Testing advanced numerical hydro-mechanical models against well-controlled experiments is a critical step in improving our understanding of unsteady granular mass flows, and necessary to provide some domains of validity for any further risk assessment.
To this end, experimental granular collapses were performed to evaluate the sand6 numerical simulator introduced by Daviet & Bertails-Descoubes (2016), which represents the granular medium as an inelastic and dilatable continuum subject to the Drucker-Prager yield criterion in the dense regime, and computes its dynamics using a 3D material point method (MPM). A specificity of this numerical model is to solve such the Drucker-Prager nonsmooth rheology without any regularisation, by leveraging tools from nonsmooth optimisation.
This nonsmooth simulator, which relies on a constant friction coefficient, is able to reproduce with high fidelity various experimental granular collapses over inclined erodible beds, provided the friction coefficient is set to the avalanche angle - and not to the stop angle, as generally done. The results, obtained for two different granular materials and for bed inclinations ranging from 0° to 20°, suggest that a simple constant friction rheology choice remains reasonable for capturing a large variety of granular collapses up to aspect ratios in the order of 10.
Investigating the precise role of the frictional walls by performing experimental and simulated collapses with various channel widths, we find out that, unlike some assumptions formerly made in the literature, the channel width has lower influence than expected on the granular flow and deposit.
The constant coefficient model is extended with a hysteresis model, thereby improving the predictions of the early-stage dynamics of the collapse. This illustrates the potential effects of such phenomenology on transient granular flows, paving the way to more elaborate analysis.

How to cite: Rousseau, G., Métivet, T., Rousseau, H., Daviet, G., and Bertails-Descoubes, F.: Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1600, https://doi.org/10.5194/egusphere-egu23-1600, 2023.

OpenFOAM [1] is a well-known and widely used framework for physical simulations. Its Finite Area Framework allows the depth-integrated simulation of flows on nearly arbitrary surfaces. It was shown that this framework can be applied to snow avalanche simulations in natural terrain [2].

We will present the latest updates to the framework and the implementation of the avalanche module. The module provides not only a model for dense flow avalanches [2], but was lately extended to simulate powder snow avalanches and mixed snow avalanches. Various well-known friction and snow entrainment models are available for use, as well as unique models for deposition and coupling of dense flow and powder cloud layer in mixed snow avalanches. For practical applications, the module provides interfaces and methods for the integration of geographic information systems (GIS) and is fully capable of using raster and shape files for in- and output.

The avalanche module is built to integrate well in the OpenFOAM structure and follows the common user concepts of OpenFOAM. Therefore, users familiar with OpenFOAM should be able to accommodate quickly to the module and to run simulations after a short time. The module is provided as open source and its structure enables and encourages the implementation and experimenting with new ideas. One mayor goal of the module is to reduce the time from model development to model evaluation and application.

The module is hosted and developed collaboratively on develop.openfoam.com/Community/avalanche. We will provide an introduction into the framework and development process and provide interested people pointers on how to get started with the module and how to implement their own ideas.

[1] Weller, H. G., Tabor, G., Jasak, H., & Fureby, C. (1998). A tensorial approach to computational continuum mechanics using object-oriented techniques. Computers in physics, 12(6), 620-631.

[2] Rauter, M., Kofler, A., Huber, A., & Fellin, W. (2018). faSavageHutterFOAM 1.0: depth-integrated simulation of dense snow avalanches on natural terrain with OpenFOAM. Geoscientific Model Development, 11(7), 2923-2939.

How to cite: Rauter, M., Kowalski, J., and Fellin, W.: From Dense Flows to Powder Cloud Simulations: The OpenFOAM Avalanche Module, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4428, https://doi.org/10.5194/egusphere-egu23-4428, 2023.

Debris flows can significantly grow along their flow path by entraining sediments stored in the channel bed and banks. This entrainment process is influenced by various factors such as flow properties (e.g., flow momentum, basal shear stress) and environmental conditions (e.g., soil water saturation, sediment availability). In recent years, different attempts to include the entrainment process in runout models have improved modelled flow properties and runout behavior by empirically linking entrainment volumes to individual modelled flow properties. Linking entrainment to environmental factors, however, has remained challenging.

