Special Issue "Artificial Intelligence Pathway for Environmental Sustainability: Monitoring, Modeling, and Decision Making"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 30 September 2022.

Special Issue Editors

Dr. Omid Rahmati
E-Mail Website
Guest Editor
Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
Interests: environmental sustainability; geospatial modeling; watershed management; natural disasters
Special Issues and Collections in MDPI journals
Dr. Zahra Kalantari
E-Mail Website
Guest Editor
Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden
Interests: sustainable urban and rural development; climate change; water-related disasters and conflicts; adaptive land-use planning; nature-based solutions; and ecosystem services
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Decisions involving sustainability require scientific information on natural processes and the role of geo-environmental and anthropogenic factors. The concept of environmental sustainability has different facets, for example, involving ecological carrying capacity, natural resources, human activities, natural disasters, and critique of technology. However, environmental processes are complicated and assessing their systems and dynamics is often difficult or even impossible. Geo-environmental modeling, an approach for simultaneously managing and learning about natural processes on the landscape scale, has been widely applied for efficient environmental management for several decades. Artificial intelligence (AI) technology, such as algorithms, data-mining, and statistical models, can provide vital information about the relationships between natural resources, natural disasters (flood, ground subsidence, landslide, debris flow, wildfire, etc.), geo-environmental factors, and human activities, and thus can support adaptive decision making. AI technology allows for the inclusion of knowledge processing (decision support systems) for sustainable natural resource management. This Special Issue will benefit natural, environmental, social, and sustainability scientists, engineers, managers, and other stakeholders with an interest in the GIS-based machine learning modeling of natural disasters and environmental planning. This open access Special Issue welcomes high-quality and innovative scientific papers describing cutting-edge research related to the application of artificial intelligence approaches, GIS, and remote sensing techniques in the study of sustainability-related issues.

Dr. Omid Rahmati
Dr. Zahra Kalantari
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

  • Artificial intelligence
  • Sustainable development
  • Nature-based solutions
  • Geo-environmental modeling
  • Natural disasters
  • Natural resources
  • Ecosystem services

Published Papers (2 papers)

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Research

Article
Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model
Sustainability 2021, 13(4), 1929; https://doi.org/10.3390/su13041929 - 11 Feb 2021
Cited by 1 | Viewed by 540
Abstract
The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on [...] Read more.
The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production. Full article
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Article
A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)
Sustainability 2021, 13(2), 682; https://doi.org/10.3390/su13020682 - 12 Jan 2021
Viewed by 605
Abstract
Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of [...] Read more.
Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods). Full article
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