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Advancing Sustainable Development Through Artificial Intelligence (AI)
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Advancing Sustainable Development Through Artificial Intelligence (AI)

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

Deadline for manuscript submissions: 28 October 2025 | Viewed by 5475

Special Issue Editors

Dr. Hossein Bonakdari

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: water resources; hydrology; AI; climate change; sustainable development; time series; hydrological modelling; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ebrahim Ghaderpour

E-Mail Website
Guest Editor
Department of Earth Sciences & CERI Research Centre, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
Interests: artificial intelligence; big data analytics; geology, hydrology; remote sensing; time series analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Silvio José Gumiere

E-Mail Website
Guest Editor
Department of Soil and Agri-Food Engineering, Universite Laval, Québec, QC G1V 0A6, Canada
Interests: climate change; drought management; soil and water conservation; irrigation; hydrological modeling; surface hydrology; rainfall runoff modeling; hydraulics; numerical modeling; hydrology; hydrologic and water resource management; environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to a Special Issue titled "Advancing Sustainable Development Through Artificial Intelligence (AI)" in the Sustainability journal. As the world faces the pressing challenges of climate change, resource depletion, and social inequality, the role of AI in driving sustainable development has become increasingly significant. AI offers the potential to optimize resource usage, enhance decision-making processes, and build more resilient and equitable systems, making it a critical area of research in our journey toward a sustainable future.

This Special Issue aims to explore the intersection of AI and sustainable development, focusing on how AI technologies can be leveraged to address a broad range of applications while ensuring a cohesive collection of high-impact articles.

We welcome submissions on the following themes:

  • AI for Climate Change Mitigation and Adaptation
  • AI-driven Resource Management (e.g., Water, Energy, Agriculture)
  • Smart Cities and Sustainable Urban Planning
  • AI in Environmental Monitoring and Conservation
  • Ethical and Social Implications of AI in Sustainable Development
  • AI-based Decision Support Systems for Sustainable Practices
  • Integration of AI with IoT for Sustainable Solutions

We look forward to receiving your contributions and showcasing innovative research that advances the field of sustainable development through the application of AI.

Dr. Hossein Bonakdari
Dr. Ebrahim Ghaderpour
Dr. Silvio José Gumiere
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

