Tür Dağılım Modellemesinde Makine Öğrenme Yöntemleri

İlksen Çelikoğlu

doi:10.53463/ecopers.20240253

Authors

İlksen Çelikoğlu Süleyman Demirel University, Institute of Applied and Natural Sciences, Department of Landscape Architecture, ISPARTA/Turkey https://orcid.org/0009-0001-3110-7201

DOI:

https://doi.org/10.53463/ecopers.20240253

Keywords:

Climate Change, Species Distribution Modeling, Global Warming, Machine Learning

Abstract

Climate change caused by global warming is one of the universal problems that is important for human life with its long-term consequences. It is predicted by studies that climate change will have a negative impact on the distribution of species. In these studies, point data showing the areas where the species are found and bioclimatic data of the areas are taken into consideration and the current and future potential distribution areas of the species are determined with different species distribution models according to climate scenarios. Different modeling tools are used to determine the effects of climate change, one of the global problems, on the distribution of species and to predict the possible distribution with different scenarios. The machine learning technique, which is frequently used in modeling the current and potential future distribution areas of species, has become widespread and has become one of the important fields of study today. As a result of studies using machine learning methods, it is envisaged that future uses of species can be planned, biological diversity can be analyzed and necessary conservation measures can be taken. In this study, machine learning methods frequently used in species distribution modeling were examined.

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