Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture Radar
Accurate landslide time prediction holds critical significance for ensuring safety and efficient production in open-pit mining operations. While the inverse velocity method serves as a prevalent data-driven forecasting approach, conventional single-point monitoring implementations frequently yield s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7449 |
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| author | Yi Ren Yihai Zhang Zhengxing Yu Mengxiang Ma Shanshan Hou Haitao Ma |
| author_facet | Yi Ren Yihai Zhang Zhengxing Yu Mengxiang Ma Shanshan Hou Haitao Ma |
| author_sort | Yi Ren |
| collection | DOAJ |
| description | Accurate landslide time prediction holds critical significance for ensuring safety and efficient production in open-pit mining operations. While the inverse velocity method serves as a prevalent data-driven forecasting approach, conventional single-point monitoring implementations frequently yield substantial deviations. This study proposes a multi-point collaborative inverse velocity landslide time prediction methodology using nonlinear least squares, which is based on slope radar multi-point group displacement monitoring data. Systematic stability evaluations were conducted for both single-point predictions and multi-point ensemble forecasts. Experimental results demonstrate that single-point-based predictions generally confine errors within 5 h, including the case of traditional smoothing treatments of velocity curves. The developed multi-point collaborative methodology achieves prediction errors below 1 h, with temporal forecast position variations and spatial point quantity adjustments inducing marginal error fluctuations under 2 h based on strict data exclusion. Enhanced data volume implementation significantly improves prediction accuracy and stability. These findings will provide substantive technical references and methodological guidance for advancing landslide temporal prediction research in open-pit mining engineering. |
| format | Article |
| id | doaj-art-e0431f397d1e4ec387b08f4689b74d52 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e0431f397d1e4ec387b08f4689b74d522025-08-20T03:17:52ZengMDPI AGApplied Sciences2076-34172025-07-011513744910.3390/app15137449Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture RadarYi Ren0Yihai Zhang1Zhengxing Yu2Mengxiang Ma3Shanshan Hou4Haitao Ma5China Academy of Safety Science and Technology, Beijing 100012, ChinaChina Academy of Safety Science and Technology, Beijing 100012, ChinaChina Academy of Safety Science and Technology, Beijing 100012, ChinaChina Academy of Safety Science and Technology, Beijing 100012, ChinaChina Academy of Safety Science and Technology, Beijing 100012, ChinaChina Academy of Safety Science and Technology, Beijing 100012, ChinaAccurate landslide time prediction holds critical significance for ensuring safety and efficient production in open-pit mining operations. While the inverse velocity method serves as a prevalent data-driven forecasting approach, conventional single-point monitoring implementations frequently yield substantial deviations. This study proposes a multi-point collaborative inverse velocity landslide time prediction methodology using nonlinear least squares, which is based on slope radar multi-point group displacement monitoring data. Systematic stability evaluations were conducted for both single-point predictions and multi-point ensemble forecasts. Experimental results demonstrate that single-point-based predictions generally confine errors within 5 h, including the case of traditional smoothing treatments of velocity curves. The developed multi-point collaborative methodology achieves prediction errors below 1 h, with temporal forecast position variations and spatial point quantity adjustments inducing marginal error fluctuations under 2 h based on strict data exclusion. Enhanced data volume implementation significantly improves prediction accuracy and stability. These findings will provide substantive technical references and methodological guidance for advancing landslide temporal prediction research in open-pit mining engineering.https://www.mdpi.com/2076-3417/15/13/7449landslide predictionsynthetic aperture radarinverse velocity methodnonlinear least squaresoptimal fitting |
| spellingShingle | Yi Ren Yihai Zhang Zhengxing Yu Mengxiang Ma Shanshan Hou Haitao Ma Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture Radar Applied Sciences landslide prediction synthetic aperture radar inverse velocity method nonlinear least squares optimal fitting |
| title | Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture Radar |
| title_full | Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture Radar |
| title_fullStr | Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture Radar |
| title_full_unstemmed | Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture Radar |
| title_short | Progressive Landslide Prediction Using an Inverse Velocity Method with Multiple Monitoring Points of Synthetic Aperture Radar |
| title_sort | progressive landslide prediction using an inverse velocity method with multiple monitoring points of synthetic aperture radar |
| topic | landslide prediction synthetic aperture radar inverse velocity method nonlinear least squares optimal fitting |
| url | https://www.mdpi.com/2076-3417/15/13/7449 |
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