A Feasibility Study on Gradient Boosting Regressor for Subsurface Sensor-Based Surface Instability Assessment

Shanelle Aira Rodrigazo, Junhwi Cho, Cherry Rose Godes, Yongseong Kim, Yongjin Kim, Seungjoo Lee and Jaeheum Yeon ()
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Shanelle Aira Rodrigazo: Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
Junhwi Cho: Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
Cherry Rose Godes: Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
Yongseong Kim: Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
Yongjin Kim: Smart E&C, Chuncheon 24341, Republic of Korea
Seungjoo Lee: Department of Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology Goyang-si 10223, Republic of Korea
Jaeheum Yeon: Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea

Land, 2025, vol. 14, issue 3, 1-15

Abstract: 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 2 = 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.

Keywords: gradient boosting regressor; subsurface monitoring; slope stability; urban expansion (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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