Here, we aim at implementing and testing the influence of flow path water-saturated conditions in debris-flow runout modelling in a Swiss debris-flow basin (Illgraben). To this end, the modified RAMMS runout model, which includes an empirical algorithm to describe entrainment as a function of basal shear stress (Frank et al., 2015), is coupled with a simple hydrological model to predict soil water saturation. In a first step, the RAMMS model was calibrated for the Illgraben site for seven events with detailed data on erosion/deposition along the fan as well as flow properties at the outflow of the simulation domain (de Haas et al., 2022). In the calibration procedure, the focus was placed on the erosion proportionality factor dz/dtau [m/kPa] (which links the maximum potential erosion depth to the basal shear stress) as it is assumed to be the driving saturation-induced increase of entrained volume. Preliminary results show that in most cases, including the entrainment process improves the reproduction of the flow properties, especially the ‘hydrograph’ front, and that the erosion proportionality factor dz/dt shows a significant degree of variation for different events. In a second step, the relationship between soil moisture conditions and maximum erosion depth expected along the flow path was investigated. The hydrologic conditions are simulated with a conceptual model solving the water balance for the basin’s headwaters. The headwater discharge serves as the water input for the channel on the fan, where an infiltration model is applied, and entrainment is investigated. The presented framework, which could be incorporated into other runout models, is expected to be useful for debris-flow entrainment modelling, as well as for assessing climate change impacts on debris-flow runout.

References

de Haas, T., McArdell, B.W., Nijland, W., Åberg, A.S., Hirschberg, J., Huguenin, P., 2022. Flow and Bed Conditions Jointly Control Debris‐Flow Erosion and Bulking. Geophysical Research Letters 49. https://doi.org/10.1029/2021GL097611

Frank, F., McArdell, B.W., Huggel, C., Vieli, A., 2015. The importance of entrainment and bulking on debris flow runout modeling: examples from the Swiss Alps. Nat. Hazards Earth Syst. Sci. 15, 2569–2583. https://doi.org/10.5194/nhess-15-2569-2015

How to cite: Könz, A. L., Hirschberg, J., McArdell, B., Bartelt, P., and Molnar, P.: Impacts of flow path water-saturation for debris-flow erosion modelling at Illgraben (Switzerland), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5309, https://doi.org/10.5194/egusphere-egu23-5309, 2023.

The purpose of this abstract is to describe a coupled CFD-MPM model that combines soil mechanics (saturated sediments) with fluid mechanics (seawater or air) as well as solid mechanics (structures) to consider interactions between soil, fluid, and structures. With this formulation, the Material Point Method, which models large deformations in porous media and structures in conjunction with the Implicit Continuous-fluid Eulerian Method, which models complex fluid flows, is combined to model large deformations in porous media and structures. The model has been validated through various benchmarks and then it is used to simulate submarine landslides due to earthquakes. It is shown that this model captures the complicated interactions between saturated sediment, seawater, and offshore structures. This allows us to estimate the impact of potential submarine landslides on offshore structures using the model. 

How to cite: Tran, Q. A.: A hybrid MPM-CFD model for simulating earthquake-induced submarine landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-112, https://doi.org/10.5194/egusphere-egu23-112, 2023.

