Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 285, P. 125324 - 125324

Published: Dec. 1, 2020

Language: Английский

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Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 29(9), P. 12875 - 12889

Published: May 14, 2021

Language: Английский

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PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

Mathematics, Journal Year: 2022, Volume and Issue: 10(4), P. 610 - 610

Published: Feb. 16, 2022

Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution data, performance learning prediction models can be reduced by modeling bias or overfitting. This paper proposes novel planar flow-based variational auto-encoder model (PFVAE), which uses long- short-term memory network (LSTM) as designs (VAE) data predictor overcome noise effects. In addition, internal structure VAE is transformed using flow, enables it learn fit nonlinearity improve dynamic adaptability network. The experiments verify that proposed superior other regarding accuracy proves effective for predicting data.

Language: Английский

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Intelligent approaches for sustainable management and valorisation of food waste

Bioresource Technology, Journal Year: 2023, Volume and Issue: 377, P. 128952 - 128952

Published: March 24, 2023

Language: Английский

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Water Temperature Prediction Using Improved Deep Learning Methods through Reptile Search Algorithm and Weighted Mean of Vectors Optimizer

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(2), P. 259 - 259

Published: Jan. 23, 2023

Precise estimation of water temperature plays a key role in environmental impact assessment, aquatic ecosystems’ management and resources planning management. In the current study, convolutional neural networks (CNN) long short-term memory (LSTM) network-based deep learning models were examined to estimate daily temperatures Bailong River China. Two novel optimization algorithms, namely reptile search algorithm (RSA) weighted mean vectors optimizer (INFO), integrated with both enhance their prediction performance. To evaluate accuracy implemented models, four statistical indicators, i.e., root square errors (RMSE), absolute errors, determination coefficient Nash–Sutcliffe efficiency utilized on basis different input combinations involving air temperature, streamflow, precipitation, sediment flows day year (DOY) parameters. It was found that LSTM-INFO model DOY outperformed other competing by considerably reducing RMSE MAE predicting temperature.

Language: Английский

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Machine learning assisted advanced battery thermal management system: A state-of-the-art review

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 60, P. 106688 - 106688

Published: Jan. 20, 2023

Language: Английский

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Modeling industrial hydrocyclone operational variables by SHAP-CatBoost - A “conscious lab” approach

Powder Technology, Journal Year: 2023, Volume and Issue: 420, P. 118416 - 118416

Published: March 7, 2023

Undoubtedly hydrocyclones play a critical role in powder technology, which can considerably affect the plants' process efficiency. However, were rarely modeled on an industrial scale, where model be used to train operators and minimize potential scale-up errors lab costs. The novel approach for filling such gap would using conscious "CL" as new concept that builds based dataset explainable artificial intelligence (XAI). As approach, this study developed CL explored interactions between hydrocyclone variables by most recent XAI method called "SHapley Additive exPlanations (SHAP)", machine-learning model, "CatBoost". output particle size of plant magnetic separator SHAP-CatBoost. SHAP could successfully all relationships, CatBoost predict O80 K80, outcomes had higher accuracy (R2 ∼ 0.90) than other conventional AIs.

Language: Английский

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Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Reviews of Geophysics, Journal Year: 2024, Volume and Issue: 62(1)

Published: Feb. 11, 2024

Abstract Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the impacts of future change lake and related processes, use mathematical models is essential. In this study, we provide a comprehensive review water temperature modeling. We begin discussing physical concepts that regulate in lakes, which serve as primer for description process‐based models. then an overview different sources observational data, including situ monitoring satellite Earth observations, used field classify various available, discuss model performance, commonly performance metrics optimization methods. Finally, analyze emerging modeling approaches, forecasting, digital twins, combining deep learning, evaluating structural differences through ensemble modeling, adapted management, coupling This aimed at diverse group professionals working fields limnology hydrology, ecologists, biologists, physicists, engineers, remote sensing researchers from private public sectors who are interested understanding its applications.

Language: Английский

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Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm

Ocean Engineering, Journal Year: 2020, Volume and Issue: 219, P. 108415 - 108415

Published: Dec. 11, 2020

Language: Английский

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A computational framework for propagated waves in a sandwich doubly curved nanocomposite panel

Engineering With Computers, Journal Year: 2020, Volume and Issue: 38(2), P. 1679 - 1696

Published: Aug. 3, 2020

Language: Английский

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