Introduction to Environmental Data Science

Statistical and machine learning methods have numerous applications in the environmental sciences, such as meteorology, hydrology, oceanography, remote sensing, agriculture, and climate change assessment. This book provides a comprehensive guide to machine learning and statistics for students and researchers, covering topics such as correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning. End-of-chapter exercises and online datasets allow readers to develop their problem-solving skills and apply analysis of real data.

Format: Hardback
Length: 500 pages
Publication date: 23 March 2023
Publisher: Cambridge University Press

The field of environmental sciences is greatly benefiting from the application of statistical and machine learning methods. These methods encompass a wide range of applications, including meteorology, hydrology, oceanography, remote sensing, agriculture, forestry, climate change assessment, and many more. In recent years, with the rapid advancements in machine learning, there has been a pressing need for a comprehensive guide to these techniques for students and researchers interested in environmental data science.

This book fills that gap by providing a comprehensive and up-to-date introduction to machine learning and statistics. It begins with intuitive explanations of the relevant background mathematics, accompanied by examples drawn from the environmental sciences. The book covers a broad range of topics, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms, and deep learning. It also delves into the recent merging of machine learning and physics, offering insights into how these two fields can be combined to address complex environmental challenges.

To enhance the learning experience, the book includes end-of-chapter exercises that allow readers to develop their problem-solving skills. Additionally, online datasets are provided, allowing readers to practice the analysis of real environmental data.

By leveraging the power of statistical and machine learning methods, environmental scientists can gain valuable insights into complex environmental systems, improve predictions, and develop more effective management strategies. This book serves as a valuable resource for students and researchers looking to advance their knowledge in this field.

Weight: 1316g
Dimension: 177 x 251 x 37 (mm)
ISBN-13: 9781107065550