Comparison of artificial intelligence algorithms to estimate sustainability indicators

Bienvenido-Huertas, David; Farinha, Fátima; Oliveira, Miguel José; Silva, Elisa; Lança, Rui

doi:10.1016/j.scs.2020.102430

Cited by 19 publications

(9 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5 and 7 ). Overall, the selected case studies confirm the experts´ general belief that SD research can benefit from real-time analysis and automated response by evaluating, managing, and modelling sustainability impacts on the earth systems (Bienvenido-Huertas et al, 2020 ; Steffen et al, 2020 ; Wiedmann et al, 2020 ), developing smart solutions for SD (Alreshidi, 2019 ; Bircanoğlu et al, 2018 ; Kościelniak et al, 2019 ), or even contributing to predicting natural disasters (Al Qundus et al, 2020 ; Alizadeh & Nikoo, 2018 ; Goralski & Tan, 2020 ; Sublime & Kalinicheva, 2019 ). Moreover, adopting AI and digitalisation could increase several stakeholders´ engagement (Fuso Nerini et al, 2018 ), such as the society (Irving & Hoffman, 2014 ; Mrówczyńska et al, 2019 ), supporting the scientific community to overcome the complexity of SD research (Gue et al, 2020 ; Sebestyén et al, 2020 ) or generating valuable information for policy-making (Gao et al, 2019 ; How et al, 2020 ; Jean et al, 2016 ; Mrówczyńska et al, 2019 ; Pirouz et al, 2020 ).…”

Section: Resultssupporting

confidence: 59%

“…Case studies found digitalisation to support climate change adaptation and preparedness (Balogun et al, 2020 ). Comparison of AI algorithms for sustainability assessment (Bienvenido-Huertas et al, 2020 ), environmental evaluation (Liu et al, 2021 ), and text mining analysis (Sebestyén et al, 2020 ) all show the power of digitalisation for SD assessment and management. As earlier stated, AI can be used to achieve sustainability and the SDGs.…”

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

See 1 more Smart Citation

Deploying digitalisation and artificial intelligence in sustainable development research

Filho

Yang

Eustachio

et al. 2022

Environ Dev Sustain

View full text Add to dashboard Cite

Many industrialised countries have benefited from the advent of twenty-first century technologies, especially automation, that have fundamentally changed manufacturing and industrial production processes. The next step in the evolution of automation is the development of artificial intelligence (AI), i.e. intelligence which is demonstrated by machines and systems, which cannot only perform tasks but also work synergistically with humans and nature. Intelligent systems that can see, analyse situations and respond sensitively to real-time cues, from human gestures and facial expressions to pedestrians crossing a busy street, will reshape transportation, precision agriculture, biodiversity conservation, environmental modelling, public health, construction and manufacturing, as well as initiatives designed to promote prosperity on Earth. This paper explores the connections between AI systems and sustainable development (SD) research. By means of a literature review, world survey, and case studies, ways in which AI can support research on SD and, inter alia, contribute to a more sustainable and equitable world, are identified.

show abstract

Section: Resultssupporting

confidence: 59%

Section: Artificial Intelligence and Sustainable Development Researchmentioning

confidence: 99%

Deploying digitalisation and artificial intelligence in sustainable development research

Filho

Yang

Eustachio

et al. 2022

Environ Dev Sustain

View full text Add to dashboard Cite

show abstract

“…Alongside artificial neural networks, regression applications can be used as statistical tools for analyzing or understanding a binary or multivariate relationship [29], [79]. Today, regression is used as a powerful tool in the scientific, commercial, and industrial fields for forecasting, modeling, and optimization [80]- [82]. In the remainder of this section, different types of neural networks and regression methods are discussed.…”

Section: A Artificial Neural Network and Regression Techniquesmentioning

confidence: 99%

Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis

et al. 2022

View full text Add to dashboard Cite

show abstract

“…While static environmental benefits are determined by the construction, manufacture, and transportation prior to purchase, autonomous environmental objectives are provided from post-purchase autonomous interactions between an AI-enhanced product and its environment, which include knowledge and decision-making, and are determined by the design, production, and distribution of products prior to purchase. A domestic robot, for example, may clean the house and its surroundings autonomously using tools and gadgets that it purchases and collects on its own timetable [89,90].…”

Section: Functionalitymentioning

confidence: 99%

Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review

Manzoor

Othman

Durdyev

et al. 2021

ASI

View full text Add to dashboard Cite

The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.

show abstract

Comparison of artificial intelligence algorithms to estimate sustainability indicators

Cited by 19 publications

References 62 publications

Deploying digitalisation and artificial intelligence in sustainable development research

Deploying digitalisation and artificial intelligence in sustainable development research

Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis

Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review

Contact Info

Product

Resources

About