Prediction of the temporal and spatial evolution of subsidence waters in the Huainan mining area based on the CA-Markov model

被引:0
|
作者
Zhang, Xuyang [1 ]
Chen, Xiaoyang [2 ]
Zhou, Yuzhi [1 ]
Chen, Yongchun [3 ]
Long, Linli [1 ]
Hu, Pian [1 ]
机构
[1] Anhui Univ Sci & Technol, Sch Earth & Environm, Huainan 232001, Peoples R China
[2] Anhui Univ Sci & Technol, Sch Earth & Environm, Anhui Engn Lab Comprehens Utilizat Water & Soil Re, Huainan 232001, Peoples R China
[3] Res Inst Co Ltd, Pingan Coal Min Engn, Huainan 232001, Peoples R China
关键词
NDWI; Huainan mining area; Center of gravity migration; CA-Markov; Decision tree classification; LAND-USE CHANGE; DYNAMICS;
D O I
10.1007/s10668-024-04631-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Coal mining leads to surface subsidence and accumulation of water, which is the main characteristic of high diving coal mining areas, and the long time series monitoring of coal mining subsidence water bodies can help to assess the integrated impact effects of coal mining on land, ecology and society. In this context, the present study aims to predict water bodies in the Huainan mining area over the 1989-2021 period using the Landsat remote sensing image data and decision tree classification method to investigate the annual changes in the water body areas. In addition, the standard deviation ellipse and center of gravity migration models were used to analyze the spatial heterogeneity of water bodies, while the CA-Markov and MCE-CA-Markov models were applied to predict the future trend of water bodies for 2030. The results showed (1) substantial increases in the subsided water bodies in the Huainan mining area over the 1989-2021 period, with a rapid expansion rate, particularly in the northwestern part of the study area. In addition, water bodies migrated naturally in the northwest-southeast direction from 2015 to 2021; (2) changes in the water body areas in the Huainan mining area from 1989 to 2021 estimated at 284.03 km2, based on decision tree classification, with 1989 as the base year, with an average annual changing rate of 20.23%; (3) a high degree of consistency between the actual water bodies in 2021 and those predicted for 2030 using the CA-Markov and MCE-CA-Markov models, showing substantial increases in the water body areas in 2030. The sinkhole ponding areas formed a large lake, particularly in the Guqiao, Gu Bei, Zhangji, Xieqiao, and Liu Zhuang mines, expanding continuously toward the northwest. Therefore, investigating and predicting the spatiotemporal evolution of water bodies in coal mining subsidence areas with high diving levels are of great importance for providing a scientific basis to ensure the effective ecological restoration of coal mining subsidence areas.
引用
收藏
页数:21
相关论文
共 50 条
  • [41] Prediction of land use around urban metro stations using the CA-Markov model
    Nong, Xingzhong
    Geng, Ming
    Jia, Xia
    JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING, 2024,
  • [42] Land use change and prediction in the Baimahe Basin using GIS and CA-Markov model
    Wang, Shixu
    Zhang, Zulu
    Wang, Xue
    35TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT (ISRSE35), 2014, 17
  • [43] Prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and quantitative analysis of driving factors
    Zhang, Xuyang
    Zhou, Yuzhi
    Long, Linli
    Hu, Pian
    Huang, Meiqin
    Chen, Yongchun
    Chen, Xiaoyang
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (06)
  • [44] Assessment and prediction of urban growth for a mega-city using CA-Markov model
    Yadav, Veerendra
    Ghosh, Sanjay Kumar
    GEOCARTO INTERNATIONAL, 2021, 36 (17) : 1960 - 1992
  • [45] Model for mining subsidence prediction based on Boltzmann function
    Wang, Ning
    Wu, Kan
    Liu, Jin
    An, Shi-Kai
    Meitan Xuebao/Journal of the China Coal Society, 2013, 38 (08): : 1352 - 1356
  • [46] Study on temporal and spatial evolution characteristics of water accumulation in coal mining subsidence area with high groundwater level: taking Anhui Province Mining Area as an example
    Sun, Ru
    Zhu, Xiaojun
    Zhang, Pengfei
    Liang, Ming
    Zhang, Xin
    Ning, Zhengyuan
    Peng
    Liu, Hui
    Yang, Xiaoyu
    Huang, Wenshan
    Yan, Yu
    Duan, Changzheng
    Meitan Kexue Jishu/Coal Science and Technology (Peking), 2022, 50 (12): : 215 - 224
  • [47] Spatial-temporal variation of groundwater and land subsidence evolution in Beijing area
    Lei, K.
    Luo, Y.
    Chen, B.
    Guo, M.
    Guo, G.
    Yang, Y.
    Wang, R.
    Prevention and Mitigation of Natural and Anthropogenic Hazards due to Land Subsidence, 2015, 372 : 7 - 11
  • [48] Simulation and forecast study of land use change based on CA-Markov model
    Zhao D.
    Du M.
    Yang J.
    Li P.
    He S.
    Zhu D.
    Yang, Jianyu (ycjyyang@cau.edu.cn), 1600, Chinese Society of Agricultural Machinery (47): : 278 - 285
  • [49] Simulation and spatiotemporal evolution analysis of biocapacity in Xilingol based on CA-Markov land simulation
    Wang, Hao
    Hu, Yunfeng
    Liang, Yuting
    ENVIRONMENTAL AND SUSTAINABILITY INDICATORS, 2021, 11
  • [50] Exploring the predictive ability of the CA-Markov model for urban functional area in Nanjing old city
    Hu, Xinyu
    Zhu, Wei
    Shen, Ximing
    Bai, Ruxia
    Shi, Yi
    Li, Chen
    Zhao, Lili
    SCIENTIFIC REPORTS, 2024, 14 (01):