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Sustainability
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Utilizing Artificial Intelligence as a Means to Achieve Sustainable Development
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Utilizing Artificial Intelligence as a Means to Achieve Sustainable Development

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 2 July 2024 | Viewed by 998

Special Issue Editors

Prof. Dr. Hongjun Wang

E-Mail Website
Guest Editor
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611715, China
Interests: artificial intelligence and its application; machine learning and its application
Prof. Dr. Peng Jin

E-Mail Website
Guest Editor
School of Electronic Information and Artificial Intelligence, Leshan Normal University, Leshan 614000, China
Interests: Intelligent control; deep learning
Dr. Yanru Chen

E-Mail Website
Guest Editor
School of Computer Science, Sichuan University, Chengdu 610017, China
Interests: industrial control system security; privacy protection; authentication key negotiation protocols; intrusion detection; intelligent internet of things; data intelligence in industrial internet; blockchain

Special Issue Information

Dear Colleagues,

The sustainable development of the world cannot be achieved without the utilization of artificial intelligence, particularly because artificial intelligence has replaced several human mechanical labors, and thus have become an irreversible trend in world development. The indispensable role of artificial intelligence is evidenced by its large-scale application in all aspect of human life, ranging from transport, grid, manufacturing, and construction to education and social media.

In this context, this Special Issue aims to collate research exploring the rapid and close integration of artificial intelligence and sustainable development and the application of such integrated mechanisms across various fields and disciplines.

Potential topics include (but not limited to) the following:

  • Application of artificial intelligence;
  • Application of machine learning;
  • Application of data mining;
  • Application of artificial intelligence in education;
  • Application of artificial intelligence in electric power;
  • Application of artificial intelligence in transportation;
  • Application of artificial intelligence in manufacturing;
  • Application of artificial intelligence in construction;
  • Application of artificial intelligence in city;
  • Application of artificial intelligence in natural language processing;
  • Artificial intelligence interdisciplinary science.

Dr. Hongjun Wang
Prof. Dr. Peng Jin
Dr. Yanru Chen
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. Sustainability 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.

Keywords

  • application of artificial intelligence
  • application of machine learning
  • application of data mining
  • application of artificial intelligence in education
  • application of artificial intelligence in electric power
  • application of artificial intelligence in transportation
  • application of artificial intelligence in manufacturing
  • application of artificial intelligence in construction
  • application of artificial intelligence in city
  • artificial intelligence interdisciplinary science

Published Papers (1 paper)

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Research

30 pages, 59008 KiB  
Article
Managing Rockfall Hazard on Strategic Linear Stakes: How Can Machine Learning Help to Better Predict Periods of Increased Rockfall Activity?
by Marie-Aurélie Chanut, Hermann Courteille, Clara Lévy, Abdourrahmane Atto, Lucas Meignan, Emmanuel Trouvé and Muriel Gasc-Barbier
Sustainability 2024, 16(9), 3802; https://doi.org/10.3390/su16093802 - 30 Apr 2024
Viewed by 687
Abstract
When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of [...] Read more.
When rockfalls hit and damage linear stakes such as roads or railways, the access to critical infrastructures (hospitals, schools, factories …) might be disturbed or stopped. Rockfall risk management often involves building protective structures that are traditionally based on the intensive use of resources such as steel or concrete. However, these solutions are expensive, considering their construction and maintenance, and it is very difficult to protect long linear stakes. A more sustainable and effective risk management strategy could be to account for changes on rockfall activity related to weather conditions. By integrating sustainability principles, we can implement mitigation measures that are less resource-intensive and more adaptable to environmental changes. For instance, instead of solely relying on physical barriers, solutions could include measures such as restriction of access, monitoring and mobilization of emergency kits containing eco-friendly materials. A critical step in developing such a strategy is accurately predicting periods of increased rockfall activity according to meteorological triggers. In this paper, we test four machine learning models to predict rockfalls on the National Road 1 at La Réunion, a key road for the socio-economic life of the island. Rainfall and rockfall data are used as inputs of the predictive models. We show that a set of features derived from the rainfall and rockfall data can predict rockfall with performances very close and almost slightly better than the standard expert model used for operational management. Metrics describing the performance of these models are translated in operational terms, such as road safety or the duration of road closings and openings, providing actionable insights for sustainable risk management practices. Full article
(This article belongs to the Special Issue Utilizing Artificial Intelligence as a Means to Achieve Sustainable Development)
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