GNSS Real-Time Warning Technology for Expansive Soil Landslide—A Case in Ningming Demonstration Area

Chen, Zi; Huang, Guanwen; Xie, Wei; Zhang, Yongzhi; Wang, Le

doi:10.3390/rs15112772

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Ma Jun et al [78] set up GNSS monitoring points on both sides of the subgrade slope and judged subgrade landslide distress according to GNSS data. Chen Zi et al [79] considered that the degradation of landslide strength is directly reflected in surface displacement. Based on the GNSS shallow real-time displacement monitoring sequence, a landslide early warning method based on the GNSS displacement rate combined with the GNSS displacement tangent angle model is proposed, and early warning thresholds of different warning levels are designed.…”

Section: Global Navigation Satellite Systemmentioning

confidence: 99%

Review of Sensor-Based Subgrade Distress Identifications

Cheng,

Xie,

Wei

et al. 2024

Sensors

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The attributes of diversity and concealment pose formidable challenges in the accurate detection and efficacious management of distresses within subgrade structures. The onset of subgrade distresses may precipitate structural degradation, thereby amplifying the frequency of traffic incidents and instigating economic ramifications. Accurate and timely detection of subgrade distresses is essential for maintaining and repairing road sections with existing distresses. This helps to prolong the service life of road infrastructure and reduce financial burden. In recent years, the advent of numerous novel technologies and methodologies has propelled significant advancements in subgrade distress detection. Therefore, this review delineates a concentrated examination of subgrade distress detection, methodically consolidating and presenting various techniques while dissecting their respective merits and constraints. By furnishing comprehensive guidance on subgrade distress detection, this review facilitates the expedient identification and targeted treatment of subgrade distresses, thereby fortifying safety and enhancing durability. The pivotal role of this review in bolstering the construction and operational facets of transportation infrastructure is underscored.

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Section: Global Navigation Satellite Systemmentioning

confidence: 99%

Review of Sensor-Based Subgrade Distress Identifications

Cheng,

Xie,

Wei

et al. 2024

Sensors

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“…In this paper, the tangential angle is primarily used as the basis for dividing landslide deformation stages. The accelerated deformation stages are further subdivided into the initial acceleration stage, the medium-term acceleration stage, and the imminent sliding stage, based on previous studies on three-stage landslides (Fan et al, 2019b;Chen et al, 2023;Tan et al, 2023). For judging the stage of landslide deformation development, the tangential angle is a common statistical quantity that can be used to further classify the deformation stage of landslides from a quantitative perspective, enabling early warning.…”

Section: Parameters For Landslide Predictionmentioning

confidence: 99%

Landslide displacement prediction from on-site deformation data based on time series ARIMA model

Wang,

Tang,

Hou

et al. 2023

Front. Environ. Sci.

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Time series Autoregressive Integrated Moving Average (ARIMA) model is often used in landslide prediction and forecasting. However, few conditions have been suggested for the application of ARIMA models in landslide displacement prediction. This paper summarizes the distribution law of the tangential angle in different time periods and analyzes the landslide displacement data by combining wavelet transform. It proposes an applicable condition for the ARIMA model in the field of landslide prediction: when the landslide deformation is in the initial deformation to initial acceleration stage, i.e., the tangential angle of landslide displacement is less than 80°, the ARIMA model has higher prediction accuracy for 24-h landslide displacement data. The prediction results are RMSE = 4.52 mm and MAPE = 2.39%, and the prediction error increases gradually with time. Meanwhile, the ARIMA model was used to predict the 24-h displacements from initial deformation to initial acceleration deformation for the landslide in Guangna Township and the landslide in Libian Gully, and the prediction results were RMSE = 1.24 mm, MAPE = 1.34% and RMSE = 5.43 mm, MAPE = 1.67%, which still maintained high accuracy and thus verified this applicable condition. At the same time, taking the landslide of Libian Gully as an example, the ARIMA model was used to test the displacement prediction effect of the landslide in the Medium-term acceleration stage and the Imminent sliding stage (the tangential angle of landslide displacement is 80° and 85°, respectively). The relative error of displacement data prediction in the Medium-term acceleration stage is within 3%, while the relative error of the prediction value in the Imminent sliding stage is more than 3%, and the error gradually increases with time. This demonstrates that the relative error of the ARIMA model in landslide prediction and forecasting is within 3%. The relative error of the prediction value in the Imminent sliding stage is above 3%, and the error increases gradually with time. Meanwhile, the prediction results are analyzed and it is concluded that the increase in prediction time and tangential angles are the main reasons for the increase in error. The applicable conditions proposed in this study can provide a reference for the application of ARIMA model in landslide prediction and forecast.

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