Special Issue "Applications of Artificial Intelligence for Sustainable Development (AAISD)"

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

Deadline for manuscript submissions: 1 September 2021.

Special Issue Editors

Dr. Álvaro Herrero
E-Mail Website
Guest Editor
GICAP Research Group, University of Burgos, Burgos, Spain
Interests: machine learning; unsupervised exploratory projection; neural networks; multiagent systems; case-based reasoning
Dr. Carlos Cambra
E-Mail Website
Guest Editor
GICAP Research Group, University of Burgos, Burgos, Spain
Interests: machine learning; time-series forecasting; IoT; drones; precision agriculture
Dr. Carlos Alonso de Armiño
E-Mail Website
Guest Editor
GICAP Research Group, University of Burgos, Burgos, Spain
Interests: time-series forecasting; clustering; macroeconomy; transport; logistics
Dr. Paweł Ksieniewicz
E-Mail Website
Guest Editor
Wrocław University of Science and Technology, Poland
Interests: hyperspectral image analysis; computational intelligence; LiDAR data processing; soft computing
Special Issues and Collections in MDPI journals
Dr. Paolo Gastaldo
E-Mail Website
Guest Editor
Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture, DITEN, University of Genoa
Interests: machine learning; embedded systems; intelligent systems for robotics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) in general and some of its subfields in particular are being perceived as some of the most promising technologies today. Furthermore, they are being successfully applied to a wide variety of problems in both academia and industry. There has been a constantly growing flow of related research papers, as well as a substantial progress being achieved in real-world, cutting-edge applications.

On the other hand, sustainability has been and still is one of the major concerns at the present time. As a result, the 2030 Agenda for Sustainable Development was adopted by the United Nations Member States as the path to achieve a better and more sustainable future for all. Urgent action is required to address the global challenges we face, such as environmental issues.

The present Special Issue aims at exploring the synergies of these two fields by studying cases of AI being used for social good and its latest advances. It is open to research papers on practical applications of AI for Sustainable Development, based on empirical experiments. Original contributions, including both data and knowledge-driven AI solutions, are cordially welcome. By means of this Special Issue, some fundamental concepts and associated AI techniques applied for sustainability will be further dealt with and promoted.

Dr. Álvaro Herrero
Dr. Carlos Cambra
Dr. Carlos Alonso de Armiño
Dr. Pawel Ksieniewicz
Dr. Paolo Gastaldo
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. 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 1900 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

The topics of interest include but are not limited to:
  • Artificial neural networks (deep and shallow)
  • Case-based reasoning
  • Evolutionary computing
  • Fuzzy computing
  • Hybrid intelligent systems
  • Image processing Intelligent agents and multiagent systems
  • Logics and causal models
  • Machine/automated learning
  • Metaheuristics
  • Natural language processing
  • Probabilistic computing
  • Reasoning under uncertainty
  • Smart planning.
The application fields of interest cover but are not limited to:
  • Biodiversity protection
  • Climate change
  • Desertification and degradation of ecosystems
  • Global warming
  • Green processes and technologies
  • Natural resource management
  • Pollution
  • Sustainable industrial and energy development
  • Disaster risk reduction
  • Geosciences
  • Clean water and sanitation
  • Quality education

Published Papers (3 papers)

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Research

Open AccessArticle
Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control
Sustainability 2021, 13(6), 3235; https://doi.org/10.3390/su13063235 - 15 Mar 2021
Viewed by 210
Abstract
Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One [...] Read more.
Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error. Full article
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Open AccessArticle
Automatic Development of Deep Learning Architectures for Image Segmentation
Sustainability 2020, 12(22), 9707; https://doi.org/10.3390/su12229707 - 20 Nov 2020
Viewed by 525
Abstract
Machine learning is a branch of artificial intelligence that has gained a lot of traction in the last years due to advances in deep neural networks. These algorithms can be used to process large quantities of data, which would be impossible to handle [...] Read more.
Machine learning is a branch of artificial intelligence that has gained a lot of traction in the last years due to advances in deep neural networks. These algorithms can be used to process large quantities of data, which would be impossible to handle manually. Often, the algorithms and methods needed for solving these tasks are problem dependent. We propose an automatic method for creating new convolutional neural network architectures which are specifically designed to solve a given problem. We describe our method in detail and we explain its reduced carbon footprint, computation time and cost compared to a manual approach. Our method uses a rewarding mechanism for creating networks with good performance and so gradually improves its architecture proposals. The application for the algorithm that we chose for this paper is segmentation of eyeglasses from images, but our method is applicable, to a larger or lesser extent, to any image processing task. We present and discuss our results, including the architecture that obtained 0.9683 intersection-over-union (IOU) score on our most complex dataset. Full article
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Open AccessArticle
Revisiting the Contested Role of Natural Resources in Violent Conflict Risk through Machine Learning
Sustainability 2020, 12(16), 6574; https://doi.org/10.3390/su12166574 - 14 Aug 2020
Viewed by 901
Abstract
The integrated character of the sustainable development goals in Agenda 2030, as well as research in environmental security, flag that sustainable peace requires sustainable and conflict-sensitive natural resource use. The precise relationship between the risk for violent conflict and natural resources remains contested [...] Read more.
The integrated character of the sustainable development goals in Agenda 2030, as well as research in environmental security, flag that sustainable peace requires sustainable and conflict-sensitive natural resource use. The precise relationship between the risk for violent conflict and natural resources remains contested because of the interplay with socio-economic variables. This paper aims to improve the understanding of natural resources’ role in the risk of violent conflicts by accounting for complex interactions with socio-economic conditions. Conflict data was analysed with machine learning techniques, which can account for complex patterns, such as variable interactions. More commonly used logistic regression models are compared with neural network models and random forest models. The results indicate that a country’s natural resource features are important predictors of its risk for violent conflict and that they interact with socio-economic conditions. Based on these empirical results and the existing literature, we interpret that natural resources can be root causes of violent intrastate conflict, and that signals from natural resources leading to conflict risk are reflected in and influenced by interacting socio-economic conditions. More specifically, the results show that variables such as access to water and food security are important predictors of conflict, while resource rents and oil and ore exports are relatively less important than other natural resource variables, contrasting what prior research has suggested. Given the potential of natural resource features to act as an early warning for violent conflict, we argue that natural resources should be included in conflict risk models for conflict prevention. Full article
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