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Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering pp 413–421Cite as

Bioclimatic Modelling: A Machine Learning Perspective

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 313))

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 behaviour 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.

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  1. School of Computing and Mathematics, Charles Sturt University, Albury, Australia

    Maumita Bhattacharya

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  1. Engineering and Computer Science, University of Bridgeport, Bridgeport, Connecticut, USA

    Tarek Sobh

  2. Computer Science and Engineering, University of Bridgeport, Bridgeport, Connecticut, USA

    Khaled Elleithy

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Bhattacharya, M. (2015). Bioclimatic Modelling: A Machine Learning Perspective. In: Sobh, T., Elleithy, K. (eds) Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering. Lecture Notes in Electrical Engineering, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-06773-5_56

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