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  • P-ISSN2765-2203
  • E-ISSN2765-2211

Application of Statistical and Machine Learning Techniques for Habitat Potential Mapping of Siberian Roe Deer in South Korea

Proceedings of the National Institute of Ecology of the Republic of Korea, (P)2765-2203; (E)2765-2211
2021, v.2 no.1, pp.1-14
https://doi.org/10.22920/PNIE.2021.2.1.1
Saro Lee (Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM))
Fatemeh Rezaie (Department of Geophysical Exploration, Korea University of Science and Technology)
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Abstract

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer’s habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

keywords
Convolutional neural network algorithm Frequency ratio method Habitat potential map Long short-term memory algorithm Machine learning algorithms Siberian roe deer
Submission Date
2020-12-28
Revised Date
2021-01-13
Accepted Date
2021-01-13

Proceedings of the National Institute of Ecology of the Republic of Korea