Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 121, С. 105982 - 105982

Опубликована: Фев. 22, 2023

Язык: Английский

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Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions

Journal of Hydrology, Год журнала: 2023, Номер 628, С. 130458 - 130458

Опубликована: Ноя. 15, 2023

Язык: Английский

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A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis

Engineering With Computers, Год журнала: 2023, Номер 40(3), С. 1501 - 1516

Опубликована: Авг. 8, 2023

Abstract There is an increasing interest in creating high-resolution 3D subsurface geo-models using multisource retrieved data, i.e., borehole, geophysical techniques, geological maps, and rock properties, for emergency managements. However, dedicating meaningful, thus interpretable views from such integrated heterogeneous data requires developing a new methodology convenient post-modeling analyses. To this end, the current paper hybrid ensemble-based automated deep learning approach modeling of bedrock proposed. The uncertainty then was quantified novel ensemble randomly deactivating process implanted on jointed weight database. applicability capturing optimum topology validated by geo-model laser-scanned bedrock-level Sweden. In comparison with intelligent quantile regression traditional geostatistical interpolation algorithms, proposed showed higher accuracy visualizing post-analyzing model. Due to use multi-source presented here subsequently created model can be representative reconcile geoengineering applications.

Язык: Английский

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Meta-transfer learning-based method for multi-fault analysis and assessment in power system

Applied Intelligence, Год журнала: 2024, Номер unknown

Опубликована: Сен. 10, 2024

Язык: Английский

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Neural Computing and Applications, Год журнала: 2024, Номер 36(21), С. 12655 - 12699

Опубликована: Май 13, 2024

Abstract Artificial neural networks (ANN), machine learning (ML), deep (DL), and ensemble (EL) are four outstanding approaches that enable algorithms to extract information from data make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, EL models have found extensive application in predicting geotechnical geoenvironmental parameters. This research aims provide a comprehensive assessment of applications addressing forecasting within field related engineering, including soil mechanics, foundation rock environmental geotechnics, transportation geotechnics. Previous studies not collectively examined all algorithms—ANN, EL—and explored their advantages disadvantages engineering. categorize address this gap existing literature systematically. An dataset relevant was gathered Web Science subjected an analysis based on approach, primary focus objectives, year publication, geographical distribution, results. Additionally, study included co-occurrence keyword covered techniques, systematic reviews, review articles data, sourced Scopus database through Elsevier Journal, were then visualized using VOS Viewer further examination. The results demonstrated ANN is widely utilized despite proven potential methods engineering due real-world laboratory civil engineers often encounter. However, when it comes behavior scenarios, techniques outperform three other methods. discussed here assist understanding benefits geo area. enables practitioners select most suitable creating certainty resilient ecosystem.

Язык: Английский

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A multi-scale analysis method with multi-feature selection for house prices forecasting

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112779 - 112779

Опубликована: Янв. 1, 2025

Язык: Английский

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Survey on deep learning based computer vision for sonar imagery

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 114, С. 105157 - 105157

Опубликована: Июль 8, 2022

Research on the automatic analysis of sonar images has focused classical, i.e. non deep learning based, approaches for a long time. Over past 15 years, however, application in this research field constantly grown. This paper gives broad overview and current involving feature extraction, classification, detection segmentation sidescan synthetic aperture imagery. Most been directed towards investigation convolutional neural networks (CNN) extraction classification tasks, with result that even small CNNs up to four layers outperform conventional methods. The purpose work is twofold. On one hand, due quick development it serves as an introduction researchers, either just starting their specific or working classical methods helps them learn about recent achievements. other our main goal guide further by identifying gaps bridge. We propose leverage combining available data into open source dataset well carrying out comparative studies developed

Язык: Английский

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Design of concrete incorporating microencapsulated phase change materials for clean energy: A ternary machine learning approach based on generative adversarial networks

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 118, С. 105652 - 105652

Опубликована: Ноя. 30, 2022

Язык: Английский

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Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Water, Год журнала: 2023, Номер 15(9), С. 1750 - 1750

Опубликована: Май 2, 2023

Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial improving water resources planning management. In the past 20 years, significant progress has been made in management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances this field, existing literature must cover ML. This article aims to understand current state-of-the-art ML used achievements domain. It most cited employed from 2009 2022. summarises reviewed papers, highlighting their strengths weaknesses, performance criteria employed, highly identified. worth noting that accuracy was significantly enhanced, resulting a substantial improvement demonstrating robust outcome. Additionally, outlines recommendations future research directions enhance of including prediction related knowledge.

Язык: Английский

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Incorporating syntax and semantics with dual graph neural networks for aspect-level sentiment analysis

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108101 - 108101

Опубликована: Фев. 23, 2024

Язык: Английский

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