Back to Search Start Over

BS-LSTM: An Ensemble Recurrent Approach to Forecasting Soil Movements in the Real World

Authors :
Pratik Chaturvedi
K. V. Uday
Varun Dutt
Praveen Kumar
Priyanka Sihag
Source :
Frontiers in Earth Science, Vol 9 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

Machine learning (ML) proposes an extensive range of techniques, which could be applied to forecasting soil movements using historical soil movements and other variables. For example, researchers have proposed recurrent ML techniques like the long short-term memory (LSTM) models for forecasting time series variables. However, the application of novel LSTM models for forecasting time series involving soil movements is yet to be fully explored. The primary objective of this research is to develop and test a new ensemble LSTM technique (called “Bidirectional-Stacked-LSTM” or “BS-LSTM”). In the BS-LSTM model, forecasts of soil movements are derived from a bidirectional LSTM for a period. These forecasts are then fed into a stacked LSTM to derive the next period’s forecast. For developing the BS-LSTM model, datasets from two real-world landslide sites in India were used: Tangni (Chamoli district) and Kumarhatti (Solan district). The initial 80% of soil movements in both datasets were used for model training and the last 20% of soil movements in both datasets were used for model testing. The BS-LSTM model’s performance was compared to other LSTM variants, including a simple LSTM, a bidirectional LSTM, a stacked LSTM, a CNN-LSTM, and a Conv-LSTM, on both datasets. Results showed that the BS-LSTM model outperformed all other LSTM model variants during training and test in both the Tangni and Kumarhatti datasets. This research highlights the utility of developing recurrent ensemble models for forecasting soil movements ahead of time.

Details

Language :
English
ISSN :
22966463
Volume :
9
Database :
OpenAIRE
Journal :
Frontiers in Earth Science
Accession number :
edsair.doi.dedup.....ec555cedf52c1d03f7a7b4dd15617e6e
Full Text :
https://doi.org/10.3389/feart.2021.696792/full