A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete

Materials Today Communications, Год журнала: 2024, Номер 39, С. 109294 - 109294

Опубликована: Май 19, 2024

Язык: Английский

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AI-driven predictions of geophysical river flows with vegetation

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 16, 2024

In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time this work. Utilizing measurements both natural laboratory flume experiments, we assess efficacy four distinct standalone techniques-Kstar, M5P, reduced error pruning tree (REPT) random forest (RF) models. addition, also test eight types ML algorithms trained with an Additive Regression (AR) Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT BA-RF). Findings from comparison their predictive capabilities, along sensitivity analysis influencing factors, indicated: (1) Vegetation height emerged as most sensitive parameter determining velocity; (2) all displayed outperforming empirical equations; (3) nearly worked optimal when model was built using input parameters. Overall, findings showed that outperform regular equations at velocity. AR-M5P (R

Язык: Английский

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Modeling flow resistance and geometry of dunes bed form in alluvial channels using hybrid RANN–AHA and GEP models

International Journal of Sediment Research, Год журнала: 2024, Номер 39(6), С. 885 - 902

Опубликована: Авг. 16, 2024

Язык: Английский

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Municipal solid waste management in Ethiopia: Physical and chemical compositions and generation rate: Systematic review

Journal of the Air & Waste Management Association, Год журнала: 2024, Номер 74(12), С. 861 - 883

Опубликована: Окт. 23, 2024

Municipal solid waste management (MSWM) in Ethiopia is a complex issue with institutional, social, political, environmental, and economic dimensions, impacting sustainable development. Effective MSWM planning necessitates understanding the generation rate composition of waste. This systematic review synthesizes qualitative quantitative data from various sources to aggregate current knowledge, identify gaps, provide comprehensive municipal Ethiopia. The findings reveal that 0.38 kg/ca/day, being predominantly food waste, followed by ash, dust, sand, yard Over 85% this MSW either reusable or recyclable, significant portion organic matter (73.13% biodegradable) 11.78% recyclable materials. Physicochemical analyses Ethiopian suitable for composting biogas production, offering opportunities reduce environmental pollution, GHGs, support urban agriculture, create job opportunities. However; challenges persist, including lack political will, weak planning, limited community awareness, inadequate infrastructure, only 31.8% collected legally, leading inefficient harmful disposal practices. To improve MSWM, should focus on public awareness; increased funding, infrastructure investment, private sector partnerships, implementing 4 R principles (reduce, reuse, recycle). An integrated approach involving government, industry, civil society essential. Further research physicochemical properties strategic uses needed enhance

Язык: Английский

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Predicting the drag coefficient of coastal trees using Support Vector Machines and boosting ensemble models

Discover Water, Год журнала: 2024, Номер 4(1)

Опубликована: Ноя. 13, 2024

The effect of green belts on wave absorption is a critical aspect coastal protection strategies. effectiveness in influenced by factors such as the type vegetation used, density and width belt, topography coastline. current study aims to explore performance various intelligent tools, including SVM (Support Vector Machine), ABR (Ada Boost Regression), ETR (Extra Trees GBR (Gradient Boosting RF (Random Forest), forecast drag coefficients trees (CD). In this direction, four dimensionless parameters relative height (H/d), (D), shoreline slope (S), propagation velocity (u/ $$\sqrt{\xi E/\rho }$$ ) were assumed input parameters, CD was considered target. To evaluate developed soft computing models, statistical indicators graphical plots Violin, Tylor, Scatter applied. results revealed that method outperforms existing machine learning techniques with R2 = 0.996, RMSE 0.003, MAE 0.002, SI 0.014. addition, Tylor diagram indicates distance index obtained using model exhibited high alignment actual data, especially comparison alternative tools.

Язык: Английский

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Optimizing bed shear stress prediction in open flow channels: an investigation of heuristic machine learning techniques

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Фев. 15, 2025

Язык: Английский

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Mean Limiting Pressure Factors Determination in Contiguous Pile Walls using RAFELA and Nonlinear Regression Models in Spatially Random Soil

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104436 - 104436

Опубликована: Фев. 1, 2025

Язык: Английский

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Predictive framework of vegetation resistance in channel flow

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 4, 2025

Predicting vegetation-induced flow resistance remains a significant challenge due to the diverse and dynamic nature of river vegetation. Although numerous empirical models are available, they often fail generalize across different environmental conditions, leading inaccurate predictions. This study introduces machine learning-based framework for predicting vegetation resistance, incorporating nine ML methods, including SVM, XGBoost, BP. To improve predictive performance, optimization algorithms such as PSO, WSO, RIME were applied. A comprehensive dataset 490 samples multiple scales was used evaluate model accuracy, indicated: (1) The submergence ratio α Froude number Fr most sensitive parameters affecting Cd, while missing density λ blockage β significantly reduce accuracy; (2) XGBoost outperforms other models, achieving highest accuracy (R2 = 0.9552); (3) stable six parameter deficiency scenarios, with maintaining R2 > 0.85 in all cases. In conclusion, this highlights transformative potential proposed overcoming long-standing challenges estimating vegetated channels. It provides valuable insights sustainable management, bolsters restoration efforts, enhances complex, environments.

Язык: Английский

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Physics-informed neural networks for predicting sediment transport in pressurized pipe flows

Environmental Earth Sciences, Год журнала: 2025, Номер 84(11)

Опубликована: Май 1, 2025

Язык: Английский

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Optimizing seasonal discharge predictions: a hybridized approach with AI and non-linear models

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 7(4), С. 3405 - 3423

Опубликована: Апрель 3, 2024

Язык: Английский

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