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Applied Sciences
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Machine Learning for Landslide Susceptibility
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Machine Learning for Landslide Susceptibility

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 6008

Special Issue Editors

Dr. Suzanne Lacasse

E-Mail Website
Guest Editor
Norwegian Geotechnical Institute, 0855 Oslo, Norway
Interests: risk assessment and management: dams, landslides, offshore foundations, tunneling; machine learning in geotechnics; geotechnical design; laboratory and in situ testing; interpretation of soil parameters
Dr. Zhongqiang Liu

E-Mail Website
Guest Editor
Norwegian Geotechnical Institute, Ullevaal Stadion, P.O. Box 3930, 0806 Oslo, Norway
Interests: offshore foundations; offshore wind turbines; machine learning; reliability-based design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinhui Li

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: slope stability; site investigation; offshore foundations; georisk; geotechnical hazards; monitoring in geotechnical engineering; machine learning; tunneling; early warning…
Dr. Raymond Cheung

E-Mail Website
Guest Editor
Geotechnical Engineering Office, Hong Kong, China
Interests: landslides; risk assessment; reliability and probability in geotechnical engineerings; slope stability; soil nailing; early landslip warning; machine learning

Special Issue Information

Dear Colleagues,

Landslides pose a serious risk to population, property, and environment in  mountainous regions and even in flat areas worldwide. Landslides have caused massive casualties and significant losses and damage to property. In recent years, machine learning (ML) techniques, including deep learning methods, have increasingly been used to model complex landslides. Analyses so far have demonstrated promising predictive ability compared to traditional, deterministic solutions, and physical model testing.

This Special Issue of Applied Sciences seeks to incorporate the latest developments in machine learning with respect to modeling and prediction of landslide susceptibility, including quantitative and qualitative assessments of the classification, volume (or area) and spatial distribution of landslides, as well as the velocity, intensity, and runout (and consequences) of existing or potential landsliding.

Authors are encouraged to submit their latest research and applications in the broad field of “Applications of Machine Learning for Landslide Susceptibility”. Authors are  encouraged to also consider how their models can be disseminated, for example, digitally or by means of equations, so that readers and practitioners can make use of them in their own work.

Dr. Suzanne Lacasse
Dr. Zhongqiang Liu
Prof. Dr. Jinhui Li
Dr. Raymond Cheung
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

16 pages, 4550 KiB  
Article
Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs
by Jiancong Xu, Yu Jiang and Chengbin Yang
Appl. Sci. 2022, 12(12), 6056; https://doi.org/10.3390/app12126056 - 14 Jun 2022
Cited by 17 | Viewed by 2560
Abstract
In order to promptly evacuate personnel and property near the foot of the landslide and take emergency treatment measures in case of sudden danger, it is very necessary to select suitable forecasting methods for conduct short-term displacement predictions in the slope-sliding process. In [...] Read more.
In order to promptly evacuate personnel and property near the foot of the landslide and take emergency treatment measures in case of sudden danger, it is very necessary to select suitable forecasting methods for conduct short-term displacement predictions in the slope-sliding process. In this paper, we used Python to develop the landslide displacement-prediction method based on the eXtreme Gradient Boosting (XGBoost) algorithm, and optimized the hyperparameters through a genetic algorithm to solve the problem of insufficient short-term displacement-prediction accuracy for landslides. We compared the deviation, relative error (RE) and median of RE of predicted values obtained using XGBoost, SVR and RNNs, and the actual value of landslide displacement. The results show that the accuracies of slope displacement prediction using XGBoost and SVR are very high, and that using RNNs is very low during the sliding process. For large displacement values and small numbers of samples, the displacement-prediction effect of XGBoost algorithm is better than that of SVR and RNNs in the sliding process of landslide. There are generally only fewer data samples collected during the landslide sliding process, so RNNs is not suitable for displacement prediction in this scenario. If the number of data samples is large enough, using RNNs to predict the long-term displacement of the slope may also have a much higher accuracy. Full article
(This article belongs to the Special Issue Machine Learning for Landslide Susceptibility)
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24 pages, 5924 KiB  
Article
Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study
by Helen Wai Ming Li, Frankie Leung Chak Lo, Thomas Kwok Chi Wong and Raymond Wai Man Cheung
Appl. Sci. 2022, 12(12), 6017; https://doi.org/10.3390/app12126017 - 13 Jun 2022
Cited by 5 | Viewed by 2143
Abstract
Practical landslide predictions are instrumental to effective landslide risk management. Recently, the use of machine learning (ML) has become a promising alternative means for landslide predictions. This paper discusses the recent progress of a pilot study of ML-powered rainfall-based natural terrain landslide susceptibility [...] Read more.
Practical landslide predictions are instrumental to effective landslide risk management. Recently, the use of machine learning (ML) has become a promising alternative means for landslide predictions. This paper discusses the recent progress of a pilot study of ML-powered rainfall-based natural terrain landslide susceptibility analysis in Hong Kong. This study is different to other similar studies in that: (1) data sampling commonly used to deal with an imbalanced dataset is not adopted, and (2) the incorporation of domain knowledge on landslide characteristics for the development of physically meaningful ML models. The results are found to be promising, with the achieved ROC AUC up to 91.5% based on the testing data. The resolution of the susceptibility map is enhanced by approximately three orders of magnitude further than the introduction of additional features critically selected with feature engineering and based on domain knowledge and past experiences. Full article
(This article belongs to the Special Issue Machine Learning for Landslide Susceptibility)
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