Geophysical mass flows involve granular earth materials surging down natural slopes, one of the major threats to mountainous regions worldwide. Accurate modeling of geophysical mass flows requires closure relations both within the flow (rheology) and at the flow-substrate interface (boundary conditions). However, although recent years have seen significant advances in the modeling of granular flow rheology, our understanding of how flowing granular materials interact with the substrate remains largely elusive. Here, we focus on micro-topography, i.e., geometric base roughness that is about the same size as the grain size, and investigate its effects on the granular flow dynamics as well as the associated closure relations. To systematically vary the base roughness from smooth to rough, we generate the base using immobile particles with varying particle size and spatial arrangement in laboratory experiments (with particle image velocimetry for flow kinematics extraction) and discrete element method simulations. Two granular flow scenarios are considered, including steady-state flow down inclines and granular column collapse. In the first scenario, it is found that basal slip occurs when the base roughness is below a range of intermediate values and a general slip law connecting the slip velocity, the mean flow velocity, and the base roughness is developed. In the second, transient flow scenario, basal slip inevitably occurs even for very rough bases due to inertial effects and a transient basal slip law is proposed to correlate the slip velocity with local flow properties based on kinetic theory arguments. The basal slip laws developed in this work can be readily incorporated as a dynamic boundary condition in continuum modeling of granular flows. In future work, grain-scale mechanisms relevant to more realistic geophysical flows will be investigated, including the feedback effects of pore fluid pressure on the flow mobility during basal sliding and the role of irregular particle shapes in hydro-mechanical modeling of geophysical mass flows.

How to cite: Jing, L., Yang, S., and Kwok, F. C. Y.: Geophysical mass flow over complex micro-topography: from grain-scale mechanics to continuum modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13333, https://doi.org/10.5194/egusphere-egu23-13333, 2023.

Geophysical mass flows typically consist of a granular solid phase having a broad grain size distribution and an interstitial fluid phase. During the flow, particles of larger sizes tend to segregate in the flow and thereby accumulate in the flow surface and front, resulting in dramatic changes in the flow and deposition characteristics, such as enhanced runout distances and stratified deposit patterns. However, current hydro-mechanical modeling of geophysical mass flows often does not consider grain size segregation and the resulting internal heterogeneity of the flow, which can largely compromise the predictability of existing hydro-mechanical models. A major challenge lies in the multiscale nature of grain segregation and its effects on the flow mobility, which requires detailed characterization of segregation mechanics at both the particle and flow levels. Here, we first review recent advances in a multiscale framework in which the driving and resistive forces of segregation on a single intruder particle or a collection of large particles have been formulated based on discrete element method simulations and theoretical analysis. Then, we discuss how these particle-scale forces can be derived toward a continuum formulation for segregation flux modeling and be connected with the flow dynamics in a two-way coupling manner. These physics-based force formulations reflect the micromechanics of segregation and lead to enhanced predictive modeling of particle size dynamics in the granular flow. Finally, we discuss the potential of extending the proposed framework to consider the effects of interstitial fluids and other mechanisms in upscaled hydro-mechanical modelling for more realistic geophysical mass flows.

How to cite: Liu, M. and Jing, L.: Modelling grain size segregation in geophysical mass flows: bridging particle-level forces and continuum models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14147, https://doi.org/10.5194/egusphere-egu23-14147, 2023.

Debris flows are rapid to very rapid flows, made up of a high concentrated mixture of water and sediments. These types of flow are catastrophic natural phenomena affecting mountain areas and causing several property damages and loss of lives every year. The mitigation of these phenomena is then fundamental:  check dams and longitudinal protection walls are among the main structural passive countermeasures. A crucial aspect in the definition of the design criteria for these structures is the analysis of the impact force exerted by a debris flow on them.
From a scientific point of view, the state of the art in this field is quite lacking, despite the relevance of the topic. In the case of impact of a debris surge on a vertical plane normal to the flow direction, according to Armanini and Scotton (1992), two main types of impact may occur. The first type consists of a complete deviation of the flow along the vertical obstacle, assuming a jet-like behavior (Figure 1).  The second type is characterized by the formation of a reflected wave after the impact, which propagates upstream (Figure 2). The analytical solution based on momentum and mass balances in both case is already known (see Armanini 2009 and Armanini et al. 2020) and the comparison between theoretical results and experimental data are quite satisfactory. 
Much less studied is the case of the impact of a debris flow surge on a vertical wall, arranged in an oblique direction with respect to the flow direction, as in the case of lateral protection walls. 
In order to better understand its kinematic characteristics, the phenomenon  has been studied in the Hydraulic Laboratory of the University of Trento. The phenomenon has been reproduced in a channel of variable slope, by releasing a certain volume of fluid and measuring its impact force on a gate situated at the end of the channel at different oblique orientation with respect to flow direction. Several slopes of the channel and concentration of the solid fraction have been investigated. 
When the flow crash into the gate, it is deviated in the vertical direction along the obstacle and forms initially a vertical jet, which is soon deviated in the direction parallel to the gate.
The phenomenon has been theoretically investigated both in the light of the one-dimensional theory of fluid impacts already adopted for the case of impact on a vertical wall arranged orthogonally to the flow, and using a simplified approach derived from the classical two-dimensional theory of Ippen (1951) of the deviations of supercritical currents. The comparison between the predictions of the theory and the experimental data turns out to be quite good.

