Investigation of Spatio-Temporal Simulation of Mining Subsidence and Its Determinants Utilizing the RF-CA Model

Jikun Xu, Chaode Yan, Baowei Zhang (), Xuanchi Chen, Xu Yan, Rongxing Wang, Binhang Yu and Muhammad Waseem Boota
Additional contact information
Jikun Xu: School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
Chaode Yan: School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
Baowei Zhang: School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450052, China
Xuanchi Chen: School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
Xu Yan: School of Electrical and Information Engineering, Henan University of Engineering, Zhengzhou 451191, China
Rongxing Wang: School of Journalism and Communication, Guangxi University, Nanning 530004, China
Binhang Yu: College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China
Muhammad Waseem Boota: College of Geography and Environmental Science, Henan University, Kaifeng 475004, China

Land, 2025, vol. 14, issue 2, 1-20

Abstract: It is important to carry out timely scientific assessments of surface subsidence in coal resource cities for ecological environmental protection. Traditional subsidence simulation methods cannot quantitatively describe the driving factors that contribute to or ignore the dynamic connections of subsidence across time and space. Thus, a novel spatio-temporal subsidence simulation model is proposed that couples random forest (RF) and cellular automaton (CA) models, which are used to quantify the contributions of driving factors and simulate the spatio-temporal dynamic changes in subsidence. The RF algorithm is first utilized to clarify the contributions of the driving factors to subsidence and to formulate transformation rules for simulation. Then, a spatio-temporal simulation of subsidence is accomplished by combining it with the CA model. Finally, the method is validated based on the Yongcheng coalfield. The results show that the depth–thickness ratio (0.242), distance to the working face (0.159), distance to buildings (0.150), and lithology (0.147) play main roles in the development of subsidence. Meanwhile, the model can effectively simulate the spatio-temporal changes in mining subsidence. The simulation results were evaluated using 2021 subsidence data as the basis data; the simulation’s overall accuracy (OA) was 0.83, and the Kappa coefficient (KC) was 0.71. This method can obtain a more realistic representation of the spatio-temporal distribution of subsidence while considering the driving factors, which provides technological support for land-use planning and ecological and environmental protection in coal resource cities.

Keywords: driving factor contribution; spatio-temporal subsidence simulation; RF model; CA model; urban sustainable development (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/2/268/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/2/268/ (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:jlands:v:14:y:2025:i:2:p:268-:d:1578418

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

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