281 research outputs found
Landslide displacement forecasting using deep learning and monitoring data across selected sites
Accurate early warning systems for landslides are a reliable
risk-reduction strategy that may significantly reduce fatalities
and economic losses. Several machine learning methods have
been examined for this purpose, underlying deep learning (DL)
models’ remarkable prediction capabilities. The long short-term
memory (LSTM) and gated recurrent unit (GRU) algorithms are
the sole DL model studied in the extant comparisons. However,
several other DL algorithms are suitable for time series forecasting
tasks. In this paper, we assess, compare, and describe seven DL
methods for forecasting future landslide displacement: multi-layer
perception (MLP), LSTM, GRU, 1D convolutional neural network
(1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture
composed of 1D CNN and LSTM (Conv-LSTM). The investigation
focuses on four landslides with different geographic locations,
geological settings, time step dimensions, and measurement
instruments. Two landslides are located in an artificial reservoir
context, while the displacement of the other two is influenced just
by rainfall. The results reveal that the MLP, GRU, and LSTM models
can make reliable predictions in all four scenarios, while the Conv-
LSTM model outperforms the others in the Baishuihe landslide,
where the landslide is highly seasonal. No evident performance
differences were found for landslides inside artificial reservoirs
rather than outside. Furthermore, the research shows that MLP is
better adapted to forecast the highest displacement peaks, while
LSTM and GRU are better suited to model lower displacement
peaks. We believe the findings of this research will serve as a precious
aid when implementing a DL-based landslide early warning
system (LEWS).SUPPORTO
SCIENTIFICO PER L’OTTIMIZZAZIONE, IMPLEMENTAZIONE E
GESTIONE DEL SISTEMA DI MONITORAGGIO CON AGGIORNAMENTO
DELLE SOGLIE DI ALLERTAMENTO DEL FENOMENO
FRANOSO DI SANT’ANDREA – PERAROLO DI CADORE (BL)”
and the Spanish Grant “SARAI, PID2020-116540RB-C21,MCIN/AEI/10.13039/501100011033” and “RISKCOASTInSAR displacement data of the El Arrecife landslideGeohazard Exploitation Platform (GEP) of the European
Space AgencyNoR Projects Sponsorship
(Project ID: 63737
InSAR time series and LSTM model to support early warning detection tools of ground instabilities: mining site case studies
Early alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.Peer ReviewedPostprint (published version
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