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Prediction of displacement of tailings dams based on MISSA-CNN-BiLSTM model
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Di LIU1, 2, Hui YANG1, 2, Caiwu LU1, 2, Shunling RUAN1, 2, Song JIANG1, 2
China Safety Science Journal | 2024, 34(9) : 145 - 154
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China Safety Science Journal | 2024, 34(9): 145-154
• Safety engineering technology •
Prediction of displacement of tailings dams based on MISSA-CNN-BiLSTM model
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Di LIU1, 2, Hui YANG1, 2, Caiwu LU1, 2, Shunling RUAN1, 2, Song JIANG1, 2
Affiliations
  • 1 School of Resource Engineering，Xi'an University of Architecture and Technology，Xi'an Shaanxi 710055，China
  • 2 Xi'an Key Laboratory of Perceptive Computing and Decision for Intelligent Industry，Xi'an University of Architecture and Technology，Xi'an Shaanxi 710055，China
Published: 2024-09-28 doi: 10.16265/j.cnki.issn1003-3033.2024.09.1091
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A comprehensive and sophisticated multi-algorithm coupled dynamic prediction model is proposed to address the intricate reality and stringent accuracy requirements of predicting tailings dam displacement. Firstly，by employing a time series decomposition model，the cumulative displacement is disaggregated into its trend and cyclical components. The trend term displacement is then forecasted using a Gaussian regression time series prediction model. Secondly，various Copula functions are employed to investigate the overall correlation between the inducing factors and the cyclical term displacement. Owing to the diverse influencing factors and strong nonlinearities associated with the cyclical term displacement，the MISSA-CNN-BiLSTM model is utilized for prediction. Lastly，the predicted trend term displacement from the Gaussian regression model and the predicted cyclical term displacement from the MISSA-CNN-BiLSTM model are merged. The results demonstrate a high degree of consistency between the predicted cumulative landslide displacements and the measured values，with a correlation coefficient of 0.996 and a root mean square error (RMSE) of 0.13 mm. The multi-algorithm coupled model，based on MISSA-CNN-BiLSTM，exhibits remarkable prediction accuracy and effectively captures step changes in tailings dam displacements.

multi strategy improved sparrow search algorithm(MISSA)  /  convolutional neural networks(CNN)  /  Bi-directional long short-term memory(BiLSTM)  /  tailing dam  /  displacement prediction  /  deep learning model
Di LIU, Hui YANG, Caiwu LU, Shunling RUAN, Song JIANG. Prediction of displacement of tailings dams based on MISSA-CNN-BiLSTM model[J]. China Safety Science Journal, 2024 , 34 (9) : 145 -154 . DOI: 10.16265/j.cnki.issn1003-3033.2024.09.1091
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Year 2024 volume 34 Issue 9
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.09.1091
  • Receive Date：2024-03-18
  • Online Date：2025-07-09
  • Published：2024-09-28
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  • Received：2024-03-18
  • Revised：2024-06-19
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Affiliations
    1 School of Resource Engineering，Xi'an University of Architecture and Technology，Xi'an Shaanxi 710055，China
    2 Xi'an Key Laboratory of Perceptive Computing and Decision for Intelligent Industry，Xi'an University of Architecture and Technology，Xi'an Shaanxi 710055，China
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2024.09.1091
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表12种不同金属材料的力学参数
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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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