  • artificial intelligence (AI)
  • sustainable development
  • climate change mitigation
  • resource management
  • environmental monitoring
  • smart cities
  • decision support systems (DSS)
  • Internet of Things (IoT)
  • ethical AI
  • resilient systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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28 pages, 33880 KiB  
Article
F-Segfomer: A Feature-Selection Approach for Land Resource Management on Unseen Domains
by Manh-Hung Nguyen and Chi-Cuong Vu
Sustainability 2025, 17(6), 2640; https://doi.org/10.3390/su17062640 - 17 Mar 2025
Viewed by 481
Abstract
Satellite imagery segmentation is essential for effective land resource management. However, diverse geographical landscapes may limit segmentation accuracy in practical applications. To address these challenges, we propose the F-Segformer network, which incorporates a Variational Information Bottleneck (VIB) module to enhance feature selection within [...] Read more.
Satellite imagery segmentation is essential for effective land resource management. However, diverse geographical landscapes may limit segmentation accuracy in practical applications. To address these challenges, we propose the F-Segformer network, which incorporates a Variational Information Bottleneck (VIB) module to enhance feature selection within the SegFormer architecture. The VIB module serves as a feature selector, providing improved regularization, while SegFormer is well adapted to unseen domains. Combining these methods, our F-Segformer robustly enhanced segmentation performance in new regions that do not appear in the training process. Additionally, we employ Online Hard Example Mining (OHEM) to prioritize challenging samples during training, the setting helps with accelerating model convergence even with the co-trained VIB loss. Experimental results on the LoveDA dataset show that our method can achieve a comparable result to well-known domain-adaptation methods without using data from the target domain. In a practical scenario when the segmentation model is trained on a domain and tested on an unseen domain, our method shows a significant improvement. Last but not least, OHME helps the model converge three times faster than without OHME. Full article
(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))
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24 pages, 4972 KiB  
Article
NDMI-Derived Field-Scale Soil Moisture Prediction Using ERA5 and LSTM for Precision Agriculture
by Elham Koohikeradeh, Silvio Jose Gumiere and Hossein Bonakdari
Sustainability 2025, 17(6), 2399; https://doi.org/10.3390/su17062399 - 9 Mar 2025
Cited by 1 | Viewed by 1225
Abstract
Accurate soil moisture prediction is fundamental to precision agriculture, facilitating optimal irrigation scheduling, efficient water resource allocation, and enhanced crop productivity. This study employs a Long Short-Term Memory (LSTM) deep learning model, integrated with high-resolution ERA5 remote sensing data, to improve soil moisture [...] Read more.
Accurate soil moisture prediction is fundamental to precision agriculture, facilitating optimal irrigation scheduling, efficient water resource allocation, and enhanced crop productivity. This study employs a Long Short-Term Memory (LSTM) deep learning model, integrated with high-resolution ERA5 remote sensing data, to improve soil moisture estimation at the field scale. Soil moisture dynamics were analyzed across six commercial potato production sites in Quebec—Goulet, DBolduc, PBolduc, BNiquet, Lalancette, and Gou-new—over a five-year period. The model exhibited high predictive accuracy, with correlation coefficients (R) ranging from 0.991 to 0.998 and Nash–Sutcliffe efficiency (NSE) values reaching 0.996, indicating strong agreement between observed and predicted soil moisture variability. The Willmott index (WI) exceeded 0.995, reinforcing the model’s reliability. The integration of NDMI assessments further validated the predictions, demonstrating a strong correlation between NDMI values and LSTM-based soil moisture estimates. These findings confirm the effectiveness of deep learning in capturing spatiotemporal variations in soil moisture, underscoring the potential of AI-driven models for real-time soil moisture monitoring and irrigation optimization. This research study provides a scientifically robust framework for enhancing data-driven agricultural water management, promoting sustainable irrigation practices, and improving resilience to soil moisture variability in agricultural systems. Full article
(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))
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18 pages, 4642 KiB  
Article
Sustainable Operation Strategy for Wet Flue Gas Desulfurization at a Coal-Fired Power Plant via an Improved Many-Objective Optimization
by Jianfeng Huang, Zhuopeng Zeng, Fenglian Hong, Qianhua Yang, Feng Wu and Shitong Peng
Sustainability 2024, 16(19), 8521; https://doi.org/10.3390/su16198521 - 30 Sep 2024
Cited by 1 | Viewed by 1800
Abstract
Coal-fired power plants account for a large share of the power generation market in China. The mainstream method of desulfurization employed in the coal-fired power generation sector now is wet flue gas desulfurization. This process is known to have a high cost and [...] Read more.
Coal-fired power plants account for a large share of the power generation market in China. The mainstream method of desulfurization employed in the coal-fired power generation sector now is wet flue gas desulfurization. This process is known to have a high cost and be energy-/materially intensive. Due to the complicated desulfurization mechanism, it is challenging to improve the overall sustainability profile involving energy-, cost-, and resource-relevant objectives via traditional mechanistic models. As such, the present study formulated a data-driven many-objective model for the sustainability of the desulfurization process. We preprocessed the actual operation data collected from the desulfurization tower in a domestic ultra-supercritical coal-fired power plant with a 600 MW unit. The extreme random forest algorithm was adopted to approximate the objective functions as prediction models for four objectives, namely, desulfurization efficiency, unit power consumption, limestone supply, and unit operation cost. Three metrics were utilized to evaluate the performance of prediction. Then, we incorporated differential evolution and non-dominated sorting genetic algorithm-III to optimize the multiple parameters and obtain the Pareto front. The results indicated that the correlation coefficient (R2) values of the prediction models were greater than 0.97. Compared with the original operation condition, the operation under optimized parameters could improve the desulfurization efficiency by 0.25% on average and reduce energy, cost, and slurry consumption significantly. This study would help develop operation strategies to improve the sustainability of coal-fired power plants. Full article
(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))
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Review

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39 pages, 1190 KiB  
Review
The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and Opportunities
by Elda Cina, Ersin Elbasi, Gremina Elmazi and Zakwan AlArnaout
Sustainability 2025, 17(11), 5148; https://doi.org/10.3390/su17115148 - 3 Jun 2025
Viewed by 1107
Abstract
As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers [...] Read more.
As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities. Full article
(This article belongs to the Special Issue Advancing Sustainable Development Through Artificial Intelligence (AI))
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