How to cite: Armanini, A., Fontanari, A., and Sartori, F.: Impact of a debris flow surge on a vertical wall oblique with respect to flow direction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17563, https://doi.org/10.5194/egusphere-egu23-17563, 2023.

The development and application of deterministic models for vegetated slope stability analysis at a local scale is a pivotal issue in international research. Such tools identify mitigation and risk management techniques during increasingly frequent critical rainfall events. In this sense, the SOSlope software, developed by ecorisQ international association (www.ecorisq.org), allows the simulation of hydro-mechanical dynamics that may influence shallow landslides' occurrence, focusing on the progressive activation of root reinforcement in space and time to counteract soil movement. 

This study presents a reconstruction of an artificially triggered landslide in Rüdlingen (Switzerland), carried out during the Triggering Rapid Mass Movements project, aiming for a back-analysis of the hydro-mechanical conditions leading to its triggering. This experiment allows comparing real-scale data on triggering dynamics of shallow landslides with modeling assumptions and results. Detailed measurements during the investigation and following slope failure were used to calibrate the hydro-mechanical input parameters used in SOSlope and evaluate the modeling capability to reproduce the landslide-triggering conditions and behaviors. 

Results show a reasonable reconstruction of the complex dynamics leading to the loss of soil stability. In particular, considering the water effect and the force redistribution dynamics during the triggering. SOSlope can quantify the effect of the root reinforcement spatial distribution and passive earth pressure. In addition to quantifying the maximum value of root reinforcement achieved to counteract soil movement, SOSlope enables observing its progressive activation in space and time. Pore water pressure dynamics show a distinctive trend regarding preferential flows in soil fractures and macropores; the decrease of suction stress due to increased water content in the soil matrix was also observed. SOSlope allows for systemic analysis of the landslide event by evaluating the different phases of change in slope stability and identifying the causes that favored their failure. These results are challenging to understand the shallow landslide triggering dynamics on vegetated slopes, given simplified assumptions through simpler models. This tool could support risk management strategies, including green-based solutions, nearby structures and infrastructure, or reforestation activities for slope stabilization. In the latter case, through the software, the structure, composition, and efficiency of the plantation can be checked. 

Future developments in SOSlope will include the implementation of a triangulated grid mesh to improve computational limitations associated with the raster input data square grid resolution and the inclusion of new tree species for root reinforcement estimation.

How to cite: Murgia, I., Giadrossich, F., Cohen, D., Capra, G. F., and Schwarz, M.: Application of SOSlope to shallow landslide triggering in Rüdlingen (Switzerland), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8895, https://doi.org/10.5194/egusphere-egu23-8895, 2023.

