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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
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    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.
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    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.

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