Enhanced industrial heat load forecasting in district networks via a multi-scale fusion ensemble deep learning

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 272, P. 126783 - 126783

Published: Feb. 8, 2025

Language: Английский

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Physics-informed hybrid model for scour evolution prediction around pile foundations under tidal currents

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(4)

Published: April 1, 2025

The local scour process around pile foundations under tidal currents exhibits complex nonlinear and nonstationary dynamic characteristics, primarily stemming from the intricate coupling relationship between levels, flow velocity, direction, evolution. In this paper, a novel hybrid machine learning (ML) framework (referred to as GVCBA) is proposed, which consists of grey wolf optimization (GWO), variational mode decomposition (VMD), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), attention mechanism. By synergistically integrating physical mechanisms with deep learning, demonstrates significantly enhanced accuracy in predicting these spatiotemporal dynamics. Based on Buckingham Π theorem, feature input parameters (e.g., Froude number Fr, periodic parameter tsin) are constructed, explicitly embedding hydrodynamic periodicity into model space, effectively overcoming overfitting tendency traditional data-driven models. Verification using measured data sea-crossing bridge shows that GVCBA framework, through multi-scale decoupling, achieves collaborative modeling oscillations cumulative effects, root mean square error 0.001 60 coefficient determination (R2) 0.985 82 test set, reducing prediction errors by over 80% compared (support vector machine, extreme gradient boosting) benchmark architectures (recurrent its structure combined CNN). Additionally, sensitivity analysis reveals Fr tsin key factors influencing prediction. This provides new method for infrastructure environments, combining interpretability engineering applicability.

Language: Английский

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Prediction of local scour depth around cylindrical piles: Using simulated annealing algorithm and ensemble learning

Ocean Engineering, Journal Year: 2025, Volume and Issue: 330, P. 121221 - 121221

Published: April 15, 2025

Language: Английский

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A review of machine learning and internet-of-things on the water quality assessment: methods, applications and future trends

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105182 - 105182

Published: May 1, 2025

Language: Английский

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A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting

Water, Journal Year: 2024, Volume and Issue: 16(20), P. 2966 - 2966

Published: Oct. 17, 2024

The dissolved oxygen concentration (DOC) is an important indicator of water quality. Accurate DOC predictions can provide a scientific basis for environment management and pollution prevention. This study proposes hybrid forecasting framework combined with Variational Mode Decomposition (VMD), convolutional neural network (CNN), Gated Recurrent Unit (GRU), the Beluga Whale Optimization (BWO) algorithm. Specifically, original sequences were decomposed using VMD. Then, CNN-GRU attention mechanism was utilized to extract key features local dependency sequences. Introducing BWO algorithm solved correction coefficients proposed system, aim improving prediction accuracy. used 4-h monitoring China urban quality data from November 2020 2023. Taking Lianyungang as example, empirical findings exhibited noteworthy enhancements in performance metrics such MSE, RMSE, MAE, MAPE within VMD-BWO-CNN-GRU-AM, reductions 0.2859, 0.3301, 0.2539, 0.0406 compared GRU. These results affirmed superior precision diminished errors model, facilitating more precise predictions. system pivotal sustainably regulating quality, particularly terms addressing concerns.

Language: Английский

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A novel interpretable hybrid model for multi-step ahead dissolved oxygen forecasting in the Mississippi River basin

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 28, 2024

Language: Английский

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