Steep slopes, deforestation, unconsolidated deposits, high annual rainfall, and a highly dissected landscape facilitate the occurrence of landslides in one of the most important Colombian highways “Via al Llano”, frequently causing traffic interruptions. Prior to a susceptibility assessment of the area, a multitemporal inventory is required. Usually, landslides are identified and mapped by visual interpretation of satellite optical and/or aerial images. However, in study areas located in tropical areas such as that of Via al Llano, due to the frequent presence of clouds, a number of images are needed to identify the landslides and estimate the period of their occurrence. Therefore, an automatic detection procedure is indispensable for large tropical areas and multitemporal event inventories. The cloud-based Google Earth Engine (GEE) allows geospatial processing of freely available multi-temporal data. In this work, we perform automatic detection of landslides using the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 (optical images) and the SAR-backscatter change from Sentinel-1 (radar images) over a sector of the Buenavista area, extending for 53km² in the south portion of the “Via al Llano”. Considering a period during which the occurrence of some landslides blocked the highway, images before and after this event were selected for automatic detection, and the results were compared with landslide inventory previously prepared by an expert operator by visual analysis of images available on Google Earth (optical-natural color images). To assess the ability of each method to discriminate between landslides and stable slopes, confusion matrices were calculated. The NDVI-based approach demonstrated an acceptable ability to identify the landslides, although generating a high number of false positives. On the other hand, the SAR-based method exhibits a lower ability to correctly detect the landslide polygons, even if generating a lower number of false positives. This is maybe due to the pattern of predicted positives which mostly consists of isolated pixels; conversely, the NDVI-based approach provides groups of adjacent pixels predicted as positives which better reproduce the shapes of the landslide polygons. Finally, by combining the two approaches and using topographic masks, better accuracy in the automatic mapping of our multitemporal landslide inventories was achieved.

How to cite: Calderon-Cucunuba, L. P. and Conoscenti, C.: Automatic mapping of multitemporal landslide inventories by using open-access Synthetic Aperture Radar and NDVI imagery in Google Earth Engine: a case study of the “Via al Llano” highway (Colombia), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15954, https://doi.org/10.5194/egusphere-egu23-15954, 2023.

Landslides in remote or uninhabited regions can be undocumented, leaving gaps in landslide inventories which are a key input for hazard and risk assessments. This can lead to landslide events being missing from research studies, and contribute to a bias in the events used for training of machine learning models.

In this work we use satellite images, terrain information, and labelled examples of landslides to train a convolutional neural network (U-Net), for the purpose of adding previously undocumented and new landslides to inventories. This model segments the input images and highlights the pixels it labels as landslides.

Our work focusses on landslides with a range of types and triggers, so that the model is exposed to a variety of training data. We describe the key properties of the landslides in the training set, and discuss the implications for future uses of the trained model.

How to cite: Leeming, K., Gonzalez Alvarez, I., Novellino, A., and Taylor, S.: Automatic detection of landslides from satellite images using a range of training events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8446, https://doi.org/10.5194/egusphere-egu23-8446, 2023.

Remote sensing techniques are now widely spread for the early detection of ground deformation, implementation of warning systems in case of imminent landslide triggering, and medium- and long-term slope instability monitoring. The large breadth of data available to the scientific community, associated with processing techniques improved as the data volume was increasing, has led to noticeable developments in the field of remote sensing data processing, using machine learning algorithms and more particularly deep neural networks.

This arsenal of data and techniques is necessary for the present scientific challenges the community of researchers on landslides still have to meet. As landslides can be complex, for risk management and disaster mitigation strategies, it is necessary to have a precise idea of their location, shape, and size to be studied and monitored. The challenge aims to automate landslide detection and mapping, especially through learning methods. Machine learning methods based on Deep Neural Networks have recently been employed for landslide studies and provide promising efficient results for landslide detection [1].

In this study, we propose an original neural network for landslide detection. More precisely, we exploit a fusion network [1] dealing with optical images on the one hand and Digital Elevation Models on the other hand. To improve the results, attention layers [3] (able to stabilize the training and more precise results) as well as mix up techniques [4] (able to generalize more efficiently) are exploited.

The model was trained and tested on the open Bijie landslide dataset.

Keywords: Remote sensing for landslide monitoring and detection, landslide detection, deep neural networks, attention

[1] Ji, S., Yu, D., Shen, C., Li, W., & Xu, Q. (2020). Landslide detection from an open satellite imagery and digital elevation model dataset using attention-boosted convolutional neural networks. Landslides, 17(6), 1337-1352.

[2] Song, W., Li, S., Fang, L., & Lu, T. (2018). Hyperspectral image classification with deep feature fusion network. IEEE Transactions on Geoscience and Remote Sensing, 56(6), 3173-3184.

