IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i17p9507-d620769.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/17/9507/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/17/9507/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seungbum Hong & Inyoung Jang & Daegeun Kim & Suhwan Kim & Hyun Su Park & Kyungeun Lee, 2022. "Predicting Potential Habitat Changes of Two Invasive Alien Fish Species with Climate Change at a Regional Scale," Sustainability, MDPI, vol. 14(10), pages 1-12, May.
    2. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    3. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    6. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    7. Banks, Jonathan & Rabbani, Arif & Nadkarni, Kabir & Renaud, Evan, 2020. "Estimating parasitic loads related to brine production from a hot sedimentary aquifer geothermal project: A case study from the Clarke Lake gas field, British Columbia," Renewable Energy, Elsevier, vol. 153(C), pages 539-552.
    8. Guo, Chuanbo & Chen, Yushun & Liu, Han & Lu, Yin & Qu, Xiao & Yuan, Hui & Lek, Sovan & Xie, Songguang, 2019. "Modelling fish communities in relation to water quality in the impounded lakes of China’s South-to-North Water Diversion Project," Ecological Modelling, Elsevier, vol. 397(C), pages 25-35.
    9. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    10. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    11. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    12. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    14. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    15. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    16. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    17. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    18. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    19. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    20. Francesco Sartor & Jonathan P. Moore & Hans-Peter Kubis, 2021. "Plasma Interleukin-10 and Cholesterol Levels May Inform about Interdependences between Fitness and Fatness in Healthy Individuals," IJERPH, MDPI, vol. 18(4), pages 1-19, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9507-:d:620769. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.