IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v251y2013icp44-53.html
   My bibliography  Save this article

Application of genetic algorithm and greedy stepwise to select input variables in classification tree models for the prediction of habitat requirements of Azolla filiculoides (Lam.) in Anzali wetland, Iran

Author

Listed:
  • Sadeghi, Roghayeh
  • Zarkami, Rahmat
  • Sabetraftar, Karim
  • Van Damme, Patrick

Abstract

The aim of the present study was to predict to what extent wetland characteristics can affect the habitat requirements of an exotic species, Azolla filiculoides (Lam.) in wetland. Biotic and abiotic variables were collected at the Selkeh wildlife refuge (a protected area in Anzali wetland, northern Iran) over the study period 2007–2008. Classification tree (CT) was used to find the relationship between the wetland characteristics and the cover percentage of A. filiculoides. Genetic algorithm (GA) and greedy stepwise (GS) were combined with CT in order to select the most important variables to explain the coverage of A. filiculoides. The applied method was assessed based on the percentage of correctly classified instances (CCI) and Cohen's kappa statistics (k). Different pruning confidence factors (PCFs) were tested in order to improve the predictive results regarding the complexity and accuracy of the prediction. The results showed that the prediction was reliable in terms of both performance criteria. Yet, after variable selection, the predictive performances of the CT improved. Due to potential collinear variables in the model, the GS method was less efficient than GA. The optimization of GA and GS resulted in an easy interpretation of the selected variables. The methods showed that both structural habitat (e.g. air temperature, humidity and depth) and physico-chemical variables (e.g. nutrients) can affect the habitat requirements of A. filiculoides in the wetland but the dependence of this aquatic fern on structural habitat was well confirmed by the CT before and after variable selection. Application of the given algorithms in combination with CT thus proved to have a better capability in selecting the most important variables explaining the cover of A. filiculoides and can be used by wetland managers in their decision-making for wetland conservation and management programs.

Suggested Citation

  • Sadeghi, Roghayeh & Zarkami, Rahmat & Sabetraftar, Karim & Van Damme, Patrick, 2013. "Application of genetic algorithm and greedy stepwise to select input variables in classification tree models for the prediction of habitat requirements of Azolla filiculoides (Lam.) in Anzali wetland,," Ecological Modelling, Elsevier, vol. 251(C), pages 44-53.
    • Handle: RePEc:eee:ecomod:v:251:y:2013:i:c:p:44-53
    DOI: 10.1016/j.ecolmodel.2012.12.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380012005765
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2012.12.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sadeghi, Roghayeh & Zarkami, Rahmat & Sabetraftar, Karim & Van Damme, Patrick, 2012. "Application of classification trees to model the distribution pattern of a new exotic species Azolla filiculoides (Lam.) at Selkeh Wildlife Refuge, Anzali wetland, Iran," Ecological Modelling, Elsevier, vol. 243(C), pages 8-17.
    2. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    3. Sadeghi, Roghayeh & Zarkami, Rahmat & Sabetraftar, Karim & Van Damme, Patrick, 2012. "Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran," Ecological Modelling, Elsevier, vol. 244(C), pages 117-126.
    4. Zarkami, Rahmat & Sadeghi, Roghayeh & Goethals, Peter, 2012. "Use of fish distribution modelling for river management," Ecological Modelling, Elsevier, vol. 230(C), pages 44-49.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Argaw Ambelu & Seblework Mekonen & Magaly Koch & Taffere Addis & Pieter Boets & Gert Everaert & Peter Goethals, 2014. "The Application of Predictive Modelling for Determining Bio-Environmental Factors Affecting the Distribution of Blackflies (Diptera: Simuliidae) in the Gilgel Gibe Watershed in Southwest Ethiopia," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-10, November.
    2. Sadeghi, Roghayeh & Zarkami, Rahmat & Van Damme, Patrick, 2014. "Modelling habitat preference of an alien aquatic fern, Azolla filiculoides (Lam.), in Anzali wetland (Iran) using data-driven methods," Ecological Modelling, Elsevier, vol. 284(C), pages 1-9.
    3. Gobeyn, Sacha & Mouton, Ans M. & Cord, Anna F. & Kaim, Andrea & Volk, Martin & Goethals, Peter L.M., 2019. "Evolutionary algorithms for species distribution modelling: A review in the context of machine learning," Ecological Modelling, Elsevier, vol. 392(C), pages 179-195.

    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. Sadeghi, Roghayeh & Zarkami, Rahmat & Van Damme, Patrick, 2014. "Modelling habitat preference of an alien aquatic fern, Azolla filiculoides (Lam.), in Anzali wetland (Iran) using data-driven methods," Ecological Modelling, Elsevier, vol. 284(C), pages 1-9.
    2. Argaw Ambelu & Seblework Mekonen & Magaly Koch & Taffere Addis & Pieter Boets & Gert Everaert & Peter Goethals, 2014. "The Application of Predictive Modelling for Determining Bio-Environmental Factors Affecting the Distribution of Blackflies (Diptera: Simuliidae) in the Gilgel Gibe Watershed in Southwest Ethiopia," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-10, November.
    3. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    4. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    5. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    6. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.
    7. David Havrlant & Peter Tóth & Julia Wörz, 2016. "On the optimal number of indicators – nowcasting GDP growth in CESEE," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 54-72.
    8. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    9. Aleksandra Riedl & Julia Wörz, 2018. "A simple approach to nowcasting GDP growth in CESEE economies," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/18, pages 56-74.
    10. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    11. repec:zbw:bofitp:2018_011 is not listed on IDEAS
    12. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    13. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    14. Jean Boivin & Marc P. Giannoni & Dalibor Stevanović, 2020. "Dynamic Effects of Credit Shocks in a Data-Rich Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 272-284, April.
    15. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    16. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    17. Sheng Zhu & Ella Kavanagh & Niall O'Sullivan, 2021. "Constructing a financial conditions index for the United Kingdom: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2976-2989, April.
    18. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    19. Jan J. J. Groen & Paolo A. Pesenti, 2011. "Commodity Prices, Commodity Currencies, and Global Economic Developments," NBER Chapters, in: Commodity Prices and Markets, pages 15-42, National Bureau of Economic Research, Inc.
    20. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    21. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.

    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:eee:ecomod:v:251:y:2013:i:c:p:44-53. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.