Landslide Displacement Prediction Model Based on Time Series and CNN-GRU
Landslide displacement prediction is an important basis for early landslide warning.This paper proposes a prediction model of landslide moving states based on time series and convolutional gated recurrent unit (CNN-GRU) to deal with the shortcomings of previous prediction models.Firstly,after employ...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2024-01-01
|
| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.02.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850091973315133440 |
|---|---|
| author | FU Zhentao LI Limin WANG Lianxia REN Ruibin CUI Chengtao FENG Qingqing |
| author_facet | FU Zhentao LI Limin WANG Lianxia REN Ruibin CUI Chengtao FENG Qingqing |
| author_sort | FU Zhentao |
| collection | DOAJ |
| description | Landslide displacement prediction is an important basis for early landslide warning.This paper proposes a prediction model of landslide moving states based on time series and convolutional gated recurrent unit (CNN-GRU) to deal with the shortcomings of previous prediction models.Firstly,after employing wavelet analysis to determine the displacement of the trend term,the exponential smoothing method is adopted to decompose the cumulative displacement to obtain two displacement types of the trend term and the periodic term,and the trend term is fitted by a five-order polynomial.Then,the autocorrelation function is utilized to test the periodic displacement characteristics,and the gray correlation method is applied to determine the correlation degree between each factor and the periodic term.Meanwhile,the periodic term and the influencing factor are input into the CNN-GRU model for prediction,and finally the predicted cumulative displacement value is obtained by superposition.By taking the Baishui River landslide in the Three Gorges Reservoir area as an example,this paper selects the data from January 2004 to December 2012 for study,and the average absolute error percentage of the final prediction results is only 0.525%,with RMSE of 9.614 and R<sup>2</sup> of 0.993.Experimental results show that CNN-GRU has higher prediction accuracy. |
| format | Article |
| id | doaj-art-b3ee8cc72bf2417a80545f375ab2b892 |
| institution | DOAJ |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-b3ee8cc72bf2417a80545f375ab2b8922025-08-20T02:42:14ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-01-014550118327Landslide Displacement Prediction Model Based on Time Series and CNN-GRUFU ZhentaoLI LiminWANG LianxiaREN RuibinCUI ChengtaoFENG QingqingLandslide displacement prediction is an important basis for early landslide warning.This paper proposes a prediction model of landslide moving states based on time series and convolutional gated recurrent unit (CNN-GRU) to deal with the shortcomings of previous prediction models.Firstly,after employing wavelet analysis to determine the displacement of the trend term,the exponential smoothing method is adopted to decompose the cumulative displacement to obtain two displacement types of the trend term and the periodic term,and the trend term is fitted by a five-order polynomial.Then,the autocorrelation function is utilized to test the periodic displacement characteristics,and the gray correlation method is applied to determine the correlation degree between each factor and the periodic term.Meanwhile,the periodic term and the influencing factor are input into the CNN-GRU model for prediction,and finally the predicted cumulative displacement value is obtained by superposition.By taking the Baishui River landslide in the Three Gorges Reservoir area as an example,this paper selects the data from January 2004 to December 2012 for study,and the average absolute error percentage of the final prediction results is only 0.525%,with RMSE of 9.614 and R<sup>2</sup> of 0.993.Experimental results show that CNN-GRU has higher prediction accuracy.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.02.001displacement predictiontime seriesconvolutional gated recurrent unitBaishui River landslide |
| spellingShingle | FU Zhentao LI Limin WANG Lianxia REN Ruibin CUI Chengtao FENG Qingqing Landslide Displacement Prediction Model Based on Time Series and CNN-GRU Renmin Zhujiang displacement prediction time series convolutional gated recurrent unit Baishui River landslide |
| title | Landslide Displacement Prediction Model Based on Time Series and CNN-GRU |
| title_full | Landslide Displacement Prediction Model Based on Time Series and CNN-GRU |
| title_fullStr | Landslide Displacement Prediction Model Based on Time Series and CNN-GRU |
| title_full_unstemmed | Landslide Displacement Prediction Model Based on Time Series and CNN-GRU |
| title_short | Landslide Displacement Prediction Model Based on Time Series and CNN-GRU |
| title_sort | landslide displacement prediction model based on time series and cnn gru |
| topic | displacement prediction time series convolutional gated recurrent unit Baishui River landslide |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.02.001 |
| work_keys_str_mv | AT fuzhentao landslidedisplacementpredictionmodelbasedontimeseriesandcnngru AT lilimin landslidedisplacementpredictionmodelbasedontimeseriesandcnngru AT wanglianxia landslidedisplacementpredictionmodelbasedontimeseriesandcnngru AT renruibin landslidedisplacementpredictionmodelbasedontimeseriesandcnngru AT cuichengtao landslidedisplacementpredictionmodelbasedontimeseriesandcnngru AT fengqingqing landslidedisplacementpredictionmodelbasedontimeseriesandcnngru |