Volume 29 Issue 6
Dec.  2021
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Feng Chunjian, Liu Hanlu, Liu Jinkun, et al. 2021. Prediction method for contribution rate of wave-induced seabed transient liquefaction to resuspension based on deep learning [J].Journal of Engineering Geology, 29(6): 1788-1795. doi: 10.13544/j.cnki.jeg.2021-0423
Citation: Feng Chunjian, Liu Hanlu, Liu Jinkun, et al. 2021. Prediction method for contribution rate of wave-induced seabed transient liquefaction to resuspension based on deep learning [J].Journal of Engineering Geology, 29(6): 1788-1795. doi: 10.13544/j.cnki.jeg.2021-0423


doi: 10.13544/j.cnki.jeg.2021-0423

the National Natural Science Foundation of China 41877223

Research on Guide for In-situ Monitoring Systems for Catastrophic Geology (landslides, etc.) of Submurine Pipelines 20200210

  • Received Date: 2021-06-30
  • Rev Recd Date: 2021-10-20
  • Available Online: 2022-01-06
  • Publish Date: 2021-12-25
  • Wave-induced transient liquefaction seepage leads to the transport of fine-grained sediments from the seabed to seawater. The contribution of wave-induced transient liquefaction to the sediment resuspension cannot be ignored,and the accurate prediction of the contribution is difficult. The observation data in the Yellow River subaquatic delta include water depth,significant wave height,significant wave period,suspended sediment concentration in the benthic chamber,and suspended sediment concentration out of the benthic chamber. These data are treated as the input data sets. Then,a deep learning model of transient liquefaction contribution to resuspension is developed based on a long short-term memory recurrent neural network. To evaluate the performance of the model,the prediction results of the deep learning model based on LSTM are compared with other models. These models include Support Vector Regression and Artificial Neural Network,and have mean absolute percentage error(MAPE),root mean square error(RMSE) and mean squared error-standard deviation(RSR). The results show that the LSTM model has the smallest error for transient pumping resuspension contribution within 3.5 days,with the mean values of MAPE,RMSE,and RSR of 5.87%,1.6730,and 0.1574,respectively. Therefore,the LSTM model can effectively reduce the error superposition problem arising from machine learning methods in continuous forecasting.
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  • Dey S,Ali S Z,Padhi E. 2018. Advances in analytical modeling of suspended sediment transport[J]. Journal of Hydro-environment Research,20 : 110-126. doi: 10.1016/j.jher.2018.02.004
    Du X, Sun Y F, Song Y P, et al. 2020. Multilayer perception neural network for assessment and prediction of earthquake-induced sand liquefaction[J]. Journal of Engineering Geology, 28 (6): 1425-1432.
    Goldstein E B, Coco G, Plant N G. 2019. A review of machine learning applications to coastal sediment transport and morphodynamics[J]. Earth-Science Reviews, 194 : 97-108. doi: 10.1016/j.earscirev.2019.04.022
    Graves A, Jaitly N, Mohamed A R. 2013. Hybrid speech recognition with deep bidirectional LSTM[C]//Automatic speech Recognition and Understanding (ASRU), 2013 IEEE Workshop on IEEE. [S.L.]: [s.n.]
    Green M O, Coco G. 2014. Review of wave-driven sediment resuspension and transport in estuaries[J]. Reviews of Geophysics, 52 (1): 77-117. doi: 10.1002/2013RG000437
    Jeng D S. 2003. Wave-induced sea floor dynamics[J]. Applied Mechanics Reviews, 56(4): 407. doi: 10.1115/1.1577359
    Hochreiter S, Schmidhuber J. 1997. Long short-term memory[J]. Neural Computation, 9 (8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735
    Huang C C, Chang M J, Lin G F, et al. 2021. Real-time forecasting of suspended sediment concentrations reservoirs by the optimal integration of multiple machine learning techniques[J]. Journal of Hydrology: Regional Studies, 34: 100804. doi: 10.1016/j.ejrh.2021.100804
    Kaveh K, Kaveh H, Bui M D, et al. 2020. Long short-term memory for predicting daily suspended sediment concentration[J]. Engineering with Computers, 37 (1): 2013-2027.
    Liu Y H, Fang R K, Sun Y C, et al. 2021. Machine learning based model for warning of regional landslide disasters[J]. Journal of Engineering Geology, 29 (1): 116-124. doi: 10.1088/1755-1315/783/1/012074
    Maa P Y, Kwon J I. 2007. Using ADV for cohesive sediment settling velocity measurements[J]. Estuarine Coastal & Shelf Science, 73(1-2): 351-354. http://www.researchgate.net/profile/Jerome_Maa/publication/228907015_Using_ADV_for_cohesive_sediment_settling_velocity_measurements/links/56254e1b08ae4d9e5c4bb259.pdf
    Maanen B V, Coco G, Bryan K R, et al. 2010. The use of artificial neural networks to analyze and predict alongshore sediment transport[J]. Copernicus Publications, 17 (5): 395-404. http://www.onacademic.com/detail/journal_1000040545937810_f6e5.html
    Mohammad Z K, Amin M M, Meysam A, et al. 2020. On the complexities of sediment load modeling using integrative machine learning: Application of the great river of Loíza in Puerto Rico[J]. Journal of Hydrology(Amsterdam), 585: 124759.
    Mohammad Z K, Özgür K, Jan A, et al. 2016. Evaluation of data driven models for river suspended sediment concentration modeling[J]. Journal of Hydrology, 535 : 457-472. doi: 10.1016/j.jhydrol.2016.02.012
    Rumelhart D E, Hinton G E, Williams R J. 1986. Learning representations by back-propagating errors[J]. Nature, 323(6088): 533-536. doi: 10.1038/323533a0
    Srivastava N, Hinton G, Krizhevsky A, et al. 2014. Dropout: A simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15 (1): 1929-1958. http://faculty.dbmi.pitt.edu/day/Bioinf2132-advanced-Bayes-and-R/index.php?path=previousDocuments/Bioinf2132-documents-2016/2016-12-15/&download=JMLRdropout.pdf
    Vapnik V, Golowich S E, Smola A. 1997. Support vector method for function approximation, regression estimation, and signal processing[J]. Advances in Neural Information Processing Systems, 9(2008): 281-287. http://citeseerx.ist.psu.edu/viewdoc/download?doi=
    Xu X B, Xu G H, Yang J J, et al. 2021. Field observation of the wave-induced pore pressure response in a silty soil seabed[J]. Geo-Marine Letters, 41(1): 13. doi: 10.1007/s00367-020-00680-6
    Xue C. 1993. Historical changes in the Yellow River delta, China[J]. Marine Geology, 113(3-4): 321-330. doi: 10.1016/0025-3227(93)90025-Q
    Yan H, Li S H, Wu L Z. 2019. Landslide displacement prediction based on multiple data-driven model methods[J]. Journal of Engineering Geology, 27 (2): 459-465. http://en.cnki.com.cn/Article_en/CJFDTotal-GCDZ201902028.htm
    Zhang S T, Jia Y G, Wen M Z, et al. 2017. Vertical migration of fine-grained sediments from interior to surface of seabed driven by seepage flows-'sub-bottom sediment pump action'[J]. Journal of Ocean University of China, 16 (1): 15-24. doi: 10.1007/s11802-017-3042-0
    Zhang S T, Jia Y G, Liu X L, et al. 2016. Feature and mechanism of sediment dynamic changing processes in the modern Yellow River delta[J]. Marine Geology and Quaternary Geology, 36 (6): 33-44. http://en.cnki.com.cn/Article_en/CJFDTOTAL-HYDZ201606006.htm
    Zhang S T, Jia Y G, Zhang Y Q, et al. 2018. In situ observations of wave pumping of sediments in the Yellow River Delta with a newly developed benthic chamber[J]. Marine Geophysical Research, 39 (4): 463-474. doi: 10.1007/s11001-018-9344-9
    杜星, 孙永福, 宋玉鹏, 等. 2020. 基于MPL神经网络的地震作用下砂土液化评估及预测[J]. 工程地质学报, 28 (6): 1425-1432. doi: 10.13544/j.cnki.jeg.2019-321
    刘艳辉, 方然可, 苏永超, 等. 2021. 基于机器学习的区域滑坡灾害预警模型研究[J]. 工程地质学报, 29 (1): 116-124. doi: 10.13544/j.cnki.jeg.2020-533
    鄢好, 李绍红, 吴礼舟. 2019. 联合多种数据驱动建模方法的滑坡位移预测研究[J]. 工程地质学报, 27 (2): 459-465. doi: 10.13544/j.cnki.jeg.2017-485
    杨作升. 1993. 埕岛油田勘探开发海洋环境[M]. 青岛: 青岛海洋大学出版社.
    张少同, 贾永刚, 刘晓磊, 等. 2016. 现代黄河三角洲沉积物动态变化过程的特征与机理[J]. 海洋地质与第四纪地质, 36 (6): 33-44. https://www.cnki.com.cn/Article/CJFDTOTAL-HYDZ201606006.htm
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