SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing
Rainfall-induced landslides in Chongqing, a region of significant interest due to its high incidence rate, have traditionally been predicted using empirical rainfall thresholds. However, these approaches suffer from regional limitations and differing levels of accuracy. This paper presents a novel p...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2024.2424423 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846126421584379904 |
|---|---|
| author | Chao He Chaofan Wang Junwen Peng Wenhui Jiang Jing Liu |
| author_facet | Chao He Chaofan Wang Junwen Peng Wenhui Jiang Jing Liu |
| author_sort | Chao He |
| collection | DOAJ |
| description | Rainfall-induced landslides in Chongqing, a region of significant interest due to its high incidence rate, have traditionally been predicted using empirical rainfall thresholds. However, these approaches suffer from regional limitations and differing levels of accuracy. This paper presents a novel prediction method for rainfall thresholds, based on Singular Value Decomposition Long Short-Term Memory (SVD-LSTM) networks, applied to the case of 148 rainfall-induced landslides in Chongqing. By utilizing Singular Value Decomposition (SVD) to decompose Long Short-Term Memory (LSTM) layer weights into two smaller matrices and adding a custom layer to the standard LSTM structure, the SVD-LSTM method reduces the dimensionality of weights in the input and intermediate layers, reducing computational complexity and accelerating model training. This multi-layer grouping concept provides a method to improve the accuracy and efficiency of rainfall threshold prediction for landslides, offering a robust solution to the geographic limitations and accuracy discrepancies inherent in empirical models. |
| format | Article |
| id | doaj-art-5fddfd090a0b4d16b0edfbd905f57862 |
| institution | Kabale University |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-5fddfd090a0b4d16b0edfbd905f578622024-12-12T18:11:17ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2024.2424423SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in ChongqingChao He0Chaofan Wang1Junwen Peng2Wenhui Jiang3Jing Liu4School of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing, ChinaSchool of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing, ChinaSchool of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing, ChinaSchool of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing, ChinaChina Mobile Group Design Institute Co., Ltd, Chongqing, ChinaRainfall-induced landslides in Chongqing, a region of significant interest due to its high incidence rate, have traditionally been predicted using empirical rainfall thresholds. However, these approaches suffer from regional limitations and differing levels of accuracy. This paper presents a novel prediction method for rainfall thresholds, based on Singular Value Decomposition Long Short-Term Memory (SVD-LSTM) networks, applied to the case of 148 rainfall-induced landslides in Chongqing. By utilizing Singular Value Decomposition (SVD) to decompose Long Short-Term Memory (LSTM) layer weights into two smaller matrices and adding a custom layer to the standard LSTM structure, the SVD-LSTM method reduces the dimensionality of weights in the input and intermediate layers, reducing computational complexity and accelerating model training. This multi-layer grouping concept provides a method to improve the accuracy and efficiency of rainfall threshold prediction for landslides, offering a robust solution to the geographic limitations and accuracy discrepancies inherent in empirical models.https://www.tandfonline.com/doi/10.1080/19475705.2024.2424423Rainfall-induced landslidesrainfall thresholdprediction modelSVD-LSTMlandslide predictionsingular value Decomposition |
| spellingShingle | Chao He Chaofan Wang Junwen Peng Wenhui Jiang Jing Liu SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing Geomatics, Natural Hazards & Risk Rainfall-induced landslides rainfall threshold prediction model SVD-LSTM landslide prediction singular value Decomposition |
| title | SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing |
| title_full | SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing |
| title_fullStr | SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing |
| title_full_unstemmed | SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing |
| title_short | SVD-LSTM-based rainfall threshold prediction for rainfall-induced landslides in Chongqing |
| title_sort | svd lstm based rainfall threshold prediction for rainfall induced landslides in chongqing |
| topic | Rainfall-induced landslides rainfall threshold prediction model SVD-LSTM landslide prediction singular value Decomposition |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2024.2424423 |
| work_keys_str_mv | AT chaohe svdlstmbasedrainfallthresholdpredictionforrainfallinducedlandslidesinchongqing AT chaofanwang svdlstmbasedrainfallthresholdpredictionforrainfallinducedlandslidesinchongqing AT junwenpeng svdlstmbasedrainfallthresholdpredictionforrainfallinducedlandslidesinchongqing AT wenhuijiang svdlstmbasedrainfallthresholdpredictionforrainfallinducedlandslidesinchongqing AT jingliu svdlstmbasedrainfallthresholdpredictionforrainfallinducedlandslidesinchongqing |