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
