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<p>Between 2018 and 2020, several landslides occurred on solar power plants constructed on hillsides after extreme rainfall in South Korea. This study presents a physics-informed machine-learning model to conduct real-time physically-based landslide susceptibility assessment on solar panels installed on mountains. Through a statistical filtering process, fourteen landslide triggering factors related to the topography, soil geotechnical properties, soil hydrological properties, meteorological effects, and solar panels model were selected. While accounting for the presence of solar panels, 136,262 numerical simulations of rainfall infiltration transient seepage and slope stability analyses were performed. Among three machine learning models (random forest, support vector regression, and multi-layer perceptron) developed from numerical simulation data points, the multi-layer perceptron (MLP) model showed the highest prediction accuracy (R<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="11069_2025_7579_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="8" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>2</mn> </mmultiscripts> </math></EquationSource> </InlineEquation> = 96% and mean square error = 0.001) without indicating overfitting. The sensitivity analysis on the developed MLP model indicated that the soil strength properties strongly influenced the factors of safety (FOS), but hydraulic properties showed a relatively small impact. The developed MLP model was applied to a solar power plant in Jangsu-gun, Jeonbuk-do, South Korea, to verify the model’s predictability and showed the applicability of the developed MLP model for physically-based landslide susceptibility assessment. Furthermore, the Jangsu-gun example demonstrated the potentiality of the MLP model for a landslide early warning system (LEWS) for mountainous solar power plants thanks to its fast computational speed at predicting real-time FOS.</p>

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Physics-based landslide susceptibility machine learning model for mountainous solar power plants

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Enok Cheon,
  • Jeong-Yeon Yu,
  • Hwan-Hui Lim,
  • Seung-Rae Lee,
  • Tae-Hyuk Kwon,
  • Ki-Il Song

摘要

Between 2018 and 2020, several landslides occurred on solar power plants constructed on hillsides after extreme rainfall in South Korea. This study presents a physics-informed machine-learning model to conduct real-time physically-based landslide susceptibility assessment on solar panels installed on mountains. Through a statistical filtering process, fourteen landslide triggering factors related to the topography, soil geotechnical properties, soil hydrological properties, meteorological effects, and solar panels model were selected. While accounting for the presence of solar panels, 136,262 numerical simulations of rainfall infiltration transient seepage and slope stability analyses were performed. Among three machine learning models (random forest, support vector regression, and multi-layer perceptron) developed from numerical simulation data points, the multi-layer perceptron (MLP) model showed the highest prediction accuracy (R \(^{2}\) 2 = 96% and mean square error = 0.001) without indicating overfitting. The sensitivity analysis on the developed MLP model indicated that the soil strength properties strongly influenced the factors of safety (FOS), but hydraulic properties showed a relatively small impact. The developed MLP model was applied to a solar power plant in Jangsu-gun, Jeonbuk-do, South Korea, to verify the model’s predictability and showed the applicability of the developed MLP model for physically-based landslide susceptibility assessment. Furthermore, the Jangsu-gun example demonstrated the potentiality of the MLP model for a landslide early warning system (LEWS) for mountainous solar power plants thanks to its fast computational speed at predicting real-time FOS.