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Evaluation of Classification Algorithms to Predict Largemouth Bass ( Micropterus salmoides ) Occurrence

Author

Listed:
  • Zhonghyun Kim

    (Division of Environmental Science & Ecological Engineering, Korea University, Seoul 02841, Korea)

  • Taeyong Shim

    (Division of Environmental Science & Ecological Engineering, Korea University, Seoul 02841, Korea)

  • Seo Jin Ki

    (Department of Environmental Engineering, Gyeongsang National University, Jinju 52725, Korea)

  • Dongil Seo

    (Department of Environmental Engineering, Chungnam National University, Daejeon 34134, Korea)

  • Kwang-Guk An

    (Department of Bioscience and Biotechnology, Chungnam National University, Daejeon 34134, Korea)

  • Jinho Jung

    (Division of Environmental Science & Ecological Engineering, Korea University, Seoul 02841, Korea)

Abstract

This study aimed to evaluate classification algorithms to predict largemouth bass ( Micropterus salmoides ) occurrence in South Korea. Fish monitoring and environmental data (temperature, precipitation, flow rate, water quality, elevation, and slope) were collected from 581 locations throughout four major river basins for 5 years (2011–2015). Initially, 13 classification models built in the caret package were evaluated for predicting largemouth bass occurrence. Based on the accuracy (>0.8) and kappa (>0.5) criteria, the top three classification algorithms (i.e., random forest (rf), C5.0, and conditional inference random forest) were selected to develop ensemble models. However, combining the best individual models did not work better than the best individual model (rf) at predicting the frequency of largemouth bass occurrence. Additionally, annual mean temperature (12.1 °C) and fall mean temperature (13.6 °C) were the most important environmental variables to discriminate the presence and absence of largemouth bass. The evaluation process proposed in this study will be useful to select a prediction model for the prediction of freshwater fish occurrence but will require further study to ensure ecological reliability.

Suggested Citation

  • Zhonghyun Kim & Taeyong Shim & Seo Jin Ki & Dongil Seo & Kwang-Guk An & Jinho Jung, 2021. "Evaluation of Classification Algorithms to Predict Largemouth Bass ( Micropterus salmoides ) Occurrence," Sustainability, MDPI, vol. 13(17), pages 1-11, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9507-:d:620769
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Guo, Chuanbo & Lek, Sovan & Ye, Shaowen & Li, Wei & Liu, Jiashou & Li, Zhongjie, 2015. "Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques," Ecological Modelling, Elsevier, vol. 306(C), pages 67-75.
    3. Kazi Ahmed & Guiling Wang & Miao Yu & Jawoo Koo & Liangzhi You, 2015. "Potential impact of climate change on cereal crop yield in West Africa," Climatic Change, Springer, vol. 133(2), pages 321-334, November.
    4. Zhonghyun Kim & Taeyong Shim & Young-Min Koo & Dongil Seo & Young-Oh Kim & Soon-Jin Hwang & Jinho Jung, 2020. "Predicting the Impact of Climate Change on Freshwater Fish Distribution by Incorporating Water Flow Rate and Quality Variables," Sustainability, MDPI, vol. 12(23), pages 1-15, November.
    5. Kärcher, Oskar & Frank, Karin & Walz, Ariane & Markovic, Danijela, 2019. "Scale effects on the performance of niche-based models of freshwater fish distributions," Ecological Modelling, Elsevier, vol. 405(C), pages 33-42.
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