Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain)

Abstract. The presented questionnaire study summarizes an evaluation of approaches, techniques and parameters of slope-instability investigation and monitoring of their occurrence, reliability and the applicability of the monitoring techniques for early warning. The study is based on information collected from 86 monitored landslides in 14 European and Asian countries. Based on the responses, lidar ALS (airborne laser scanners), geophysical logging, aerial photographs, resistivity surveying, GB InSAR (ground-based synthetic aperture radar interferometer) and the refraction seismic were considered the most reliable methods for investigation of structure and character of landslides. Especially lidar ALS and geophysical logging were ranked high despite their application at relatively few landslides. Precipitation amount, pore-water pressure and displacement monitored by wire extensometers, dGPS and total stations, followed by air temperature and EM-emissions monitoring and displacement monitored by the TM 71 crack gauge were considered the most promising parameters for early warning.

Section: Discussionsupporting

confidence: 81%

Application and reliability of techniques for landslide site investigation, monitoring and early warning – outcomes from a questionnaire study

Baroň

Supper

2013

“…The evolution process of landslide is a complex nonlinear process caused by the complex interaction of different factors. The accurate prediction of reservoir landslide processes is an important basis for early prevention, and it can reduce the loss of property and lives (Corominas et al, 2005). Therefore, geological surveying, monitoring, landslide prevention and landslide prediction must be improved to minimize the losses caused by landslides (Kirschbaum et al, 2010;Miyagi et al, 2011;Ahmed, 2013).…”

Section: Introductionmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

“…Prediction of the landslide development process is a critical task in landslide research Helmstetter et al, 2004;Corominas et al, 2005;Gao, 2007). Accurate prediction can provide a scientific guide to landslide prewarning and forecast and engineering control as quickly as possible.…”

Section: Introductionmentioning

confidence: 99%

Application of GA–SVM method with parameter optimization for landslide development prediction

Jiming

2014

107

Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multifactor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multifactor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.