USE AND VIZUALIZATION OF BIG OPEN SOURCE DATA FOR ANALYSIS OF HABITAT SUITABILITY OF EUROPEAN BEECH IN SERBIA
DOI:
https://doi.org/10.24867/08KG02BekerKeywords:
Neural networks, Habitat suitability, Machine learning, Big Data, Fagus sylvaticaAbstract
Habitat suitability studies are becoming ever more valuable because of faster changing of environment and global warming. In this paper six machine learning models are trained on area of whole Europe and results are analyzed and compared on the area of Serbia. Final results are visualized.
References
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[2] Teo Beker, Master Thesis:“Big Data and machine learning for global evaluation of habitat suitability of European forest species”, Milano, Politecnico di Milano, 2019.
[3] Wolpert, David H., and William G. Macready. "No free lunch theorems for optimization." IEEE transactions on evolutionary computation 1.1 (1997): 67-82.
[4] Takaku, Junichi, Takeo Tadono, and Ken Tsutsui. "GENERATION OF HIGH RESOLUTION GLOBAL DSM FROM ALOS PRISM." ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 2.4 (2014).
[5] Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
[6] Bengio, Yoshua. "Practical recommendations for gradient-based training of deep architectures." Neural networks: Tricks of the trade. Springer, Berlin, Heidelberg, 2012. 437-478.
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[8] McKinney, Wes. "pandas: a foundational Python library for data analysis and statistics." Python for High Performance and Scientific Computing 14 (2011).
[2] Teo Beker, Master Thesis:“Big Data and machine learning for global evaluation of habitat suitability of European forest species”, Milano, Politecnico di Milano, 2019.
[3] Wolpert, David H., and William G. Macready. "No free lunch theorems for optimization." IEEE transactions on evolutionary computation 1.1 (1997): 67-82.
[4] Takaku, Junichi, Takeo Tadono, and Ken Tsutsui. "GENERATION OF HIGH RESOLUTION GLOBAL DSM FROM ALOS PRISM." ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 2.4 (2014).
[5] Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
[6] Bengio, Yoshua. "Practical recommendations for gradient-based training of deep architectures." Neural networks: Tricks of the trade. Springer, Berlin, Heidelberg, 2012. 437-478.
[7] GDAL/OGR contributors. "GDAL/OGR geospatial data abstraction software library." Open Source Geospatial Foundation (2018).
[8] McKinney, Wes. "pandas: a foundational Python library for data analysis and statistics." Python for High Performance and Scientific Computing 14 (2011).
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Published
2020-08-02
Issue
Section
Geodesy Engineering
