An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy

Journal of Hydrology, Год журнала: 2023, Номер 629, С. 130558 - 130558

Опубликована: Дек. 7, 2023

Язык: Английский

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A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 121719 - 121719

Опубликована: Сен. 22, 2023

Язык: Английский

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Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series

Journal of Hydrology, Год журнала: 2024, Номер 634, С. 131041 - 131041

Опубликована: Март 11, 2024

Язык: Английский

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DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion

Journal of Hydrology, Год журнала: 2024, Номер 643, С. 131996 - 131996

Опубликована: Сен. 16, 2024

Язык: Английский

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Runoff prediction using a multi-scale two-phase processing hybrid model

Stochastic Environmental Research and Risk Assessment, Год журнала: 2025, Номер unknown

Опубликована: Янв. 19, 2025

Язык: Английский

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The CEEMDAN-EWT-CNN-GRU-SVM Model: A Robust Framework for Decomposing Non-Stationary Time Series, Extracting Data features, and Predicting Solar Radiation

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104267 - 104267

Опубликована: Фев. 1, 2025

Язык: Английский

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Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition

Journal of Hydroinformatics, Год журнала: 2023, Номер 26(1), С. 255 - 283

Опубликована: Дек. 11, 2023

Abstract Accurate runoff prediction is vital in efficiently managing water resources. In this paper, a hybrid model combining complete ensemble empirical mode decomposition with adaptive noise, variational decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) proposed. Firstly, CEEMDAN used to decompose the original data, high-frequency component decomposed using VMD. Then, each input into LSTM optimized by CABES for prediction. Finally, results of individual predictions are combined reconstructed produce monthly predictions. The employed predict at Xiajiang hydrological station Yingluoxia station. A comprehensive comparison conducted other models including back propagation (BP), LSTM, etc. assessment model's performance uses four evaluation indexes. Results reveal that CEEMDAN-VMD-CABES-LSTM showcased highest forecast accuracy among all evaluated. Compared single root mean square error (RMSE) absolute percentage (MAPE) decreased 71.09 65.26%, respectively, RMSE MAPE 65.13 40.42%, respectively. R NSEC both sites near 1.

Язык: Английский

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A novel machine learning-based framework for the water quality parameters prediction using hybrid long short-term memory and locally weighted scatterplot smoothing methods

Journal of Hydroinformatics, Год журнала: 2024, Номер 26(5), С. 1059 - 1079

Опубликована: Апрель 12, 2024

ABSTRACT Water quality prediction is crucial for effective river stream management. Dissolved oxygen, conductivity and chemical oxygen demand are vital parameters water quality. Development of machine learning (ML) deep (DL) methods made them widely used in this domain. Sophisticated DL techniques, especially long short-term memory (LSTM) networks, required accurate, real-time multistep prediction. LSTM networks predicting due to their ability handle long-term dependencies sequential data. We propose a novel hybrid approach combining with data smoothing method. The Sava at the Jamena hydrological station serves as case study. Our workflow uses alongside LOcally WEighted Scatterplot Smoothing (LOWESS) technique filtering. For comparison, Support Vector Regressor (SVR) baseline Performance evaluated using Root Mean Squared Error (RMSE) Coefficient Determination R2 metrics. Results demonstrate that outperforms method, an up 0.9998 RMSE 0.0230 on test set dissolved oxygen. Over 5-day period, our achieves 0.9912 0.1610 confirming it reliable method

Язык: Английский

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Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management

Environmental Research, Год журнала: 2024, Номер 248, С. 118267 - 118267

Опубликована: Янв. 18, 2024

Язык: Английский

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Monthly runoff prediction using gated recurrent unit neural network based on variational modal decomposition and optimized by whale optimization algorithm

Environmental Earth Sciences, Год журнала: 2024, Номер 83(2)

Опубликована: Янв. 1, 2024

Язык: Английский

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