[3] Niu, Z., Zhong, G., & Yu, H. (2021). A review on the attention mechanism of deep learning. Neurocomputing, 452, 48-62.

[4] Thulasidasan, S., Chennupati, G., Bilmes, J. A., Bhattacharya, T., & Michalak, S. (2019). On mixup training: Improved calibration and predictive uncertainty for deep neural networks. Advances in Neural Information Processing Systems, 32.

How to cite: Lissak, C., Corpetti, T., and Letard, M.: “Fusion network with attention for landslide detection. Application to Bijie landslide open dataset”, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14199, https://doi.org/10.5194/egusphere-egu23-14199, 2023.

The accuracy of landslide hazard models depends on landslide databases for model training and testing. Landslide databases frequently lack information on the underlying triggering mechanism (i.e., earthquake, rainfall), rendering them nearly useless in hazard models.

We created Landsifier, a Python-based unique library with three different machine-Learning frameworks for assessing the likely triggering mechanisms of individual landslides or entire inventories based on landslide 2D platforms and 3D shapes relying on an underlying digital elevation model (DEM). The base method extracts landslide planform properties as a feature space for the shallow learner-random forest (RF). An alternative approach uses 2D landslide images as input for the convolutional neural network deep learning algorithm (CNN). The final framework uses topological data analysis (TDA) to extract features from 3D landslide surfaces, which are then fed into the random forest classifier as a feature space. We tested the developed methods on six inventories spread over Japan. We achieved mean accuracy ranging from 70% to 98%.

Advancing this trigger classifier, we are working on the next generation to classify also the landslide types (i.e., flows, slides, falls, complex) similarly.

How to cite: Ozturk, U., Rana, K., Bhuyan, K., and Malik, N.: Landsifier: A python library to estimate likely triggers and types of landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1131, https://doi.org/10.5194/egusphere-egu23-1131, 2023.

Every year, landslides kill or injure thousands of people worldwide and substantially impact human livelihood. With the increasing number of extreme weather events due to the changing climate, urban sprawl and intensification of human activities, the amount of deadly landslide events is expected to grow. Landslides often occur unexpectedly due to the difficulty of predicting their location and timing. In such cases, providing information on the spatial extent of the landslide hazard is essential for organising and executing first-response actions on the ground.

This study explores the advantages and limitations of using high-resolution Synthetic Aperture Radar (SAR) data from Sentinel-1 within a deep learning framework for rapidly mapping landslide events. The objectives of the research are four-fold: 1) to investigate how Sentinel-1 landslide mapping can be improved using deep learning; 2) to explore if the addition of up to three pre-event scenes could improve the SAR-based classification accuracies; 3) to test if and how much the addition of polarimetric decomposition features and interferometric coherence help to improve classification accuracies; 4) to test if performing data augmentation affects the final results.

We adopt a semantic segmentation model – U-Net, and a novel deep network - U²-Net, to map landslides based on limited but globally distributed landslide inventory data. In total, 306 image patches with 128x128 pixels size were split into 80% for training/validation of the model and 20% for testing it. We calculate radar backscatter information (gamma nought VV and VH), polarimetric decomposition features (alpha angle, entropy, anisotropy) and interferometric coherence between temporally adjacent scenes. The features are calculated for three pre-event scenes and one post-event scene. Copernicus Digital Elevation Model (DEM) data are used to integrate land surface elevation and slope information into the classification process.

Using all Sentinel-1 features, the best result of deep learning model obtained 0.96 for the Dice coefficient on validation data. The landslide detection based on U²-Net gave slightly better results than the U-Net-based approach. The accuracies of models based on one, two or three pre-event scenes did not substantially differ, indicating no added values of increasing pre-event SAR features. Higher accuracies were reached when polarimetric decomposition features were combined with interferometric coherence compared to runs with only radar backscatter. Increasing the sample size using image augmentation methods such as four-directional rotation and flipping helped advance the accuracy.

Future research is directed towards (i) increasing and diversifying the landslide examples, (ii) performing landslide-events-based resampling and (iii) adding pre- and post-event optical data from Sentinel-2.  

