Progress in Disaster Science, Journal Year: 2025, Volume and Issue: unknown, P. 100443 - 100443
Published: June 1, 2025
Language: Английский
Journal of the Knowledge Economy, Journal Year: 2025, Volume and Issue: unknown
Published: April 9, 2025
Language: Английский
Citations
0
IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 71 - 110
Published: April 17, 2025
This chapter explores the role of emerging technologies in disaster risk reduction and management (DRRM), focusing on integration Internet Things (IoT), Artificial Intelligence (AI), Data Analytics to enhance urban resilience. IoT-enabled sensors smart infrastructure provide real-time data for early warning systems, monitoring, emergency response. AI-driven predictive analytics enhances assessment, resource allocation, post-disaster recovery, while enables integration, visualization, scenario planning. Despite their potential, challenges like quality, scalability, cybersecurity, ethical concerns must be addressed. The future Disaster Risk Reduction Management (DRRM) will depend incorporation modern technology, increased public involvement, global cooperation, allowing cities develop more intelligent, secure, sustainable settings.
Language: Английский
Citations
0
Published: April 25, 2025
Emerging infectious diseases are a major concern to public health in the United States, requiring advanced surveillance technologies for early diagnosis and response. The incorporation of artificial intelligence (AI)-driven predictive analytics into syndromic represents game-changing technique that uses big data, machine learning, real-time indicators improve disease outbreak detection. purpose this review is explore AI-driven surveillance, emphasizing its ability increase detection emerging States. findings indicate increases speed, accuracy, scalability surveillance. AI-powered methods, such as deep learning natural language processing, may identify anomalies symptom patterns, monitor progression, predict epidemics more accurately. However, with proper safety measures place, AI has potential transform increasing likely national preparedness threats.
Language: Английский
Citations
0
Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100982 - 100982
Published: May 1, 2025
Language: Английский
Citations
0
Environments, Journal Year: 2025, Volume and Issue: 12(5), P. 158 - 158
Published: May 10, 2025
Rapid population growth and climate change have created challenges for managing water quality. Protecting sources devising practical solutions are essential restoring impaired inland rivers. Traditional quality monitoring forecasting methods rely on labor-intensive sampling analysis, which often costly. In recent years, real-time monitoring, remote sensing, machine learning significantly improved the accuracy of forecasting. This paper categorizes approaches into traditional, deep learning, hybrid models, evaluating their performance in parameters. long short-term memory (LSTMs), gated recurrent units (GRUs) LSTM- GRU-based models been widely used river Combining sensing with a network has enhanced data collection efficiency by capturing spatial variability within network, complementing high temporal resolution situ measurements, improving overall robustness predictive models. Additionally, leveraging weather prediction can further enhance better decision-making resource management.
Language: Английский
Citations
0
Progress in Disaster Science, Journal Year: 2025, Volume and Issue: unknown, P. 100443 - 100443
Published: June 1, 2025
Language: Английский
Citations
0