Urban land use function prediction method based on RF and cellular automaton model

被引:0
作者
Song, Wenjun [1 ]
Ling, Min [1 ]
机构
[1] Shanghai Construct Management Vocat Coll, Dept Municipal Engn, Shanghai 201702, Peoples R China
来源
COMPUTATIONAL URBAN SCIENCE | 2025年 / 5卷 / 01期
关键词
Random forest; Cellular automaton; Urban; Land use function identification; Land use function prediction;
D O I
10.1007/s43762-025-00169-x
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
To accurately grasp the dynamic changes of urban land use and solve the difficulties and challenges in predicting urban land use functions at present, this study integrates interest point data and open street map data through kernel density estimation technology. Moreover, the study also integrates random forest algorithm and cellular automaton model, and finally proposes a new urban land use function prediction method based on random forest algorithm and cellular automaton model. The experiment results show that the comprehensive precision and Kappa coefficient calculated by the research method reach 81.88% and 0.71, separately, verifying the validity of the way. The prediction results of this method indicate that the number of squares required for road and transportation, industrial land, public services, residential land, green squares, and commercial service land in Hulunbuir City in 2030 is expected to reach 2000, 3889, 2591, 9280, 2696, and 8988, respectively. This provides a scientific basis for future urban planning. To sum up, the method raised by the study has high applicability and accuracy in predicting the distribution pattern of urban functional regions, and has important instructing significance for urban planning, optimal assignment of land resources, and continuous expanding of urbanization.
引用
收藏
页数:15
相关论文
共 24 条
[1]   Prediction of climatic changes caused by land use changes in urban area using artificial neural networks [J].
Arabaci, Derya ;
Simsek, Cagdas Kuscu .
THEORETICAL AND APPLIED CLIMATOLOGY, 2023, 152 (1-2) :265-279
[2]  
Cao H., 2023, ARTIF INTELL APPL, V1, P106, DOI DOI 10.47852/BONVIEWAIA2202337
[3]   Duo satellite-based remotely sensed land surface temperature prediction by various methods of machine learning [J].
Chauhan, Shivam ;
Jethoo, Ajay Singh ;
Mishra, Ajay ;
Varshney, Vaibhav .
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, 2024, 18 (04) :467-485
[4]  
Gautam Anita, 2024, IOP Conference Series: Earth and Environmental Science, V1412, DOI 10.1088/1755-1315/1412/1/012032
[5]   Flood risk assessment through rapid urbanization LULC change with destruction of urban green infrastructures based on NASA Landsat time series data: A case of study Kuala Lumpur between 1990 - 2021 [J].
Ghalehteimouri, Kamran Jafarpour ;
Ros, Faizah Che ;
Rambat, Shuib .
ECOLOGICAL FRONTIERS, 2024, 44 (02) :289-306
[6]   Urban construction land demand prediction and spatial pattern simulation under carbon peak and neutrality goals: A case study of Guangzhou, China [J].
Hu Xintao ;
Li Zhihui ;
Cai Yumei ;
Wu Feng .
JOURNAL OF GEOGRAPHICAL SCIENCES, 2022, 32 (11) :2251-2270
[7]  
Huang C., 2024, Journal of Environmental Management, V11, P351
[8]  
JafarpourGhalehteimouri K., 2022, Environmental Challenges, V6, P100399, DOI [10.1016/j.envc.2021.100399, DOI 10.1016/J.ENVC.2021.100399]
[10]   Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur, Malaysia [J].
Kamran, Jafarpour Ghalehteimouri ;
Faizah, Che Ros ;
Shuib, Rambat .
REGIONAL SUSTAINABILITY, 2024, 5 (04)