IDEAS home Printed from https://ideas.repec.org/a/caa/jnljfs/v62y2016i9id51-2016-jfs.html
   My bibliography  Save this article

Generalized height-diameter models for Fagus orientalis Lipsky in Hyrcanian forest, Iran

Author

Listed:
  • K. Ahmadi

    (Department of Forestry, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran)

  • S.J. Alavi

    (Department of Forestry, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran)

Abstract

In this study, 39 generalized height-diameter prediction models were developed for Oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forest in Iran. Data were collected from 75 permanent sample plots in uneven-aged stands of F. orientalis. A total of 1,067 individual tree height-diameter measurements were available for this study. For model testing a 10-fold cross validation method was used. The goodness of fit of the models was evaluated using six statistical measures including Akaike information criteria, Bayesian information criterion, root mean square error (RMSE), mean error, R2 and R2adj. Results showed that the R2 ranged from 0.62 to 0.78 and RMSE from 3.3 to 4.7 in the validation phase. Considering all the performance criteria, a model which uses DBH, dominant height, basal area per hectare and number of trees per hectare was found to be the best model to predict the height of Oriental beech from these data.

Suggested Citation

  • K. Ahmadi & S.J. Alavi, 2016. "Generalized height-diameter models for Fagus orientalis Lipsky in Hyrcanian forest, Iran," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 62(9), pages 413-421.
    • Handle: RePEc:caa:jnljfs:v:62:y:2016:i:9:id:51-2016-jfs
    DOI: 10.17221/51/2016-JFS
    as

    Download full text from publisher

    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/51/2016-JFS.html
    Download Restriction: free of charge
    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/51/2016-JFS.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/51/2016-JFS?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. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, February.
    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. Vangelis Marinakis & Themistoklis Koutsellis & Alexandros Nikas & Haris Doukas, 2021. "AI and Data Democratisation for Intelligent Energy Management," Energies, MDPI, vol. 14(14), pages 1-14, July.
    2. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    3. Emrouznejad, Ali & De Witte, Kristof, 2010. "COOPER-framework: A unified process for non-parametric projects," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1573-1586, December.
    4. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    5. Mehri, Ali & Darooneh, Amir H. & Shariati, Ashrafalsadat, 2012. "The complex networks approach for authorship attribution of books," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2429-2437.
    6. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    7. Beni Rohrbach & Sharolyn Anderson & Patrick Laube, 2016. "The effects of sample size on data quality in participatory mapping of past land use," Environment and Planning B, , vol. 43(4), pages 681-697, July.
    8. César Alfaro & Javier Cano-Montero & Javier Gómez & Javier M. Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
    9. Derya Ozturk & Nergiz Uzel-Gunini, 2022. "Investigation of the effects of hybrid modeling approaches, factor standardization, and categorical mapping on the performance of landslide susceptibility mapping in Van, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2571-2604, December.
    10. Shaheen, Muhammad & Khan, Muhammad Zeb, 2016. "A method of data mining for selection of site for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1225-1233.
    11. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    12. Andry Alamsyah & Fatma Saviera, 2021. "A Comparison of Indonesia E-Commerce Sentiment Analysis for Marketing Intelligence Effort," Papers 2103.00231, arXiv.org.
    13. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 0. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    14. Asil Oztekin, 2018. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 20(2), pages 223-238, April.
    15. Yucel, Ahmet & Dag, Ali & Oztekin, Asil & Carpenter, Mark, 2022. "A novel text analytic methodology for classification of product and service reviews," Journal of Business Research, Elsevier, vol. 151(C), pages 287-297.
    16. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    17. Kesriklioğlu, Esma & Oktay, Erkan & Karaaslan, Abdulkerim, 2023. "Predicting total household energy expenditures using ensemble learning methods," Energy, Elsevier, vol. 276(C).
    18. Abdorrahman Haeri, 2020. "Analyzing safety level and recognizing flaws of commercial centers through data mining approach," Journal of Risk and Reliability, , vol. 234(3), pages 512-526, June.
    19. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 2017. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 19(5), pages 1085-1099, October.
    20. Saljooghi, Saeed & Safisamghabadib, Azamdokht, 2016. "Analyzing Semiconductor component's market sales data to create an Expert Fuzzy inference system," MPRA Paper 79846, University Library of Munich, Germany.

    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:caa:jnljfs:v:62:y:2016:i:9:id:51-2016-jfs. 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: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    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.