Interpretable Multivariate Landslide Displacement Forecasting Based on InSAR and Deep Learning: PatchTST with Learnable Channel Fusion.
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| Title: | Interpretable Multivariate Landslide Displacement Forecasting Based on InSAR and Deep Learning: PatchTST with Learnable Channel Fusion. |
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| Authors: | Xia, Zhuge1 (AUTHOR), Liu, Huan2 (AUTHOR), Qian, Kun3 (AUTHOR) kun_qian_academic@163.com, Zhang, Qi4 (AUTHOR), Xiong, Jiacheng5 (AUTHOR), Huang, Qihuan1 (AUTHOR), He, Xiufeng1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1872. 24p. |
| Subjects: | Landslide prediction, Radar interferometry, Deep learning, San Xia Dam (China), Time series analysis |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? PatchTST-Fusion improves multivariate MT-InSAR landslide displacement forecasting. Learnable channel fusion captures coupled effects of displacement, rainfall, and reservoir water level. What are the implications of the main findings? TimeSHAP reveals feature- and time-specific trigger contributions. The framework supports interpretable landslide monitoring and early warning. Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194915005 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Interpretable Multivariate Landslide Displacement Forecasting Based on InSAR and Deep Learning: PatchTST with Learnable Channel Fusion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xia%2C+Zhuge%22">Xia, Zhuge</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Huan%22">Liu, Huan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qian%2C+Kun%22">Qian, Kun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> kun_qian_academic@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Qi%22">Zhang, Qi</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiong%2C+Jiacheng%22">Xiong, Jiacheng</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Qihuan%22">Huang, Qihuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Xiufeng%22">He, Xiufeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1872. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Landslide+prediction%22">Landslide prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+interferometry%22">Radar interferometry</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22San+Xia+Dam+%28China%29%22">San Xia Dam (China)</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? PatchTST-Fusion improves multivariate MT-InSAR landslide displacement forecasting. Learnable channel fusion captures coupled effects of displacement, rainfall, and reservoir water level. What are the implications of the main findings? TimeSHAP reveals feature- and time-specific trigger contributions. The framework supports interpretable landslide monitoring and early warning. Accurate time series forecasting is fundamental to geohazard early warning, yet remains a major challenge. Conventional in situ geotechnical monitoring remains costly and spatially constrained, whereas deep learning applied to remote sensing data has become increasingly prevalent but often suffers from opacity of model decision-making. To address this issue, we propose a Transformer-based forecasting framework, namely PatchTST-Fusion, adapted for multivariate Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) time series. The framework integrates model interpretability analysis through TimeSHAP, providing temporal and feature-level attributions across the input sequence. Landslide deformation time series are first derived from Copernicus Sentinel-1 SAR data. Variational Mode Decomposition is then applied to decompose the non-linear signals into trend, seasonal, and noise components. The denoised displacement series are modeled and forecast using the proposed PatchTST-Fusion, which incorporates rainfall and reservoir water level fluctuations as feature-level drivers. Application to the Daping landslide cluster in the Three Gorges Reservoir Area in China demonstrates that our method captures both the long-term and transient non-linear coupling between deformation and its triggers, surpassing state-of-the-art models including CNN-BiGRU-Attention, Informer and original PatchTST with 7–55% improvements in MAE and 10–52% improvements in RMSE. Beyond predictive gains, feature attribution of environmental triggers via TimeSHAP reveals that rainfall and reservoir regulation exert temporally distinct influences on slope kinematics, with high relative importance concentrated in specific periods and characteristic lagged responses. This interpretable framework provides both enhanced forecasting accuracy and process-based insights, offering a broadly applicable tool for landslide early warning in reservoir regions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18121872 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1872 Subjects: – SubjectFull: Landslide prediction Type: general – SubjectFull: Radar interferometry Type: general – SubjectFull: Deep learning Type: general – SubjectFull: San Xia Dam (China) Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: China Type: general Titles: – TitleFull: Interpretable Multivariate Landslide Displacement Forecasting Based on InSAR and Deep Learning: PatchTST with Learnable Channel Fusion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xia, Zhuge – PersonEntity: Name: NameFull: Liu, Huan – PersonEntity: Name: NameFull: Qian, Kun – PersonEntity: Name: NameFull: Zhang, Qi – PersonEntity: Name: NameFull: Xiong, Jiacheng – PersonEntity: Name: NameFull: Huang, Qihuan – PersonEntity: Name: NameFull: He, Xiufeng IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 12 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
