Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machine Learning: An Example from Southern Alaska Shows Topographical Biases and Strong Differences-Reference-Cited by-同舟云学术

Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machine Learning: An Example from Southern Alaska Shows Topographical Biases and Strong Differences

Published:2018 Issue: Volume: Page:227-241
ISSN:
Container-title:Machine Learning for Ecology and Sustainable Natural Resource Management
language:
Short-container-title:

Author:

Huettmann Falk

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-96978-7_11

Reference32 articles.

1. Bari A, Chaouchi M, Jung T (2014) Predictive analytics for dummies. Wiley, New York

2. Betts MG, Ganio L, Huso M, Som N, Huettmann F, Bowman J, Wintle WA (2009) Comment on “Methods to account for spatial autocorrelation in the analysis of species distributional data: a review”. Ecography 32:374–378

3. Bieniek PA, Bhatt US, Walsh JE, Rupp TS, Zhang J, Krieger JR, Lader R (2015) Dynamical downscaling of ERA-interim temperature and precipitation for Alaska. JAMC https://doi.org/10.1175/JAMC-D-15-0153.1

4. Breiman L (2001) Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci 16:199–231

5. Chernetsov N, Huettmann F (2005) Linking global climate grid surfaces with local long-term migration monitoring data: spatial computations for the pied flycatcher to assess climate-related population dynamics on a continental scale.Lecture notes in computer science (LNCS) 3482, International Conference on Computational Science and its Applications (ICCSA) Proceedings Part III: 133–142

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