The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Sustainability, Journal Year: 2023, Volume and Issue: 15(13), P. 10543 - 10543

Published: July 4, 2023

Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts control essential to lessen these effects safeguard populations. By utilizing its capacity handle massive amounts of data provide accurate forecasts, deep learning has emerged as potent tool for improving prediction control. The current state applications in forecasting management is thoroughly reviewed this work. review discusses variety subjects, such the sources utilized, models used, assessment measures adopted judge their efficacy. It assesses approaches critically points out advantages disadvantages. article also examines challenges with accessibility, interpretability models, ethical considerations prediction. report describes potential directions deep-learning research enhance predictions Incorporating uncertainty estimates into integrating many sources, developing hybrid mix other methodologies, enhancing few these. These goals can help become more precise effective, which will result better plans forecasts. Overall, useful resource academics professionals working on topic management. reviewing art, emphasizing difficulties, outlining areas future study, it lays solid basis. Communities prepare destructive floods by implementing cutting-edge algorithms, thereby protecting people infrastructure.

Language: Английский

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Applications of Artificial Intelligent and Machine Learning Techniques in Image Processing

Advances in computational intelligence and robotics book series, Journal Year: 2023, Volume and Issue: unknown, P. 151 - 173

Published: June 30, 2023

This chapter explores the role of AI and machine learning (ML) in image processing, focusing on their applications. It covers techniques like supervised learning, unsupervised reinforcement deep learning. include rule-based systems, expert fuzzy logic, genetic algorithms. Machine SVM, decision trees, random forests, K-means clustering, PCA. Deep CNN, RNN, GANs are used tasks object recognition, classification, segmentation. The emphasizes impact ML accuracy, efficiency, decision-making. also discusses evaluation metrics performance analysis, emphasizing importance selecting appropriate techniques. addresses ethical considerations, such as fairness, privacy, transparency, human-AI collaboration.

Language: Английский

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Influences of wildfire on the forest ecosystem and climate change: A comprehensive study

Environmental Research, Journal Year: 2023, Volume and Issue: 240, P. 117537 - 117537

Published: Oct. 30, 2023

Language: Английский

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Prevention/mitigation of natural disasters in urban areas

Smart Construction and Sustainable Cities, Journal Year: 2023, Volume and Issue: 1(1)

Published: Aug. 9, 2023

Abstract Preventing/mitigating natural disasters in urban areas can indirectly be part of the 17 sustainable economic and social development intentions according to United Nations 2015. Four types disasters—flooding, heavy rain-induced slope failures/landslides; earthquakes causing structure failure/collapse, land subsidence—are briefly considered this article. With increased frequency climate change-induced extreme weathers, numbers flooding failures/landslides has recent years. There are both engineering methods prevent their occurrence, more effectively early prediction warning systems mitigate resulting damage. However, still cannot predicted an extent that is sufficient avoid damage, developing adopting structures resilient against earthquakes, is, featuring earthquake resistance, vibration damping, seismic isolation, essential tasks for city development. Land subsidence results from human activity, mainly due excessive pumping groundwater, which a “natural” disaster caused by activity. Countermeasures include effective regional and/or national freshwater management local water recycling groundwater. Finally, perspectives risk hazard prevention through enhanced field monitoring, assessment with multi-criteria decision-making (MCDM), artificial intelligence (AI) technology.

Language: Английский

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Fog Computing-Integrated ML-Based Framework and Solutions for Intelligent Systems

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2024, Volume and Issue: unknown, P. 196 - 224

Published: March 18, 2024

The integration of fog computing and machine learning (ML) in digital healthcare has revolutionized patient care, operations, personalized treatment. This chapter explores the potential telemedicine, remote monitoring, It highlights its role addressing data processing challenges, enabling real-time analytics, ensuring secure transmission medical information. Key case studies demonstrate how these integrated solutions are driving innovation industry. combination ML offers a promising avenue for future healthcare, focusing on data-driven decision-making precision medicine.

