Special Issue "The Artificial Intelligence Models for Landslide Hazard Assessment"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology and Hydrogeology".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editors

Prof. Dr. Francesco Sdao
E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, Campus Macchia Romana, Potenza (Italy)
Interests: engineering geology; landslide hazard and risk assessment; artificial intelligence models; applied hydrogeology; cultural heritages and natural risk; applied geomorphology
Special Issues and Collections in MDPI journals
Assist. Prof. Dr. Filomena Canora
E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, Campus Macchia Romana, Potenza (Italy)
Interests: engineering geology; applied hydrogeology; landslide hazard and risk assessment; artificial intelligence models; applied geomorphology
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to participate in this Special Issue, which will focus primarily on the study and the application of the artificial intelligence methods for landslide hazard assessment and mapping in anthropic areas (urban areas, roads, communication infractures, etc.).

Landslides are geomorphological phenomena due to natural processes and human activities that play a key role in the landscape evolution, mainly in mountainous and hilly environments, and represent a serious hazard in many areas of the world because of its social and economic effects. Landslides are responsible both for direct and indirect damages, may cause loss of life and property, and damage natural resources and hamper infrastructure such as roads, bridges, and communication lines. Additionally, landslides represent one of the greater types of natural disasters that have occurred worldwide in recent years. This trend is expected to continue in the future, due to increased unplanned urbanization and development, continued deforestation, and increased regional precipitation in landslide-prone areas due to climatic change.

The assessement of landslide susceptibility and hazard has been recognized by the scientific community as one of the most significant tools in the landslide and monitoring studies for landslide risk evaluation and its management and mitigation.

Over the last decades, several methods, both qualitative and quantitative, for landslide hazard assessment and mapping have been developed, such as geomorphological approach, heuristic approach, probabilistic and deterministic approach, statistical analysis, and multicriteria decision-making models.

In the last years, artificial intelligence (AI) models are increasingly being utilized as promising tools to evaluate and map landslide hazards. These models are based on innovative methods and techniques, such as artificial neural networks, fuzzy logic, neuro-fuzzy logic, genetic algorithms, machine learning, and expert systems.

This Special Issue aims to collect original contributions on the assessment of landslide hazard in anthropized areas, especially in urban areas or communication lines (roads, railway axes, etc.) or civil engineering works (dams, underground works, pipelines, etc.), using models and methods based on artificial intelligence applications.

Prof. Dr. Francesco Sdao
Assist. Prof. Dr. Filomena Canora
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 papers will be 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. Water 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 2000 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.

Keywords

  • landslide hazard assessment
  • artificial intelligence methods
  • GIS
  • management and mitigation of landslide risk
  • urban areas
  • roads and transportation corridors

Published Papers (1 paper)

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Research

Article
Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China
Water 2019, 11(10), 2144; https://doi.org/10.3390/w11102144 - 15 Oct 2019
Cited by 4 | Viewed by 844
Abstract
The aim of this project was to produce an earthquake–landslide debris flow disaster chain susceptibility map for the Changbai Mountain region, China, by applying data-driven model series and parallel model and Bayesian Networks model. The accuracy of these two models was then compared. [...] Read more.
The aim of this project was to produce an earthquake–landslide debris flow disaster chain susceptibility map for the Changbai Mountain region, China, by applying data-driven model series and parallel model and Bayesian Networks model. The accuracy of these two models was then compared. Parameters related to the occurrence of landslide and debris flow disasters, including earthquake intensity, rainfall, elevation, slope, slope aspect, lithology, distance to rivers, distance to faults, land use, and the normalized difference vegetation index (NDVI), were chosen and applied in these two models. Disaster chain susceptibility zones created using the two models were then contrasted and verified using the occurrence of past disasters obtained from remote sensing interpretations and field investigations. Both disaster chain susceptibility maps showed that the high susceptibility zones are situated within a 10 km radius around the Tianchi volcano, whereas the northern and southwestern sections of the study area comprise primarily very low or low susceptibility zones. The two models produced similar and compatible results as indicated by the outcomes of basic linear correlation and cross-correlation analyses. The verification results of the ROC curves were found to be 0.7727 and 0.8062 for the series and parallel model and BN model, respectively. These results indicate that the two models can be used as a preliminary base for further research activities aimed at providing hazard management tools, forecasting services, and early warning systems. Full article
(This article belongs to the Special Issue The Artificial Intelligence Models for Landslide Hazard Assessment)
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