Initial data from monitoring of a landslide process affecting Emine Cape, Bulgaria

In October 2019, a 3D extensometer was installed to monitor slow landslide movements, affecting the slope of Cape Emine north of the lighthouse. For almost 2 years of observations, a tendency of shrinkage of the zone as a result of lateral pressure from the southern landslide was established. The movements are divided into two stages: the first – until December 2020, and the second – after that date. In the months from April to June the movements in direction X are more intensive due to the rainy situation at this time of the year.

The Disaster Mitigation of Slow-Landslide Movement Induced By Rainfall Based on Ps-Insar Method

Disaster management is a big issue in the past few years. Talking about the disaster, an aspect that should be focussed on is mitigation. The development and the ability of Remote sensing technology have a significant impact on disaster management and significantly contribute to disaster mitigation, such as for the disaster monitoring system. The slow-landslide movement is rarely considered in disaster mitigation, even though the acceleration can increase time by time and will be more dangerous than usual. Therefore, the observation of the remote sensing technology is needed for disaster mitigation. PS-InSAR as a space-based observation method can observe the continuous movement on a site location. Thus, this study illustrates the slow-landslide movement mechanism based on remote sensing technology using the PS-InSAR method compared with rainfall data. In this study, the Sentinel-1 images and STAMPS/MTI by Hooper (2004) successfully detect the displacement rate of the Kalibawang Village, Special Region of Yogyakarta, Indonesia, with the maximum displacement rate -23 mm/year along the Line of Sight (LoS) of the satellite. The PS-InSAR result was also compared with the rainfall data, and shows a correlation of the movement during the rainfall season. Therefore, further mitigation is needed to reduce the risk of the disaster.

Quantifying sediment mass redistribution from joint time-lapse gravimetry and photogrammetry surveys

The accurate quantification of sediment mass redistribution is central to the study of surface processes, yet it remains a challenging task. Here we test a new combination of terrestrial gravity and drone photogrammetry methods to quantify sediment mass redistribution over a 1 km2 area. Gravity and photogrammetry are complementary methods. Indeed, gravity changes are sensitive to mass changes and to their location. Thus, by using photogrammetry data to constrain this location, the sediment mass can be properly estimated from the gravity data. We carried out three joint gravimetry–photogrammetry surveys, once a year in 2015, 2016 and 2017, over a 1 km2 area in southern Taiwan, featuring both a wide meander of the Laonong River and a slow landslide. We first removed the gravity changes from non-sediment effects, such as tides, groundwater, surface displacements and air pressure variations. Then, we inverted the density of the sediment with an attempt to distinguish the density of the landslide from the density of the river sediments. We eventually estimate an average loss of 3.7 ± 0.4 × 109 kg of sediment from 2015 to 2017 mostly due to the slow landslide. Although the gravity devices used in this study are expensive and need week-long surveys, new instrumentation currently being developed will enable dense and continuous measurements at lower cost, making the method that has been developed and tested in this study well-suited for the estimation of erosion, sediment transfer and deposition in landscapes.

Numerical analysis of pipeline response to slow landslides: case study

A numerical analysis is carried out to study the behaviour of pipelines subjected to slow landslides at three at-risk landslide zones of Manitoba Pipeline Network. The pipeline’s longitudinal axis is parallel to the slow landslides at all three research sites. The ground displacements monitored for 5 years are imposed on the pipe using a special purpose pipe–soil interaction element (PSI element) using ABAQUS/Standard. The stiffness of PSI elements is defined based on soil–pipe interface properties according to a 2017 technical report from Pipeline Research Council International Inc. The results of the numerical analysis are compared with the instrumentation data to draw recommendations for future monitoring programs in slow landslide zones.

Quantifying sediment mass redistribution from joint time-lapse gravimetry and photogrammetry surveys

The accurate quantification of sediment mass redistribution is central to the study of surface processes, yet it remains a challenging task. Here we test a new combination of terrestrial gravity and drone photogrammetry methods to quantify sediment redistribution over a 1-km2 area. Gravity and photogrammetry are complementary methods. Indeed, gravity changes are sensitive to mass changes and to their location. Thus, by using photogrammetry data to constrain this location, the sediment mass can be properly estimated from the gravity data. We carried out 3 joint gravity-photogrammetry surveys, once a year in 2015, 2016 and 2017 over a 1-km2 area in southern Taiwan featuring both a wide meander of the Laonong River and a slow landslide. We first removed the gravity changes from non-sediment effects, such as tides, groundwater, surface displacements and air pressure variations. Then, we inverted the density of the sediment, with an attempt to distinguish the density of the landslide from the density of the river sediments. We eventually estimate an average loss of 4.7 ± 0.4 × 109 kg of sediment from 2015 to 2017, mostly due to the slow landslide. Although the gravity devices used in this study are expensive and need week-long surveys, new instrumentation progresses shall enable dense and continuous measurements at lower cost, making this method relevant to improve the estimation of erosion, sediment transfer and deposition in landscapes.

Prediction of rainfall induced landslide movements by artificial neural networks

Many slow to moderate landslides are monitored in order to react on time and prevent loss of lives and reduce material damage. In most of such cases there are very limited data on the geometry, hydrogeological and material properties of the landslide. The aim of the paper is to test the ability of artificial neural networks (ANN) to make reliable short term predictions of rainfall induced landslide movements based on normally available data: rainfall and measured displacements. The back propagation artificial neural network was trained and tested for two sliding phenomena, which are very different in nature. One is moderately moving earthflow and the other very slow landslide, with maximum rate of movements 600 mm/day and 0.094 mm/day, respectively. The results show that in both cases a trained ANN can predict landslide movements with sufficient reliability and can therefore be used together with weather forecast to assist authorities when faced with difficult decisions, such as evacuation. The accuracy of the ANN prediction of movements depends on the type and architecture of ANN as well as on the organisation of the input data used for training, as it is shown by case histories.