Language: Английский

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Comprehensive Survey of Artificial Intelligence Techniques and Strategies for Climate Change Mitigation

Energy, Journal Year: 2024, Volume and Issue: 308, P. 132827 - 132827

Published: Aug. 29, 2024

Language: Английский

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Automatic Flood Monitoring Method with SAR and Optical Data Using Google Earth Engine

Water, Journal Year: 2025, Volume and Issue: 17(2), P. 177 - 177

Published: Jan. 10, 2025

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on classification, efficiently automatically processing multi-source imagery to generate reliable inundation maps remains challenging. In this study, a new automatic method, utilizing optical Synthetic Aperture Radar (SAR) imagery, was developed based the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) innovatively combined with Edge Otsu segmentation SAR enhance initial accuracy of area mapping. To more effectively distinguish areas from non-seasonal water bodies, such as lakes, rivers, reservoirs, pre-flood Landsat-8 analyzed. Non-seasonal bodies were classified using multi-index methods body probability distributions, thereby further enhancing method applied catastrophic floods in Poyang Lake, Jiangxi Province, 2020, East Dongting Hunan China, 2024. results demonstrated classification accuracies 92.6% 97.2% mapping during Lake events, respectively. This offers efficient precise information support decision-makers emergency responders, fully demonstrating its substantial potential practical applications.

Language: Английский

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Integrating Machine Vision for Enhanced Biomedical Signal and Image Processing

Algorithms for intelligent systems, Journal Year: 2025, Volume and Issue: unknown, P. 89 - 116

Published: Jan. 1, 2025

Language: Английский

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Big Data in Construction: Current Applications and Future Opportunities

Big Data and Cognitive Computing, Journal Year: 2022, Volume and Issue: 6(1), P. 18 - 18

Published: Feb. 6, 2022

Big data have become an integral part of various research fields due to the rapid advancements in digital technologies available for dealing with data. The construction industry is no exception and has seen a spike being generated introduction disruptive technologies. However, despite availability such technologies, lagging harnessing big This paper critically explores literature published since 2010 identify trends how can benefit from presence tools as computer-aided drawing (CAD) building information modelling (BIM) provide great opportunity researchers further improve infrastructure be developed, monitored, or improved future. gaps existing been explored detailed analysis was carried out different ways which storage work relevance industry. engineering (BDE) statistics are among most crucial steps integrating technology construction. results this study suggest that while studies set stage improving research, integration associated into not very clear. Among future opportunities, safety, site management, heritage conservation, project waste minimization quality improvements key areas.

Language: Английский

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Civil Infrastructure Damage and Corrosion Detection: An Application of Machine Learning

Buildings, Journal Year: 2022, Volume and Issue: 12(2), P. 156 - 156

Published: Feb. 1, 2022

Automatic detection of corrosion and associated damages to civil infrastructures such as bridges, buildings, roads, from aerial images captured by an Unmanned Aerial Vehicle (UAV), helps one overcome the challenges shortcomings (objectivity reliability) with manual inspection methods. Deep learning methods have been widely reported in literature for infrastructure detection. Among them, convolutional neural networks (CNNs) display promising applicability automatic image features less affected noises. Therefore, current study, we propose a modified version deep hierarchical CNN architecture, based on 16 convolution layers cycle generative adversarial network (CycleGAN), predict pixel-wise segmentation end-to-end manner using Bolte Bridge sky rail areas Victoria (Melbourne). The convolutedly designed model proposed study is aggregation multi-scale multilevel while moving low high-level layers, thus reducing consistency loss due inclusion CycleGAN. standard approaches only use last layer, but our architecture differs these uses multiple layers. Moreover, used guided filtering Conditional Random Fields (CRFs) refine prediction results. Additionally, effectiveness was assessed benchmarking data 600 infrastructure. Overall, results show that produced advanced performances when evaluated different methods, including baseline, PSPNet, DeepLab, SegNet. extended method displayed Global Accuracy (GA); Class Average (CAC); mean Intersection Of Union (IOU); Precision (P); Recall (R); F-score values 0.989, 0.931, 0.878, 0.849, 0.818 0.833, respectively.

Language: Английский

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