Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder
Abstract Predicting slope movement has become a great challenge, especially in the Himalayan region, as such natural hazards cause great damage. Machine Learning (ML) models can help in the prediction of landslide hazards. Despite the capabilities of ML models in predicting landslide hazards, most e...
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Nature Portfolio
2025-04-01
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| author | Praveen Kumar Priyanka Priyanka K. V. Uday Varun Dutt |
| author_facet | Praveen Kumar Priyanka Priyanka K. V. Uday Varun Dutt |
| author_sort | Praveen Kumar |
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| description | Abstract Predicting slope movement has become a great challenge, especially in the Himalayan region, as such natural hazards cause great damage. Machine Learning (ML) models can help in the prediction of landslide hazards. Despite the capabilities of ML models in predicting landslide hazards, most existing approaches are deficient in capturing changes in weather conditions at day, hour, or minute scales, thus affecting their accuracy in real-time scenarios. These models also generally have difficulties in generalizing predictions due to limited data availability, and they cannot frequently provide multi-step ahead predictions that are crucial for effective disaster preparedness and timely response. We introduced the hierarchical architecture ML model, specifically the hierarchical transformer prediction autoencoder (H-TPA), which is capable of predicting slope movement with high temporal resolution and enhanced generalization capabilities. This study was based on a rich dataset from sixty-four landslide locations over five years. In this work, we utilize 1,066,009 samples for the training set, which were balanced down to 23,328 samples in order to address class imbalance. The validation set contained 100,000 samples, while the test set was made up of 164,082 samples. This work also presents a VSA methodology for determining threshold values of environmental attributes that trigger slope movements. The performance evaluation of the H-TPA model using this dataset demonstrates very good performance with an F1 score of 0.889, 0.760, and 0.746 for the training, validation, and test datasets, respectively, in predicting slope movements 10 min in advance. Moreover, the present study focused on the analyses of weather condition factors and soil moisture affecting the landslide triggers, which indicated the role of temperature, humidity, barometric pressure, rainfall, and sunlight intensity in small or large slope movements according to certain threshold values. This study generally contributes to the present understanding and enhances the knowledge of landslide prediction in the Himalayan region, besides providing recommendations for geo-scientific knowledge improvement and mitigation strategies. |
| format | Article |
| id | doaj-art-51a2482720da4cdc99b64afb1419a63d |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-51a2482720da4cdc99b64afb1419a63d2025-08-20T03:13:57ZengNature PortfolioScientific Reports2045-23222025-04-0115112010.1038/s41598-025-97147-4Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoderPraveen Kumar0Priyanka Priyanka1K. V. Uday2Varun Dutt3School of Computing and Electrical Engineering, Indian Institute of Technology MandiSchool of Computing and Electrical Engineering, Indian Institute of Technology MandiSchool of Civil and Environmental Engineering, Indian Institute of Technology MandiSchool of Computing and Electrical Engineering, Indian Institute of Technology MandiAbstract Predicting slope movement has become a great challenge, especially in the Himalayan region, as such natural hazards cause great damage. Machine Learning (ML) models can help in the prediction of landslide hazards. Despite the capabilities of ML models in predicting landslide hazards, most existing approaches are deficient in capturing changes in weather conditions at day, hour, or minute scales, thus affecting their accuracy in real-time scenarios. These models also generally have difficulties in generalizing predictions due to limited data availability, and they cannot frequently provide multi-step ahead predictions that are crucial for effective disaster preparedness and timely response. We introduced the hierarchical architecture ML model, specifically the hierarchical transformer prediction autoencoder (H-TPA), which is capable of predicting slope movement with high temporal resolution and enhanced generalization capabilities. This study was based on a rich dataset from sixty-four landslide locations over five years. In this work, we utilize 1,066,009 samples for the training set, which were balanced down to 23,328 samples in order to address class imbalance. The validation set contained 100,000 samples, while the test set was made up of 164,082 samples. This work also presents a VSA methodology for determining threshold values of environmental attributes that trigger slope movements. The performance evaluation of the H-TPA model using this dataset demonstrates very good performance with an F1 score of 0.889, 0.760, and 0.746 for the training, validation, and test datasets, respectively, in predicting slope movements 10 min in advance. Moreover, the present study focused on the analyses of weather condition factors and soil moisture affecting the landslide triggers, which indicated the role of temperature, humidity, barometric pressure, rainfall, and sunlight intensity in small or large slope movements according to certain threshold values. This study generally contributes to the present understanding and enhances the knowledge of landslide prediction in the Himalayan region, besides providing recommendations for geo-scientific knowledge improvement and mitigation strategies.https://doi.org/10.1038/s41598-025-97147-4LandslideMonitoringHierarchical transformerVariable sensitivity analysisEnvironmental factorsMachine learning |
| spellingShingle | Praveen Kumar Priyanka Priyanka K. V. Uday Varun Dutt Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder Scientific Reports Landslide Monitoring Hierarchical transformer Variable sensitivity analysis Environmental factors Machine learning |
| title | Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder |
| title_full | Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder |
| title_fullStr | Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder |
| title_full_unstemmed | Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder |
| title_short | Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder |
| title_sort | improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder |
| topic | Landslide Monitoring Hierarchical transformer Variable sensitivity analysis Environmental factors Machine learning |
| url | https://doi.org/10.1038/s41598-025-97147-4 |
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