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DOI: 10.14569/IJACSA.2013.040201
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Machine Learning for Bioclimatic Modelling

Author 1: Maumita Bhattacharya

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 4 Issue 2, 2013.

  • Abstract and Keywords
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Abstract: Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the impact of climate change. However, success of machine learning-based approaches depends on a number of factors. While it can be safely said that no particular ML technique can be effective in all applications and success of a technique is predominantly dependent on the application or the type of the problem, it is useful to understand their behavior to ensure informed choice of techniques. This paper presents a comprehensive review of machine learning-based bioclimatic model generation and analyses the factors influencing success of such models. Considering the wide use of statistical techniques, in our discussion we also include conventional statistical techniques used in bioclimatic modelling.

Keywords: Machine Learning; Bioclimatic Modelling; Geographic Range; Artificial Neural Network; Evolutionary Algorithm

Maumita Bhattacharya, “Machine Learning for Bioclimatic Modelling” International Journal of Advanced Computer Science and Applications(IJACSA), 4(2), 2013. http://dx.doi.org/10.14569/IJACSA.2013.040201

@article{Bhattacharya2013,
title = {Machine Learning for Bioclimatic Modelling},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2013.040201},
url = {http://dx.doi.org/10.14569/IJACSA.2013.040201},
year = {2013},
publisher = {The Science and Information Organization},
volume = {4},
number = {2},
author = {Maumita Bhattacharya}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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