GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran

Geomatics Natural Hazards and Risk, Journal Year: 2021, Volume and Issue: 12(1), P. 2087 - 2116

Published: Jan. 1, 2021

Severe flood events in the trans-boundary Shatt Al-Arab basin (Iraq-Iran) claim hundreds of human lives and cause damage to economy environment. Therefore, developing a hazard model recognize basin's susceptible areas flooding is important for decision makers comprehensive risk management. The map was prepared using geographical information systems (GIS) multi-criteria analysis (MCDA) along with application analytical hierarchy process (AHP) method identify optimal selection weights factors that contribute risk. causative used this study were rainfall, distance river, digital elevation (DEM), slope, land use/land cover (LULC), drainage density, soils, lithology. derived consisted four distinct categories (zones). These zones depict high, intermediate, low, very low around 20%, 40%, 39%, 2% area, respectively. produced further verified historical event area. results found be consistent data events, revealing model's effectiveness realistic representation mapping.

Language: Английский

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Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

Geoscience Frontiers, Journal Year: 2021, Volume and Issue: 12(5), P. 101177 - 101177

Published: Feb. 23, 2021

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance (EPB) shield tunnelling. Five artificial intelligence (AI) models based on machine and deep learning techniques—back-propagation neural network (BPNN), extreme (ELM), support vector (SVM), long-short term memory (LSTM), gated recurrent unit (GRU)—are used. geological nine operational parameters that influence are considered. A field case of tunnelling in Shenzhen City, China is analyzed using developed models. total 1000 datasets adopted to establish The prediction performance five ranked as GRU > LSTM SVM ELM BPNN. Moreover, Pearson correlation coefficient (PCC) sensitivity analysis. results reveal main thrust (MT), penetration (P), foam volume (FV), grouting (GV) have strong correlations with (AS). An empirical formula constructed high-correlation influential factors their corresponding datasets. Finally, performances method compared. all perform better than method.

Language: Английский

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Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network

Acta Geotechnica, Journal Year: 2021, Volume and Issue: 17(4), P. 1167 - 1182

Published: July 30, 2021

Language: Английский

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Time-series prediction of shield movement performance during tunneling based on hybrid model

Tunnelling and Underground Space Technology, Journal Year: 2021, Volume and Issue: 119, P. 104245 - 104245

Published: Oct. 28, 2021

Language: Английский

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Novel model for risk identification during karst excavation

Reliability Engineering & System Safety, Journal Year: 2021, Volume and Issue: 209, P. 107435 - 107435

Published: Jan. 9, 2021

Language: Английский

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Mitigating tunnel-induced damages using deep neural networks

Automation in Construction, Journal Year: 2022, Volume and Issue: 138, P. 104219 - 104219

Published: April 7, 2022

Language: Английский

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The development trends of existing building energy conservation and emission reduction—A comprehensive review

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 13170 - 13188

Published: Oct. 17, 2022

The energy conservation of existing buildings plays a significant role in reducing global carbon emissions and building consumption. research on focuses discussing new buildings. However, the is relatively single, there lack systematic in-depth review. Based bibliometric qualitative analysis methods, this study selects relevant literature from 2008 to 2022 for network mapping summarizes results. seven indicators impact factor, total publications, average publication year, total/average citations, normative citations were selected analyze articles by country, institution, journal, keywords, highly cited literature. Three major themes efficiency are obtained analyzing quantitative results keywords literature: (1) influencing factors energy-saving obstacles consumption, (2) measures, (3) optimization its evaluation method. Finally, based results, qualitatively analyzes latest obtain future development trends possible directions throughout their life cycle. This provides innovative ideas renovation buildings: Technology empowerment (using renewable energy, negative technology, "artificial intelligence + Internet Things 5G" cogeneration, etc.) Formulating regulations policy incentives different decision-makers (implementing demand response strategies such as time-of-use pricing guide restrict user behavior, considering tax trading, etc.). premise utilizing increasing capacity resilience distribution grids, will be energy-efficient, economical, intelligent comfortable future.

Language: Английский

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Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 126, P. 106779 - 106779

Published: July 20, 2023

Language: Английский

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Enhancing construction safety: Machine learning-based classification of injury types

Safety Science, Journal Year: 2023, Volume and Issue: 162, P. 106102 - 106102

Published: Feb. 20, 2023

The construction industry is a hazardous with significant injuries and fatalities. Few studies have used data-driven analysis to investigate due accidents. This study aims deploy machine learning (ML) models predict four injury types (ITs): Upper limbs, lower head/neck, back/trunk. A total of 16,878 accident records in Australia were collected fed into several ML algorithms, including fine trees, ensemble boosted xgboost, random forest, two support vector machines, logistic regression. Six performance metrics precision, recall, accuracy, F1 score, the area under receiver operating curve (AUROC), precision recall (AUPRC) evaluate modeling outputs. Random forest showed superior predicting (accuracy 79.3%; 78.0%; score 78.5%; 77.1%; AUROC 0.98; AUPRC 0.78). critical features analyzed using feature importance method nature mechanism had impacts. study's findings contribute safety enhancement by providing quantitative prediction subsequent development controls construction.

Language: Английский

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Spatial autocorrelation and spatial heterogeneity of underground parking space development in Chinese megacities based on multisource open data

Applied Geography, Journal Year: 2023, Volume and Issue: 153, P. 102897 - 102897

Published: Feb. 20, 2023

Language: Английский

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