A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment
Urban expansion into rural and peri-urban areas increases landslide risks, posing significant threats to infrastructure and public safety. However, most studies focus on surface displacement or meteorological inputs, with less emphasis on subsurface sensor data that could detect early instability pr...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2073-445X/14/3/565 |
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| author | Shanelle Aira Rodrigazo Junhwi Cho Cherry Rose Godes Yongseong Kim Yongjin Kim Seungjoo Lee Jaeheum Yeon |
| author_facet | Shanelle Aira Rodrigazo Junhwi Cho Cherry Rose Godes Yongseong Kim Yongjin Kim Seungjoo Lee Jaeheum Yeon |
| author_sort | Shanelle Aira Rodrigazo |
| collection | DOAJ |
| description | Urban expansion into rural and peri-urban areas increases landslide risks, posing significant threats to infrastructure and public safety. However, most studies focus on surface displacement or meteorological inputs, with less emphasis on subsurface sensor data that could detect early instability precursors. To address these gaps, this study presents a proof-of-concept validation, establishing the feasibility of using subsurface sensor data to predict near-surface slope displacements. A laboratory-scale slope model (300 cm × 50 cm × 50 cm) at a 30° inclination was subjected to simulated rainfall (150 mm/h for 180 s), with displacement measured at depths of 5 cm and 25 cm using PDP-2000 extensometers. The Gradient Boosting Regressor (GBR) effectively captured the nonlinear relationship between subsurface and surface displacements, achieving high predictive accuracy (R<sup>2</sup> = 0.939, MSE = 0.470, MAE = 0.320, RMSE = 0.686). Results demonstrate that, while subsurface sensors do not detect sudden failure events, they effectively capture progressive deformation, offering valuable inputs for multi-sensor EWS in proactive urban planning. Despite demonstrating feasibility, limitations include the controlled laboratory environment and simplified slope conditions. Future work should focus on field-scale validation and multi-sensor fusion to enhance real-world applicability in diverse geological settings. |
| format | Article |
| id | doaj-art-5ba3f7b00d8b4079946fe6988d02b8d9 |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj-art-5ba3f7b00d8b4079946fe6988d02b8d92025-08-20T01:48:41ZengMDPI AGLand2073-445X2025-03-0114356510.3390/land14030565A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability AssessmentShanelle Aira Rodrigazo0Junhwi Cho1Cherry Rose Godes2Yongseong Kim3Yongjin Kim4Seungjoo Lee5Jaeheum Yeon6Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaSmart E&C, Chuncheon 24341, Republic of KoreaDepartment of Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology Goyang-si 10223, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaUrban expansion into rural and peri-urban areas increases landslide risks, posing significant threats to infrastructure and public safety. However, most studies focus on surface displacement or meteorological inputs, with less emphasis on subsurface sensor data that could detect early instability precursors. To address these gaps, this study presents a proof-of-concept validation, establishing the feasibility of using subsurface sensor data to predict near-surface slope displacements. A laboratory-scale slope model (300 cm × 50 cm × 50 cm) at a 30° inclination was subjected to simulated rainfall (150 mm/h for 180 s), with displacement measured at depths of 5 cm and 25 cm using PDP-2000 extensometers. The Gradient Boosting Regressor (GBR) effectively captured the nonlinear relationship between subsurface and surface displacements, achieving high predictive accuracy (R<sup>2</sup> = 0.939, MSE = 0.470, MAE = 0.320, RMSE = 0.686). Results demonstrate that, while subsurface sensors do not detect sudden failure events, they effectively capture progressive deformation, offering valuable inputs for multi-sensor EWS in proactive urban planning. Despite demonstrating feasibility, limitations include the controlled laboratory environment and simplified slope conditions. Future work should focus on field-scale validation and multi-sensor fusion to enhance real-world applicability in diverse geological settings.https://www.mdpi.com/2073-445X/14/3/565gradient boosting regressorsubsurface monitoringslope stabilityurban expansion |
| spellingShingle | Shanelle Aira Rodrigazo Junhwi Cho Cherry Rose Godes Yongseong Kim Yongjin Kim Seungjoo Lee Jaeheum Yeon A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment Land gradient boosting regressor subsurface monitoring slope stability urban expansion |
| title | A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment |
| title_full | A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment |
| title_fullStr | A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment |
| title_full_unstemmed | A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment |
| title_short | A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment |
| title_sort | feasibility study on gradient boosting regressor for subsurface sensor based surface instability assessment |
| topic | gradient boosting regressor subsurface monitoring slope stability urban expansion |
| url | https://www.mdpi.com/2073-445X/14/3/565 |
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