Predictions of soil movements using persistence, auto-regression, and neural network models: a case-study in Mandi, India
by Ankush Pathania; Praveen Kumar; Priyanka; Aakash Maurya; Mohit Kumar; Pratik Chaturvedi; Ravinder Singh; K.V. Uday; Varun Dutt
International Journal of Swarm Intelligence (IJSI), Vol. 7, No. 1, 2022

Abstract: The problem of soil movements and associated landslides is common in the areas of Himachal Pradesh State in India due to the hilly terrain. Prediction of soil movements ahead of time may help save lives and infrastructure. Prior research has used machine learning models to predict soil movements but a comparison of different models for soil movement predictions is less explored. Here, we compared various machine learning models like persistence, auto-regression (AR), long-short term memory (LSTM), and multi-layer perceptron (MLP) in their ability of forecasting soil movements on a landslide location. We used data of soil movements collected by a low-cost landslide monitoring system installed at Gharpa landslide in Himachal Pradesh, India. Persistence, AR, MLP, and LSTM models were evaluated to predict downward soil movements along the Gharpa Hill. Root mean squared error (RMSE) metric was used for model evaluation on a 70% training and 30% test data split. Results revealed that the AR and persistence models gives best and second-best results followed by the LSTM and MLP models, respectively. We highlight the implications of our results for time-series forecasting of soil movements in the real world.

Online publication date: Thu, 24-Feb-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Swarm Intelligence (IJSI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com