INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN
Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/1091/2022/isprs-archives-XLIII-B3-2022-1091-2022.pdf |
| _version_ | 1828347972693262336 |
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| author | Y. K. Chen Y. T. Lin H. Y. Yen N. H. Chang H. M. Lin K. H. Yang C. S. Chen L. P. Wang H. K. Cheng H. H. Wu J. Y. Han |
| author_facet | Y. K. Chen Y. T. Lin H. Y. Yen N. H. Chang H. M. Lin K. H. Yang C. S. Chen L. P. Wang H. K. Cheng H. H. Wu J. Y. Han |
| author_sort | Y. K. Chen |
| collection | DOAJ |
| description | Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction. |
| first_indexed | 2024-04-14T00:47:55Z |
| format | Article |
| id | doaj.art-6e08a1236e97466d95551353938c2a9a |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-14T00:47:55Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-6e08a1236e97466d95551353938c2a9a2022-12-22T02:21:55ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342022-05-01XLIII-B3-20221091109610.5194/isprs-archives-XLIII-B3-2022-1091-2022INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWANY. K. Chen0Y. T. Lin1H. Y. Yen2N. H. Chang3H. M. Lin4K. H. Yang5C. S. Chen6L. P. Wang7H. K. Cheng8H. H. Wu9J. Y. Han10Department of Civil Engineering, National Taiwan University, Taipei, TaiwanNational Center for Research on Earthquake Engineering, Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, Taipei, TaiwanCECI Engineering Consultants, Inc., Taipei, TaiwanCECI Engineering Consultants, Inc., Taipei, TaiwanDepartment of Civil Engineering, National Taiwan University, Taipei, TaiwanTaiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/1091/2022/isprs-archives-XLIII-B3-2022-1091-2022.pdf |
| spellingShingle | Y. K. Chen Y. T. Lin H. Y. Yen N. H. Chang H. M. Lin K. H. Yang C. S. Chen L. P. Wang H. K. Cheng H. H. Wu J. Y. Han INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN |
| title_full | INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN |
| title_fullStr | INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN |
| title_full_unstemmed | INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN |
| title_short | INTEGRATING INSAR INFORMATION AND SPATIAL-TEMPORAL FACTORS IN MACHINE LEARNING ANALYSIS FOR LANDSLIDE PREDICTION – A CASE STUDY FOR PROVINCIAL HIGHWAY 18 AREA IN TAIWAN |
| title_sort | integrating insar information and spatial temporal factors in machine learning analysis for landslide prediction a case study for provincial highway 18 area in taiwan |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/1091/2022/isprs-archives-XLIII-B3-2022-1091-2022.pdf |
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