Planning Landslide Countermeasure Works through Long Term Monitoring and Grey Box Modelling

Abstract

The design of countermeasure works to mitigate landslide risk needs to deal with the multiple unknowns that are linked with soil properties, distribution and rheology. Most of the time, the degree of definition of all these elements is low. Through landslide monitoring, it is possible to acquire signals from the landslide that carry synthetic information about its dynamic. Thus, if it is possible to define a model that is able to link the landslide displacements with the triggering factors and to predict them consistently, that model may be used to evaluate the effect of a countermeasure work directly, bypassing the geomechanical uncertainty. In this paper, an example application of this approach is described. The displacements of a landslide located in North East Italy are connected with the water discharge of the small stream the crosses the landslide body. A countermeasure work that intercepts the discharge of the torrent is expected to reduce the landslide displacements of approximately 70%, with lower costs of construction and smaller impacts on the environment and landscape with respect of other types of structural mitigation works such as slope reprofiling and large retaining walls.

Investigation and Monitoring for Ever-Updating Engineering Geological Models: The Example of the Passo della Morte Landslide System

“…By introducing the concreteness of uncertainty, the design of countermeasure works for landslide risk mitigation becomes an iterative process with explicit levels of reliability associated with their economic and environmental impacts, which is crucial to inform decision makers. For example, following the same methodology, an innovative mitigation strategy has been implemented for Landslide 1-2 at Passo della Morte, where the modelling of the effectiveness of the countermeasure was based solely on the monitoring data [29,30].…”

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

“…Xie et al [104] Laowuji, China Rainfall, toe excavation Total Station LSTM Bossi and Marcato [105] Passo della Morte, Italy Rainfall, groundwater Inclinometer Linear regression Yang et al [106] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS LSTM Miao et al [107] Baishuihe, China Rainfall, reservoir level GNSS, inclinometer GA-SVR, GS-SVR, PSO-SVR Li et al [37] Baishuihe, China Rainfall, reservoir level GNSS LASSO-ELM, Copula (ELM, SVM, RF, kNN) Logar et al [108] Ventor, United Kingdom Rainfall Crackmeter ANN Krkač et al [33] Kostanjek, Croatia Groundwater (change), season GNSS RF Zhou et al [109] Bazimen, China Rainfall, reservoir level GNSS PSO-SVM (GA-SVM, GS-SVM, BPNN) Cao et al [110] Baijiabao, China Rainfall, groundwater, reservoir level GNSS ELM (SVM) Lian et al [111] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS LSSVM, ELM, combination Chen and Zeng [112] Baishuihe, China None GNSS BPNN Du et al [31] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS, inclinometer BPNN Lian et al [113] Buishuihe, China None GNSS EEMD-ELM, M-EEMD-ELM (ANN, BPNN, RBFNN, SVR, ELM) Corominas et al [114] Vallcebre, Spain Groundwater Extensometers Physics Neaupane and Achet [115] Okharpauwa, Nepal…”

“…However, separate modelling of the hydro-meteorological conditions is required, as only effective precipitation can be used in the model. Support Vector Regression (SVR) is a data-driven equivalent of a direct relation, while Bossi and Marcato [105] found a direct relation with river discharge. However, a model with a direct relation between precipitation and displacement does not account for changing soil conditions and associated infiltration dynamics.…”

A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China

“…Pearson product-moment correlation coefficient (PPMCC) analysis and principal component analysis (PCA). In this study, a PPMCC analysis was used to analyze the correlations among the LSM factors of five sub-categories (geomorphology, hydrology, vegetation index, wetness index, and building index), and the factors with a significant correlation were deleted [29]. https://doi.org/10.1371/journal.pone.0229818.g003…”