How to cite: Orynbaikyzy, A., Albrecht, F., Yao, W., Plank, S., Camero, A., and Martinis, S.: Using Deep Learning for Sentinel-1-based Landslide Mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6884, https://doi.org/10.5194/egusphere-egu23-6884, 2023.

Landslides are a major type of natural hazard that cause significant human and economic losses in mountainous regions worldwide. Optical and synthetic aperture radar (SAR) satellite data are increasingly being used to support landslide investigation due to their multi-spectral and textural characteristics, multi-temporal revisit rates, and large area coverage. Understanding landslide occurrence, kinematics and correlation to external triggering factors is essential for landslide hazard assessment. Landslides are usually triggered by rainfall and thus, are often covered by clouds, which limits the use of optical images only. Exploiting SAR data, and their cloud penetration and all weather measurement capability, provides more precise temporal characterization of landslide kinematics and its occurrence. However, except for a few research studies, the full potential of SAR data for operational landslide analysis are not fully exploited yet. This is a very demanding task, considering the availability of a vast amount of Sentinel-1 data that have been globally available since October 2014.

In this presentation we summarise all the achievements that were made within the framework of MultiSat4SLOWS project (Multi-Satellite imaging for Space-based Landslide Occurrence and Warning Service), financed within the Helmholtz Imaging 2020 call. The project aims on developing a multi-sensor approach for detection and analysis of the landslide occurrence time and its spatial extent using freely available SAR data from Sentinel-1. Within this project,  we generated a reference database based on Sentinel-1 and -2 data for training, testing and validation of deep learning algorithms. The reference database contains various landslide examples that occurred worldwide and include pre- and post-event polarimetric, coherence and backscatter features. Also, we investigated the applicability of SAR/InSAR time-series data for landslide time detection. Finally, we introduce a prototype of a Visual Analytics platform for rapid landslide analysis of spatial and temporal ground deformation patterns and correlation with external triggering factors.

How to cite: Motagh, M., Plank, S., Wang, W., Orynbaikyzy, A., Vassileva, M., and Sips, M.: Advances in landslide analysis by using remote sensing and artificial intelligence (AI): Results from MultiSat4SLOWS project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13292, https://doi.org/10.5194/egusphere-egu23-13292, 2023.

Persistent Scatterer Interferometry (PSI) is a powerful multitemporal A-DInSAR (Advanced Differential Synthetic Aperture Radar Interferometry) technique widely used for monitoring and measuring Earth’s surface displacements over large areas with sub-centimetric precision. The capability to detect ground deformation processes relies on the available PSI spatial density, strictly related to the resolution of the considered sensor and the presence of stable natural and artificial reflectors. A new data fusion approach, developed as part of the “MUSAR” project funded by ASI (Italian Space Agency), integrates multi-band SAR sensors to improve data coverage of PSI data by synthesizing multi-sensor displacement information. The integration of multi-mission PSI generates synthetic measurement points, named Ground Deformation Markers (GD-Markers), featuring vertical (Up-Down) and horizontal (Est-West) components of the displacements. The fusion of PSI data extracted by C-band Sentinel-1 images from the Copernicus initiative and the COSMO-SkyMed constellation in the X-band from ASI contributed to creating a dataset with high information content.

Each GD-Markers cluster with displacement measurements identifies a specific deformation process in the region of interest. After selecting the relevant cluster of points, the deformation processes were classified into different categories (e.g., landslide, subsidence) to improve their understanding and evaluation for mitigating natural-related hazards. This study aimed to develop a machine learning-based classification system, starting from GD-Markers point clouds, which support the automatization of ground displacement identification and characterization. The synthetic points were characterized as individual entities or point clouds, formed by a discrete cluster of points in space, to evaluate the advantage of treating each point independently or incorporating local neighborhood information. The structured point data were analyzed using a supervised Random Forest (RF) approach to evaluate the performance of point cloud classification and categorization for identifying the best initial setting. Each point was assigned a label representing a deformation process in point cloud classification, while one label is provided for the entire point cloud dataset with categorization.

