AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change

Computational Urban Science, Journal Year: 2023, Volume and Issue: 3(1)

Published: July 17, 2023

Abstract Climate change is one of the most pressing global challenges we face today. The impacts rising temperatures, sea levels, and extreme weather events are already being felt around world only expected to worsen in coming years. To mitigate adapt these impacts, need innovative, data-driven solutions. Artificial intelligence (AI) has emerged as a promising tool for climate adaptation, offering range capabilities that can help identify vulnerable areas, simulate future scenarios, assess risks opportunities businesses infrastructure. With ability analyze large volumes data from models, satellite imagery, other sources, AI provide valuable insights inform decision-making us prepare change. However, use adaptation also raises important ethical considerations potential biases must be addressed. As continue develop deploy solutions, it crucial ensure they transparent, fair, equitable. In this context, article explores latest innovations directions AI-enabled strategies, highlighting both benefits considered. By harnessing power work towards more resilient, sustainable, equitable all.

Language: Английский

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Artificial Intelligence in Environmental Monitoring: Advancements, Challenges, and Future Directions

Hygiene and Environmental Health Advances, Journal Year: 2024, Volume and Issue: unknown, P. 100114 - 100114

Published: Oct. 1, 2024

Language: Английский

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Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts

Ecotoxicology and Environmental Safety, Journal Year: 2024, Volume and Issue: 280, P. 116532 - 116532

Published: June 7, 2024

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, contributing myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts air utilizing cutting-edge research methodologies big data analytics offer comprehensive overview. It highlights emergence new pollutants, their sources, characteristics, thereby broadening our understanding contemporary quality challenges. The detrimental effects pollution are examined thoroughly, emphasizing both short-term long-term impacts. Particularly vulnerable identified, underscoring need for targeted risk assessments interventions. presents an in-depth analysis global disease burden attributable offering comparative perspective illuminates varying different regions. Furthermore, it addresses economic ramifications quantifying losses, discusses implications public policy care systems. Innovative intervention measures explored, including case studies demonstrating effectiveness. paper also brings light recent discoveries insights in field, setting stage future directions. calls international cooperation tackling underscores crucial role awareness education mitigating its exploration serves not only as scientific discourse but clarion call action against invisible insidious making vital read researchers, policymakers, general public.

Language: Английский

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Artificial neural network an innovative approach in air pollutant prediction for environmental applications: A review

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102305 - 102305

Published: May 22, 2024

Air pollution in the environment is growing daily as a result of urbanization and population growth, which causes numerous health issues. Information about air quality environmental risks provided by pollutant data crucial for management. The use artificial neural network (ANN) approaches predicting pollutants reviewed this research. These methods are based on several forecast intervals, including hourly, daily, monthly ones. This study shows that ANN techniques contaminants more precisely than traditional methods. It has been discovered input parameters architecture-type algorithms used affect accuracy prediction models. therefore accurate reliable other empirical models because they can handle wide range meteorological parameters. Finally, research gap networks identified. review may inspire researchers to certain extent promote development intelligence prediction.

Language: Английский

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AI-driven participatory environmental management: Innovations, applications, and future prospects

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 373, P. 123864 - 123864

Published: Jan. 1, 2025

The rapid advancement of Artificial Intelligence (AI) presents unprecedented opportunities for participatory environmental management. This paper explores the integration AI technologies into approaches, which engage diverse stakeholders in decision-making processes. Using artificial intelligence, a corpus 80 papers was compiled and subsequently analyzed with text mining tools. By identifying systematizing academics' contributions to knowledge about AI-driven tools, this study also discusses challenges ethical considerations inherent deployment, emphasizing need transparent, equitable, accountable systems. Looking ahead, we outline future prospects management, focusing on potential foster adaptive management strategies, enhance stakeholder collaboration, support sustainable development goals.

Language: Английский

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An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 866, P. 161336 - 161336

Published: Jan. 2, 2023

Language: Английский

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International and Constitutional Efforts to Protect the Environment Through the Use of Artificial Intelligence Techniques

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown, P. 387 - 398

Published: Jan. 16, 2024

The purpose of the study is to demonstrate role artificial intelligence in ability accelerate global efforts protect environment and conserve natural resources by monitoring air pollution energy emissions. Monitoring attacks on forest areas, as well describing constitutions world's countries, including Jordanian Constitution, adding constitutional texts addressing environmental protection using technologies. importance this research lies demonstrating international technologies accelerating local environment, resources, use digital tools monitor And countries' constitutions,

Language: Английский

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Enhancing Roundabout Efficiency Through Autonomous Vehicle Coordination

Vehicles, Journal Year: 2025, Volume and Issue: 7(1), P. 9 - 9

Published: Jan. 24, 2025

The paper discusses the potential for autonomous vehicles to improve traffic flow on roundabouts, suggesting that their ability slow down strategically can enhance and reduce pollution both main yielding roads. A simulator a roundabout was developed busy intersection of new city neighborhood. We consider some cars are self-driving, they fully aware scenario. By optimizing speed timing reduction, these help maintain balance between number time crossing This study evaluates effectiveness intervention, demonstrating significantly efficiency, reducing congestion pollution. application genetic algorithms is highlighted as an effective optimization method find right vehicle’s reduction ratio combination road efficiency.

Language: Английский

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Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

Diagnostics, Journal Year: 2023, Volume and Issue: 13(4), P. 814 - 814

Published: Feb. 20, 2023

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying diagnosing pneumonia is X-ray imaging. However, early discrimination difficult radiologists doctors because similarities between tuberculosis. As a result, patients do not receive proper care, which in turn does prevent from spreading. The goal this study to extract hybrid features using variety techniques order achieve promising results differentiating In study, several approaches identification distinguishing were suggested. first proposed system uses techniques, VGG16 + support vector machine (SVM) ResNet18 SVM. second an artificial neural network (ANN) based on integrating ResNet18, before after reducing high dimensions principal component analysis (PCA) method. third ANN separately with handcrafted extracted by local binary pattern (LBP), discrete wavelet transform (DWT) gray level co-occurrence matrix (GLCM) algorithms. All systems have achieved superior differentiation LBP, DWT GLCM (LDG) reached accuracy 99.6%, sensitivity 99.17%, specificity 99.42%, precision 99.63%, AUC 99.58%.

Language: Английский

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Evaluating the Impact of AI-Based Sustainability Measures in Industry 5.0: A Longitudinal Study

BIO Web of Conferences, Journal Year: 2024, Volume and Issue: 86, P. 01058 - 01058

Published: Jan. 1, 2024

In the context of Industry 5.0, this long-term study assesses significant influence AI-based sustainability metrics. It also illuminates a novel paradigm in which artificial intelligence (AI) and human expertise work together to jointly drive sustainability, financial performance, employee satisfaction, overall ecological responsibility. AI-driven efforts produced surprising 12% reduction trash creation, an amazing 7% energy usage, 8% drop CO2 emissions over five-year period. Financially speaking, these actions showed up as steady 4% annual revenue growth, $2 million cost reductions on average each year, cumulative 3.4% gain return investment. The factor is even more notable, with satisfaction ratings rising from 4.2 4.7 work-life balance scores significantly 4.1 4.6. By 2024, 70% workers will have adopted AI, demonstrating how essential AI has become working. An all-encompassing score that included dynamic components increased 60 75 indicating general improvement sustainability. This emphasizes mutually beneficial relationship between 5.0. shows fosters sustainable balanced industrial future by improving environmental responsibility workforce while producing benefits.

Language: Английский

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