Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Journal of Scientifi...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Journal of Scientific Reports-A
Article . 2024 . Peer-reviewed
Data sources: Crossref
versions View all 2 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Forecasting urban forest recreation areas in Turkey using machine learning methods

Authors: Mehmet Cüneyt Özbalcı; Sena Dikici; Turgay Tugay Bilgin;

Forecasting urban forest recreation areas in Turkey using machine learning methods

Abstract

Recreation is the process of revitalizing and renewing human existence through optional activities, serving as a broad description. It has prominently arisen as a reaction to personal requirements for stress reduction, especially in developed urban areas. Engaging in this recreational activity provides a way to utilize one's spare time, providing refreshment for both the physical and mental aspects, whether done alone or with others, in countryside or city environments. Urban forests are important leisure places within city environments. An expanded presence of urban forest places can greatly enhance the general well-being of society. The estimation of urban forest areas in the future may receive increased attention, leading to measures to extend current areas or prepare for future activities and services. We utilized official statistics from the years 2013 to 2021, sourced from the Republic of Turkey official website. Ministry of Agriculture and Forestry's General Directorate of Forestry. We used statistics that contained information about urban forests, classified as Type D recreational areas, to create a dataset. We performed provincial-level area projections for the year 2021. Using the KNIME platform, we used three different analysis techniques: linear regression analysis, gradient-boosted regression trees and artificial neural networks. It is seen that the results of linear regression and artificial neural networks are close to each other and give good results. The peak performance was attained using artificial neural networks, resulting in an R2 score of 0.99. This study differs from other similar projects by concentrating on calculating urban forest recreational spaces per province throughout Turkey, using data provided by government agencies. The accomplishments highlight the ability to make reliable predictions about future forest resources by using analogous forecasts in the upcoming years.

Related Organizations
Keywords

Semi- and Unsupervised Learning, Recreation areas;Urban forests;Linear regression analysis;Gradient boosted regression trees;Artificial neural network, Yarı ve Denetimsiz Öğrenme

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold