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Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the three Gorges Reservoir Region, China

机译:BP神经网络在三峡库区累计滑坡岸毁预测中的应用

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摘要

In recent years, a large number of bank destruction occur in the reservoir area under the effect of water fluctuation, which may be lead to reservoir accumulative landslide geological hazards finally. The paper conducted the bank destruction forecasting study for accumulative landslides in the Three Gorges Reservoir Region, China utilizing back-propagation (BP) neural network approach. A representative scenario of Jinle landslide is then taken for analysis purposes. On the basis of the existing data sets of bank destruction cases, the BP neural network forecasting model and the corresponding programs for bank destruction are both presented, whose forecasting result is validated by two independent approaches, namely empirical method and numerical modeling method. Furthermore, the BP neural network model had obvious advantages over the convention approaches in the aspects of the fast calculation speed and high convenience. According to the bank destruction forecasting scale presented above, the corresponding revetment measures can be proposed to prevent the occurring of the bank destruction, whose effectiveness has been further validated by the actual engineering practice.
机译:近年来,在水涨落的作用下,库区大量堤岸破坏,可能最终导致库区滑坡累积地质灾害。本文运用BP神经网络方法对中国三峡库区堆积滑坡进行了堤岸破坏预测研究。然后以金乐滑坡为代表进行分析。在现有银行破坏案例数据集的基础上,提出了BP神经网络预测模型和相应的银行破坏程序,并通过经验方法和数值建模方法两种独立的方法对预测结果进行了验证。此外,BP神经网络模型在计算速度快,方便性高等方面比常规方法有明显的优势。根据以上给出的堤岸破坏预测规模,可以提出相应的护岸措施,以防止堤岸破坏的发生,其有效性已经通过实际工程实践得到了进一步验证。

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