人員的合作，以推進地球科學的前沿。」
--Vipin Kumar，明尼蘇達大學
《大規模機器學習在地球科學中的應用》為研究人員和實務工作者提供了地球科學、計算機科學、統計學及相關領域交集中的一些關鍵挑戰的廣泛概述。它探討了多種主題，並提供了機器學習在地球科學領域應用的最新研究彙編。
根據觀測數據進行預測是本書的一個主題，書中包括了使用網絡科學來理解和發現極端氣候和天氣事件中的遠程聯繫的章節，以及在高維度中使用結構估計的章節。書中還探討了使用集成機器學習模型來結合全球氣候模型的預測，並利用空間和時間模式的信息。
本書的第二部分討論了氣候中的統計降尺度，採用最先進的可擴展機器學習，並概述了理解和預測由於環境條件變化而導致的生物物種擴散的方法。書中還深入探討了使用大規模機器學習研究龍捲風形成的問題。
本書的最後一部分涵蓋了使用深度學習算法對具有非常高解析度的影像進行分類，以及在土地覆蓋的遙感影像中進行光譜信號的解混。作者在書的最後一章中還將長尾分佈應用於地球科學資源。
作者簡介
Ashok N. Srivastava, Ph.D. is the VP of Data and Artificial Intelligence Systems and the Chief Data Scientist at Verizon. He leads a new research and development center in Palo Alto focusing on building products and technologies powered by big data, large-scale machine learning, and analytics. He is an Adjunct Professor at Stanford University in the Electrical Engineering Department and is the Editor-in-Chief of the AIAA Journal of Aerospace Information Systems. Dr. Srivastava is a Fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA).
He is the author of over 100 research articles, has edited 4 books, has 5 patents awarded, and over 30 under file. He has won numerous awards including the IEEE Computer Society Technical Achievement Award for "pioneering contributions to intelligent information systems," the NASA Exceptional Achievement Medal for contributions to state-of-the-art data mining and analysis, the NASA Honor Award for Outstanding Leadership, the NASA Distinguished Performance Award, several NASA Group Achievement Awards, the Distinguished Engineering Alumni Award from UC Boulder, the IBM Golden Circle Award, and the Department of Education Merit Fellowship.
Dr. Ramakrishna Nemani is a senior Earth scientist with the NASA Advanced Supercomputing division at Ames Research Center, California, USA. He leads NASA's efforts in ecological forecasting to understand the impacts of the impending climatic changes on Earth's ecosystems and in collaborative computing, bringing scientists together with big data and supercomputing to provide insights into how our planet is changing and the forces underlying such changes.
He has published over 190 papers on a variety of topics including remote sensing, global ecology, ecological forecasting, climatology and scientific computing with over 28000 citations. He served on the science teams of several missions including Landsat-8, NPP, EOS/MODIS, ALOS-2 and GCOM-C. He has received numerous awards from NASA including the exceptional scientific achievement medal in 2008, exceptional achievement medal in 2011, outstanding leadership medal in 2012 and eight group achievement awards.
Karsten Steinhaeuser, Ph.D. is a Research Scientist affiliated with the Department of Computer Science & Engineering at the University of Minnesota and a Data Scientist with Progeny Systems Corporation. His research centers around data mining and machine learning, in particular construction and analysis of complex networks, with applications in diverse domains including climate, ecology, social networks, time series analysis, and computer vision. He is actively involved in shaping an emerging research area called climate informatics, which lies at the intersection of computer science and climate sciences, and his interests are more generally in interdisciplinary research and scientific problems relating to climate and sustainability.
Dr. Steinhaeuser has been awarded one patent and has authored several book chapters as well as numerous peer reviewed articles and papers on these topics. His work has been recognized with multiple awards including two Oak Ridge National Laboratory Significant Event Awards for "Novel Analyses of the Simulation Results from the CCSM 3.0 Climate Model" and "Science Support for a Climate Change War Game and Follow-Up Support to the US Department of Defense."
作者簡介(中文翻譯)
Ashok N. Srivastava，博士，是Verizon的數據與人工智慧系統副總裁及首席數據科學家。他領導位於帕洛阿爾托的新研究與開發中心，專注於構建由大數據、大規模機器學習和分析驅動的產品與技術。他是史丹佛大學電機工程系的兼任教授，也是AIAA航空資訊系統期刊的主編。Srivastava博士是IEEE、美國科學促進會（AAAS）及美國航空航天學會（AIAA）的會士。
他是超過100篇研究文章的作者，編輯了4本書籍，擁有5項專利，並有超過30項專利正在申請中。他獲得了多項獎項，包括IEEE計算機學會技術成就獎，以表彰他在智能資訊系統方面的開創性貢獻，NASA卓越成就獎以表彰他在尖端數據挖掘和分析方面的貢獻，NASA傑出領導獎，NASA傑出表現獎，數個NASA團隊成就獎，科羅拉多大學博爾德分校的傑出工程校友獎，IBM金圈獎，以及教育部優異獎學金。
Dr. Ramakrishna Nemani 是美國NASA艾姆斯研究中心高級超級計算部門的資深地球科學家。他領導NASA在生態預測方面的努力，以了解即將到來的氣候變化對地球生態系統的影響，並在協作計算中，將科學家與大數據和超級計算結合起來，以提供有關我們星球變化及其背後力量的見解。
他在遙感、全球生態學、生態預測、氣候學和科學計算等多個主題上發表了超過190篇論文，引用次數超過28000次。他曾參與多個任務的科學團隊，包括Landsat-8、NPP、EOS/MODIS、ALOS-2和GCOM-C。他獲得了NASA的多項獎項，包括2008年的卓越科學成就獎、2011年的卓越成就獎、2012年的傑出領導獎以及八項團隊成就獎。
Karsten Steinhaeuser，博士，是明尼蘇達大學計算機科學與工程系的研究科學家，並且是Progeny Systems Corporation的數據科學家。他的研究集中在數據挖掘和機器學習，特別是複雜網絡的構建和分析，應用於氣候、生態、社交網絡、時間序列分析和計算機視覺等多個領域。他積極參與塑造一個新興的研究領域，稱為氣候資訊學，該領域位於計算機科學和氣候科學的交集上，他的興趣更廣泛地涉及與氣候和可持續性相關的跨學科研究和科學問題。
Steinhaeuser博士獲得了一項專利，並撰寫了幾個書籍章節以及多篇經過同行評審的文章和論文。他的工作獲得了多項獎項，包括兩項奧克里奇國家實驗室的重大事件獎，以表彰他對“CCSM 3.0氣候模型模擬結果的新穎分析”和“對氣候變化戰爭遊戲的科學支持及對美國國防部的後續支持”。
