A robust prediction model for species distribution using bagging ensembles with deep neural networks

Jehyeok Rew, Yongjang Cho, Eenjun Hwang

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

Original languageEnglish
Article number1495
JournalRemote Sensing
Issue number8
Publication statusPublished - 2021 Apr 2

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Bootstrap
  • Deep neural network
  • Ensemble model
  • Species distribution model

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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