We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. By closing this message, you are consenting to our use of cookies.

Add your Insight

Manuscript deadline
28 February 2022

Cover image - Geomatics, Natural Hazards and Risk

Geomatics, Natural Hazards and Risk

Special Issue Editor(s)

Professor Biswajeet Pradhan, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Australia
[email protected]

Visit JournalArticles

Explainable landslide modeling through integrating physical and machine-learning models

Heavy and prolonged rainfall and strong earthquakes trigger landslides, causing severe damage to human lives and property. Therefore, the reliable prediction of landslides is essential for hazard and risk mitigation, which can reduce fatalities and economic losses. Currently, physical and machine-learning models are the most-common approaches for landslide modeling. The physical model, which combines susceptibility analysis with soil and rock mechanics, is a suitable method at a local scale, but requires site-specific geotechnical information. Alternatively, the machine-learning model, which uses historical landslides (inventories) and conditioning factors through machine-learning algorithms, is appropriate on a regional scale. However, the availability of adequate training data/inventories and explainability are the main limitations of this method.

These two approaches, in fact, complement each other. The physical model is interpretable and offers the capability of extrapolation beyond observed conditions. In contrast, machine-learning approaches are highly flexible for data adaptation. This special issue covers integrated physical and machine-learning models for explainable spatial landslide prediction and early-warning systems.

Topics of interest include, but are not limited to:

  • The design and development of explainable landslide-susceptibility models.
  • Evaluating and improving common physical and machine-learning landslide-prediction models.
  • New methodologies for integrated landslide-susceptibility modeling.
  • Integrated physical models for landslide predictions.
  • Explainable landslide deep-learning/machine-learning modeling.
  • Susceptibility assessment and mapping, spatial statistics, and visualization of integrated sensors, data, and information.
  • Applications of multi-source remote sensing data and information fusion for landslide modelling.
  • Landslide detection and monitoring.

Looking to Publish your Research?

We aim to make publishing with Taylor & Francis a rewarding experience for all our authors. Please visit our Author Services website for more information and guidance, and do contact us if there is anything we can help with!

Submission Instructions

Geomatics, Natural Hazards & Risk welcomes original articles, review articles, and technical papers.

On submission, please select "yes" to the question "Are you submitting your paper for a specific special issue?" and include "Explainable landslide modeling through integrating physical and machine learning models" as the issue title.

Instructions for AuthorsSubmit an Article