Mitigation of emerging implications of climate change on food production systems

Food Research International, Journal Year: 2020, Volume and Issue: 134, P. 109256 - 109256

Published: April 23, 2020

Language: Английский

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Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

Environmental Science and Ecotechnology, Journal Year: 2023, Volume and Issue: 19, P. 100330 - 100330

Published: Oct. 19, 2023

The recent advancements made in the realms of Artificial Intelligence (AI) and Things (AIoT) have unveiled transformative prospects opportunities to enhance optimize environmental performance efficiency smart cities. These strides have, turn, impacted eco-cities, catalyzing ongoing improvements driving solutions address complex challenges. This aligns with visionary concept smarter an emerging paradigm urbanism characterized by seamless integration advanced technologies strategies. However, there remains a significant gap thoroughly understanding this new intricate spectrum its multifaceted underlying dimensions. To bridge gap, study provides comprehensive systematic review burgeoning landscape eco-cities their leading-edge AI AIoT for sustainability. ensure thoroughness, employs unified evidence synthesis framework integrating aggregative, configurative, narrative approaches. At core lie these subsequent research inquiries: What are foundational underpinnings how do they intricately interrelate, particularly paradigms, solutions, data-driven technologies? key drivers enablers propelling materialization eco-cities? primary that can be harnessed development In what ways contribute fostering sustainability practices, potential benefits offer challenges barriers arise implementation findings significantly deepen broaden our both sustainable urban as well formidable nature pose. Beyond theoretical enrichment, invaluable insights perspectives poised empower policymakers, practitioners, researchers advance eco-urbanism AI- AIoT-driven urbanism. Through insightful exploration contemporary identification successfully applied stakeholders gain necessary groundwork making well-informed decisions, implementing effective strategies, designing policies prioritize well-being.

Language: Английский

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Potential Use of Chat GPT in Global Warming

Annals of Biomedical Engineering, Journal Year: 2023, Volume and Issue: 51(6), P. 1126 - 1127

Published: March 1, 2023

Language: Английский

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The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

AI & Society, Journal Year: 2021, Volume and Issue: 38(1), P. 283 - 307

Published: Oct. 18, 2021

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities AI offers in domain: it can help improve expand current understanding of change, contribute combatting crisis effectively. However, development also raises sets problems when considering change: possible exacerbation social ethical challenges already associated with AI, contribution change greenhouse gases emitted by training data computation-intensive systems. assess carbon footprint research, factors influence AI's gas (GHG) emissions domain. find research may be significant highlight need for more evidence concerning trade-off between GHG generated energy resource efficiency gains offer. light our analysis, argue leveraging offered whilst limiting its risks a gambit which requires responsive, evidence-based, effective governance become winning strategy. conclude identifying European Union as being especially well-placed play leading policy response provide 13 recommendations are designed harness while reducing impact on environment.

Language: Английский

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The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities

Sustainability, Journal Year: 2020, Volume and Issue: 12(20), P. 8548 - 8548

Published: Oct. 15, 2020

The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics behaviors commonly associated with human intelligence. Today, various applications being used areas ranging from marketing to banking finance, agriculture healthcare security, space exploration robotics transport, chatbots creativity manufacturing. More recently, have also started become an integral part many urban services. Urban intelligences manage transport systems cities, run restaurants shops where every day urbanity expressed, repair infrastructure, govern multiple domains such as traffic, air quality monitoring, garbage collection, energy. In age uncertainty complexity that upon us, adoption expected continue, so its impact on sustainability our cities. This viewpoint explores questions lens smart sustainable generates insights into emerging potential symbiosis between urbanism. terms methodology, this deploys thorough review current status cities literature, research, developments, trends, applications. doing, it contributes existing academic debates fields AI. addition, by shedding light uptake seeks help policymakers, planners, citizens make informed decisions about

Language: Английский

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Towards neural Earth system modelling by integrating artificial intelligence in Earth system science

Nature Machine Intelligence, Journal Year: 2021, Volume and Issue: 3(8), P. 667 - 674

Published: Aug. 17, 2021

Language: Английский

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Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

Atmosphere, Journal Year: 2022, Volume and Issue: 13(2), P. 180 - 180

Published: Jan. 23, 2022

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in field climate and numerical weather prediction using Google Scholar search engine. The common topics interest abstracts were identified, some them examined detail: research—photovoltaic wind energy, atmospheric physics processes; research—parametrizations, extreme events, change. With created database, it was also possible to extract commonly meteorological fields (wind, precipitation, temperature, pressure, radiation), (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, XGBoost), countries (China, USA, Australia, India, Germany) these topics. Performing critical reviews literature, authors are trying predict future research direction fields, with main conclusion being that will be a key feature forecasting.

Language: Английский

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Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

Sustainability, Journal Year: 2021, Volume and Issue: 13(16), P. 8952 - 8952

Published: Aug. 10, 2021

Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies have nevertheless either struggled or failed accomplish smart city transformation. This is mainly due short-sighted, technologically determined reductionist approaches being applied complex urbanization problems. Besides this, as underpinned by our ability engage with environments, analyze them, make efficient, sustainable equitable decisions, need for a green approach intensified. perspective paper, reflecting authors’ opinions interpretations, concentrates on “green AI” concept an enabler of transformation, it offers opportunity move away from purely technocentric efficiency solutions towards capable realizing desired futures. The aim this paper two-fold: first, highlight fundamental shortfalls mainstream system conceptualization practice, second, advocate consolidated approach—i.e., AI—to further support methodological includes thorough appraisal current literatures, practices, developments, trends applications. informs authorities planners importance adoption deployment systems that address efficiency, sustainability equity issues cities.

Language: Английский

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A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

Applied Intelligence, Journal Year: 2021, Volume and Issue: 51(9), P. 6497 - 6527

Published: June 11, 2021

Language: Английский

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Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Hydrology and earth system sciences, Journal Year: 2021, Volume and Issue: 25(10), P. 5517 - 5534

Published: Oct. 21, 2021

Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data abundant. Previous studies demonstrated applicability LSTM-based rainfall–runoff modelling; however, LSTMs not been tested on catchments Great Britain (GB). Moreover, opportunities exist to use spatial and seasonal patterns model performances improve our understanding hydrological processes examine advantages disadvantages simulation. By training two LSTM architectures across a large sample 669 GB, we demonstrate that Entity Aware (EA LSTM) simulate discharge with median Nash–Sutcliffe efficiency (NSE) scores 0.88 0.86 respectively. We find outperform suite benchmark conceptual models, suggesting an opportunity additional refine models. In summary, show largest performance improvements north-east Scotland south-east England. The England remained difficult model, part due inability configured this study learn groundwater processes, human abstractions complex percolation properties hydro-meteorological variables typically employed modelling.

Language: Английский

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