Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels (Hardcover)

William W. Hsieh

  • 出版商: Cambridge
  • 出版日期: 2009-07-30
  • 定價: $3,150
  • 售價: 9.0$2,835
  • 語言: 英文
  • 頁數: 364
  • 裝訂: Hardcover
  • ISBN: 0521791928
  • ISBN-13: 9780521791922
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

Machine learning methods originated from artificial intelligence and are now used in various fields in environmental sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the environmental sciences. Due to their powerful nonlinear modeling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modeling of environmental data, oceanographic and hydrological forecasting, ecological modeling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing web sites for downloading computer code and data sources. A resources website containing datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work.
Preface Excerpt
Machine learning is a major subfield in computational intelligence (also called artificial intelligence). Its main objective is to use computational methods to extract information from data. Neural network methods, generally regarded as forming the first wave of breakthrough in machine learning, became popular in the late 1980s, while kernel methods arrived in a second wave in the second half of the 1990s. This is the first single-authored textbook to give a unified treatment of machine learning methods and their applications in the environmental sciences.

Machine learning methods began to infiltrate the environmental sciences in the 1990s. Today, thanks to their powerful nonlinear modeling capability, they are no longer an exotic fringe species, as they are heavily used in satellite data processing, in general circulation models (GCM), in weather and climate prediction, air quality forecasting, analysis and modeling of environmental data, oceanographic and hydrological forecasting, ecological modeling, and in the monitoring of snow, ice and forests, etc.

This book presents machine learning methods and their applications in the environmental sciences (including satellite remote sensing, atmospheric science, climate science, oceanography, hydrology and ecology), written at a level suitable for beginning graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in environmental sciences interested in applying these new methods to their own work.

Chapters 1-3, intended mainly as background material for students, cover the standard statistical methods used in environmental sciences. The machine learning methods of chapters 4-12 provide powerful nonlinear generalizations for many of these standard linear statistical methods. End-of-chapter review questions are included, allowing readers to develop their problem-solving skills and monitor their understanding of the material presented. An appendix lists websites available for downloading computer code and data sources. A resources website is available containing datasets for exercises, and additional material to keep the book completely up-to-date.

About the Author
WILLIAM W. HSIEH is a Professor in the Department of Earth and Ocean Sciences and in the Department of Physics and Astronomy, as well as Chair of the Atmospheric Science Programme, at the University of British Columbia. He is internationally known for his pioneering work in developing and applying machine learning methods in environmental sciences. He has published over 80 peer-reviewed journal publications covering areas of climate variability, machine learning, oceanography, atmospheric science and hydrology.

商品描述(中文翻譯)

機器學習方法源自人工智慧,如今在環境科學的各個領域中被廣泛應用。這是第一本由單一作者撰寫的教科書,提供了機器學習方法及其在環境科學中的應用的統一介紹。由於其強大的非線性建模能力,機器學習方法如今被用於衛星數據處理、大氣循環模型(GCM)、天氣和氣候預測、空氣質量預報、環境數據分析和建模、海洋和水文預報、生態建模以及雪、冰和森林的監測。本書包含章末的復習問題和附錄列出的下載電腦代碼和數據來源的網站。還提供了包含練習數據集和受密碼保護的解答的資源網站。本書適合研究生一年級和高年級本科生閱讀,同時也適用於環境科學研究人員和從事相關工作的從業人員,他們有興趣將這些新方法應用於自己的工作中。

前言摘錄:
機器學習是計算智能(也稱為人工智慧)的一個重要子領域,其主要目標是利用計算方法從數據中提取信息。神經網絡方法通常被認為是機器學習的第一波突破,於1980年代末開始流行,而核方法則在1990年代下半葉形成第二波浪潮。這是第一本由單一作者撰寫的教科書,統一介紹了機器學習方法及其在環境科學中的應用。

機器學習方法在1990年代開始進入環境科學領域。如今,由於其強大的非線性建模能力,它們已不再是一種異乎尋常的邊緣物種,而是在衛星數據處理、大氣循環模型(GCM)、天氣和氣候預測、空氣質量預報、環境數據分析和建模、海洋和水文預報、生態建模以及雪、冰和森林的監測等領域中得到廣泛應用。

本書介紹了機器學習方法及其在環境科學中的應用(包括衛星遙感、大氣科學、氣候科學、海洋學、水文學和生態學),適合初級研究生和高年級本科生閱讀。同時,對於對將這些新方法應用於自己工作中的環境科學研究人員和從業人員也具有價值。

第1至3章主要作為學生的背景材料,介紹了環境科學中常用的標準統計方法。第4至12章的機器學習方法為這些標準線性統計方法提供了強大的非線性推廣。章末的復習問題可幫助讀者培養解決問題的能力,並監測對所學內容的理解。附錄列出了可下載電腦代碼和數據來源的網站。還提供了包含練習數據集和保持書籍完全更新的其他材料的資源網站。

關於作者:
威廉·W·謝(William W. Hsieh)是英屬哥倫比亞大學地球與海洋科學系和物理與天文學系的教授,也是大氣科學計劃的主席。他以在環境科學中開發和應用機器學習方法的開創性工作而享譽國際。他發表了80多篇經同行評審的期刊論文,涵蓋氣候變異性、機器學習、海洋學、大氣科學和水文學等領域。