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

Jiayu Yan, Huiping Liu (), Shangyuan Yu, Xiaowen Zong and Yao Shan
Additional contact information
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

Sustainability, 2023, vol. 15, issue 7, 1-18

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.

Keywords: urban green space; model optimization; Marine Predators Algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/7/5634/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/7/5634/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:7:p:5634-:d:1105326

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().