Comparing models’ performances allowed the definition of the best possible approach for classifying the deformation processes observed by GD-Markers point clouds. The analysis assessed the effectiveness of the classification of single points or clusters to identify the optimal setup that achieves an accurate segmentation between adjacent deformation processes. Identifying this initial setting was essential for selecting and developing advanced deep-learning approaches.

How to cite: Masciulli, C., Gaeta, M., Berardo, G., Pantozzi, G., Stefanini, C. A., and Mazzanti, P.: ML-based characterization of PS-InSAR multi-mission point clouds for ground deformation classification, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14546, https://doi.org/10.5194/egusphere-egu23-14546, 2023.

The Jurassic red-strata of the Three Gorges Reservoir Area in China is interbedded of thick siltstone and thin sandy-mudstone and contains many clay minerals, such as montmorillonite and illite, which is water sensitive, weak and expansive, and easy to decompose by water weathering. In particular, due to the seasonal rainfall, development of settlements, and large-scale reservoir impoundment, many slow-moving landslides (e.g., deep rotation and planar landslides) often occur. Notwithstanding, the reconnaissance, updating, and mapping of kinematic features of township area landslides lack the appropriate attention of the government and researchers. Landslide susceptibility mapping is necessary prerequisites for landslide hazard and risk assessment. But a certain proportion of unpredictability is always closely related to modeling. The main objective of this work is to introduce deep ensemble learning into landslide susceptibility assessment to improve the performance of maximum likelihood models. Therefore, the current model construction has focused on three basic classifiers: decision tree, support vector machine, multi-layer perceptron neural network model, and two homogeneous ensemble models: random forest and extreme gradient boosting. Two prominent ensemble techniques—homogeneous/heterogeneous model ensemble and bagging, boosting, stacking ensemble strategy—were applied to implement the deep ensemble learning. Then, thirteen influencing factors were prepared as predictors and dependent variables. The landslide susceptibility maps were validated by the area under the receiver operating characteristic curve. The results of validation showed that the ensemble model shows that the ROC/AUC value is higher than 0.9, which is improved compared with the basic classifiers. Deep ensemble learning focuses more on detecting the landslide susceptibility area with the highest probability of occurrence. The Stacking based RF-XGBoost model obtained the best verification score (AUC=0.955). The comparison between the susceptibility map and landslide inventory data is encouraging as most of the recorded landslide pixels (about 83.3%) are at a high susceptibility level. Besides, from the information gain rate, we found that the Yangtze River and human engineering activities mainly affect the results, which is consistent with the current situation in the study area. The research results in the township-level landslide susceptibility map can also be extended to other urban and rural areas affected by landslides to reduce the landslide disaster risk and formulate further development strategies.

How to cite: Zeng, T., Yin, K., and Wu, L.: Uncertainty research of landslide susceptibility mapping based deep ensemble learning: different basic classifier and ensemble strategy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2445, https://doi.org/10.5194/egusphere-egu23-2445, 2023.

Geophysical mass flows are commonly modelled using depth-averaged (DA) numerical models, which rely on closure relations to account for erosion and deposition. While erosion and deposition are grain scale phenomena, their physics is overlooked due to simplifications required in DA models. In this study, a framework is proposed to transfer the grain-scale physics of erosion and deposition to the continuum scale of DA models. A long short-term memory (LSTM) neural network is coupled with a DA model to incorporate the grain-scale physics of erosion and deposition. As a surrogate model for the closure relation, the LSTM model is trained using computed results from grain-scale Discrete Element Method (DEM) simulations. The surrogate model is evaluated by studying the deposition of an initially flowing granular mass over slope. The effective flow depth h and DA velocity u calculated by the DA-LSTM model are compared with DEM simulation results. The DA-LSTM model is demonstrated to provide more computational efficiency compared to DEM simulations. The newly proposed surrogate model offers a promising approach to calculating more complex closures using deep learning techniques.

How to cite: Nikooei, M. and Edward Choi, C.: A surrogate model for depth-averaged erosion and deposition closures using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9956, https://doi.org/10.5194/egusphere-egu23-9956, 2023.