Machine Learning for Ecology and Sustainable Natural Resource Management

• Corporate Author: SpringerLink (Online service)
• Other Authors: Humphries, Grant (Editor, http://id.loc.gov/vocabulary/relators/edt), Magness, Dawn R. (Editor, http://id.loc.gov/vocabulary/relators/edt), Huettmann, Falk (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Subjects: Ecology .; Bioinformatics .; Computational biology .; Biostatistics.; Data mining.; Pattern recognition.; Ecology.; Computer Appl. in Life Sciences.; Data Mining and Knowledge Discovery.; Pattern Recognition.

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology,...

Table of Contents:

• 1: Introduction to Machine Learning A. Data-intensive science B. Data Issues and Availability
• 2: Data-mining in Ecological and Wildlife Research A. Multiple Methods in the Scientific Process B. Data-mining in Ecological and Wildlife Research C. Applications in Ecological Research a. Predicting Patterns in Space and Time b. Data Exploration and Hypothesis Generation c. Pattern Recognition for Sampling D. Bringing It All Together: Leveraging Multiple Methods to Increase Knowledge for Resource Management
• 3: Machine Learning and Resource Management A. Web-based Machine Learning Applications for Wildlife Management B. Linking Machine Learning in Management Applications C. Machine Learning and the Cloud for Natural Resource Applications D. The Global View: Hopes and Disappointments E. The Future of Machine Learning.