Landslide displacement forecasting using deep learning and monitoring data across selected sites
Metadata
Show full item recordEditorial
Springer Nature
Materia
Landslide hazard Remote sensing Landslide early warning Landslide forecasting Artificial intelligence
Date
2023-06-30Referencia bibliográfica
Nava, L., Carraro, E., Reyes-Carmona, C. et al. Landslide displacement forecasting using deep learning and monitoring data across selected sites. Landslides (2023). [https://doi.org/10.1007/s10346-023-02104-9]
Sponsorship
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 “RISKCOAST; InSAR displacement data of the El Arrecife landslide; Geohazard Exploitation Platform (GEP) of the European Space Agency; NoR Projects Sponsorship (Project ID: 63737)Abstract
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).