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Research on Dam Displacement Prediction Based on Improved Combined Deep Learning Model
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Chuan-dong REN1, Zhi-zhen WANG2, Shu-ping LIU3, Hong-wei LIU1, Long-tan HOU1
Water Resources and Power | 2023, 41(10) : 100 - 103
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Water Resources and Power | 2023, 41(10): 100-103
• DAM SAFETY AND MONITORING •
Research on Dam Displacement Prediction Based on Improved Combined Deep Learning Model
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Chuan-dong REN1, Zhi-zhen WANG2, Shu-ping LIU3, Hong-wei LIU1, Long-tan HOU1
Affiliations
  • 1.Shandong Provincial Water Resources Survey and Design Institute Co., Ltd., Jinan 250013, China
  • 2.Shandong Agricultural Exchange and Cooperation Center, Jinan 250013, China
  • 3.Shandong Hydraulic Engineering Construction Quality and Safety Center, Jinan 250013, China
Published: 2023-10-25 doi: 10.20040/j.cnki.1000-7709.2023.20222611
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Dam displacement can directly affect the quality and operation safety of the dam. To find out the prediction model of the dam displacement, the temporal convolutional neural network model was used to predict the dam displacement. Three bionic algorithms of the sparrow search algorithm (SSA), the gray wolf algorithm (GWO) and the bat algorithm (BA) were improved by genetic algorithm, and three optimization algorithms including MSSA, MGWO and MBA were obtained. Taking root mean square error, determination coefficient, mean absolute error, efficiency coefficient and GPI index as precision index system, three combined weighted models including D-MSSA-TCN, D-MGWO-TCN and DMBA-TCN were constructed based on the deep belief network model (DBN). The results show that the MSSA algorithm had the highest operating efficiency and accuracy among all the algorithms. The accuracy of the three combined models was significantly higher than the rest of the models. The D-MSSA-TCN model had the highest accuracy among all models and can be recommended for estimating dam displacement.

dam displacement  /  temporal convolutional neural network  /  sparrow search algorithm  /  genetic algorithm  /  deep belief network model
任传栋, 王志真, 刘淑萍, 刘洪伟, 侯龙潭. 基于改进组合深度学习模型的大坝位移预测研究. 水电能源科学, 2023 , 41 (10) : 100 -103 . DOI: 10.20040/j.cnki.1000-7709.2023.20222611
Chuan-dong REN, Zhi-zhen WANG, Shu-ping LIU, Hong-wei LIU, Long-tan HOU. Research on Dam Displacement Prediction Based on Improved Combined Deep Learning Model[J]. Water Resources and Power, 2023 , 41 (10) : 100 -103 . DOI: 10.20040/j.cnki.1000-7709.2023.20222611
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Year 2023 volume 41 Issue 10
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20222611
  • Receive Date：2022-12-20
  • Online Date：2026-01-28
  • Published：2023-10-25
Article Data
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History
  • Received：2022-12-20
  • Revised：2023-01-24
Affiliations
    1.Shandong Provincial Water Resources Survey and Design Institute Co., Ltd., Jinan 250013, China
    2.Shandong Agricultural Exchange and Cooperation Center, Jinan 250013, China
    3.Shandong Hydraulic Engineering Construction Quality and Safety Center, Jinan 250013, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20222611
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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
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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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