UPOTREBA I VIZUELIZACIJA VELIKIH PODATAKA OTVORENOG TIPA ZA ANALIZU POGODNOSTI STANIŠTA EVROPSKE BUKVE NA PODRUČJU SRBIJE
Ključne reči:
Neuronske mreže, pogodnost staništa, mašinsko učenje, veliki podaci, Fagus sylvatica
Apstrakt
Studije pogodnosti staništa dobijaju povećani značaj usled ubrzanog menjanja zivotne sredine i globalnog zagrevanja. U ovom radu je istrenirano 6 modela mašinskog učenja na području cele Evrope i analizirani su i upoređeni rezultati na području Srbije. Finalni rezultati su vizualizovani.
Reference
[1] Franklin, Janet. „Mapping species distributions: spatial inference and prediction“. Cambridge University Press, 2010.
[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).
[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).
Objavljeno
2020-08-02
Sekcija
Geodetsko inženjerstvo
