IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5634-d1105326.html
   My bibliography  Save this article

Classification of Urban Green Space Types Using Machine Learning Optimized by Marine Predators Algorithm

Author

Listed:
  • Jiayu Yan

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Huiping Liu

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Shangyuan Yu

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Xiaowen Zong

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Yao Shan

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

Abstract

The accuracy of machine learning models is affected by hyperparameters when classifying different types of urban green spaces. To investigate the impact of hyperparametric algorithms on model optimization, this study used the Marine Predators Algorithm (MPA) to optimize three models: K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF). The feasibility of the algorithm was illustrated by extracting and analyzing park green space and attached green spaces within the fifth-ring road of Beijing. A dataset of urban green space type labels was constructed using SPOT6. Three optimized models, MPA-KNN, MPA-SVM and MPA-RF, were constructed. The optimum hyperparameter combination was chosen based on the accuracy of the validation set, and the three optimized models were compared in terms of the Area Under Curve (AUC) value, accuracy on the test set, and other indicators. The results showed that applying MPA improves the accuracy of the validation set of the KNN, SVM, and RF models by 4.2%, 2.2%, and 1.2%, respectively. The MPA-RF model had an AUC value of 0.983 and a test set accuracy of 89.93%, indicating that it was the most accurate of the three models.

Suggested Citation

  • Jiayu Yan & Huiping Liu & Shangyuan Yu & Xiaowen Zong & Yao Shan, 2023. "Classification of Urban Green Space Types Using Machine Learning Optimized by Marine Predators Algorithm," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5634-:d:1105326
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5634/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/5634/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin Seidl & Manal Saifane, 2021. "A green intensity index to better assess the multiple functions of urban vegetation with an application to Paris metropolitan area," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 15204-15224, October.
    2. Nhat-Duc Hoang & Xuan-Linh Tran, 2021. "Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-22, May.
    3. Jeroen Degerickx & Martin Hermy & Ben Somers, 2020. "Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data," Sustainability, MDPI, vol. 12(5), pages 1-35, March.
    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. Maliha Sanzana Misty & Muhammad Al-Amin Hoque & Sharif A. Mukul, 2024. "Assessment of Urban Green Space Dynamics in Dhaka South City Corporation of Bangladesh Using Geospatial Techniques," Land, MDPI, vol. 13(9), pages 1-17, September.

    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. Jaloliddin Rustamov & Zahiriddin Rustamov & Nazar Zaki, 2023. "Green Space Quality Analysis Using Machine Learning Approaches," Sustainability, MDPI, vol. 15(10), pages 1-25, May.
    2. Manley, Kyle & Nyelele, Charity & Egoh, Benis N., 2022. "A review of machine learning and big data applications in addressing ecosystem service research gaps," Ecosystem Services, Elsevier, vol. 57(C).
    3. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    4. Mohammad Reza Khalilnezhad & Francesca Ugolini & Luciano Massetti, 2021. "Attitudes and Behaviors toward the Use of Public and Private Green Space during the COVID-19 Pandemic in Iran," Land, MDPI, vol. 10(10), pages 1-22, October.
    5. Bin Li & Shaoning Li & Hongjuan Lei & Na Zhao & Chenchen Liu & Jiaxing Fang & Xu Liu & Shaowei Lu & Xiaotian Xu, 2024. "Application of High-Spatial-Resolution Imagery and Deep Learning Algorithms to Spatial Allocation of Urban Parks’ Supply and Demand in Beijing, China," Land, MDPI, vol. 13(7), pages 1-21, July.

    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:15:y:2023:i:7:p:5634-:d:1105326. 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.