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Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN
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Yi ZHOU1, Guowen PENG1, **, Zhao HUANG2, Pengfei YANG1, Dandan LIU1, Xiaoli CHEN1
China Safety Science Journal | 2024, 34(4) : 145 - 152
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China Safety Science Journal | 2024, 34(4): 145-152
• Safety engineering technology •
Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN
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Yi ZHOU1, Guowen PENG1, **, Zhao HUANG2, Pengfei YANG1, Dandan LIU1, Xiaoli CHEN1
Affiliations
  • 1 School of Resource & Environment and Safety Engineering，University of South China，Hengyang Hunan 421001，China
  • 2 China National Nuclear Corporation 272 Uranium Industry Co.，Ltd.，Hengyang Hunan 421001，China
Published: 2024-04-28 doi: 10.16265/j.cnki.issn1003-3033.2024.04.1693
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To improve the efficiency of monitoring work in the retirement treatment of uranium tailings ponds，an intelligent monitoring and prediction model of deformation of uranium tailings ponds was proposed based on SBAS-InSAR technology and BPNN. Firstly，SBAS-InSAR technology was used to obtain the cumulative deformation and annual deformation rate of the uranium tailings pond over the past two years. The accuracy of InSAR monitoring values was verified using seven Global Navigation Satellite System (GNSS) monitoring stations on the first dam. Then，the cumulative settlement of four dam sections，including Leigongtang dam，nanpo cross dam，Battle dam and Songlin dam，was selected and analyzed in conjunction with rainfall. Finally，the cumulative settlement data of 100 settlement points of the uranium tailings dam were randomly extracted to predict the deformation of the uranium tailings dam. The results show that from December 2020 to December 2022，the deformation rate of uranium tailings dam is between -60.06-34.94 mm/a. The overall settlement of the uranium tailings dam is in a sinking state，with a maximum cumulative settlement of -46.67 mm. The average absolute error between the predicted values of BPNN and the actual monitoring values is 0.586 mm，and the mean square error is 0.624 mm.

small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR)  /  back propagation neural network(BPNN)  /  uranium tailings pond  /  intelligent deformation monitoring  /  sentinel-1A
Yi ZHOU, Guowen PENG, Zhao HUANG, Pengfei YANG, Dandan LIU, Xiaoli CHEN. Intelligent monitoring and prediction of deformation of uranium tailings dam based on SBAS-InSAR and BPNN[J]. China Safety Science Journal, 2024 , 34 (4) : 145 -152 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1693
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Year 2024 volume 34 Issue 4
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.04.1693
  • Receive Date：2023-12-15
  • Online Date：2025-07-09
  • Published：2024-04-28
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  • Received：2023-12-15
  • Revised：2024-01-18
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Affiliations
    1 School of Resource & Environment and Safety Engineering，University of South China，Hengyang Hunan 421001，China
    2 China National Nuclear Corporation 272 Uranium Industry Co.，Ltd.，Hengyang Hunan 421001，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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