Research on RNN and LSTM Method for Dynamic Prediction of Landslide Displacement
Good landslide displacement prediction is an important part of the landslide disaster warning.Limited by the nonlinear dynamic characteristics of landslide displacement evolution,historical data is generally missing in traditional prediction methods,resulting in low prediction accuracy.To this end,t...
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Editorial Office of Pearl River
2021-01-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.002 |
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| author | ZHANG Mingyue LI Limin WEN Zongzhou |
| author_facet | ZHANG Mingyue LI Limin WEN Zongzhou |
| author_sort | ZHANG Mingyue |
| collection | DOAJ |
| description | Good landslide displacement prediction is an important part of the landslide disaster warning.Limited by the nonlinear dynamic characteristics of landslide displacement evolution,historical data is generally missing in traditional prediction methods,resulting in low prediction accuracy.To this end,this paper proposes a deep learning method for landslide displacement prediction to establish two dynamic displacement prediction models of recurrent neural network (RNN) and long short term memory network (LSTM) for comparison,and selects the displacement changes of multiple monitoring points for dynamic prediction by the method of “circulation training”,taking the Xintan landslide project as an example.The results show that when the error function meets the expected accuracy,the LSTM model has higher prediction accuracy.In addition,various evaluation indicators also show that the overall prediction effect of the LSTM model is better. |
| format | Article |
| id | doaj-art-edf00032376f4c1f8672448c479e1fbe |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-edf00032376f4c1f8672448c479e1fbe2025-01-15T02:29:24ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352021-01-014247648307Research on RNN and LSTM Method for Dynamic Prediction of Landslide DisplacementZHANG MingyueLI LiminWEN ZongzhouGood landslide displacement prediction is an important part of the landslide disaster warning.Limited by the nonlinear dynamic characteristics of landslide displacement evolution,historical data is generally missing in traditional prediction methods,resulting in low prediction accuracy.To this end,this paper proposes a deep learning method for landslide displacement prediction to establish two dynamic displacement prediction models of recurrent neural network (RNN) and long short term memory network (LSTM) for comparison,and selects the displacement changes of multiple monitoring points for dynamic prediction by the method of “circulation training”,taking the Xintan landslide project as an example.The results show that when the error function meets the expected accuracy,the LSTM model has higher prediction accuracy.In addition,various evaluation indicators also show that the overall prediction effect of the LSTM model is better.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.002landslide disasterdisplacement predictionrecurrent neural networklong short term memory networkdynamic prediction |
| spellingShingle | ZHANG Mingyue LI Limin WEN Zongzhou Research on RNN and LSTM Method for Dynamic Prediction of Landslide Displacement Renmin Zhujiang landslide disaster displacement prediction recurrent neural network long short term memory network dynamic prediction |
| title | Research on RNN and LSTM Method for Dynamic Prediction of Landslide Displacement |
| title_full | Research on RNN and LSTM Method for Dynamic Prediction of Landslide Displacement |
| title_fullStr | Research on RNN and LSTM Method for Dynamic Prediction of Landslide Displacement |
| title_full_unstemmed | Research on RNN and LSTM Method for Dynamic Prediction of Landslide Displacement |
| title_short | Research on RNN and LSTM Method for Dynamic Prediction of Landslide Displacement |
| title_sort | research on rnn and lstm method for dynamic prediction of landslide displacement |
| topic | landslide disaster displacement prediction recurrent neural network long short term memory network dynamic prediction |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2021.09.002 |
| work_keys_str_mv | AT zhangmingyue researchonrnnandlstmmethodfordynamicpredictionoflandslidedisplacement AT lilimin researchonrnnandlstmmethodfordynamicpredictionoflandslidedisplacement AT wenzongzhou researchonrnnandlstmmethodfordynamicpredictionoflandslidedisplacement |