Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131275 - 131275

Published: May 7, 2024

Language: 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, Journal Year: 2023, Volume and Issue: 238, P. 121719 - 121719

Published: Sept. 22, 2023

Language: Английский

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Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Water Resources Research, Journal Year: 2023, Volume and Issue: 59(9)

Published: Sept. 1, 2023

Abstract Accurate runoff forecasting plays a vital role in issuing timely flood warnings. Whereas, previous research has primarily focused on historical and precipitation variability while disregarding other factors' influence. Additionally, the prediction process of most machine learning models is opaque, resulting low interpretability model predictions. Hence, this study develops an ensemble deep to forecast from three hydrological stations. Initially, time‐varying filtered based empirical mode decomposition employed decompose series into several internal functions (IMFs). Subsequently, complexity each IMF component evaluated by multi‐scale permutation entropy, IMFs are classified high‐ low‐frequency portions entropy values. Considering high‐frequency still exhibit great volatility, robust local mean adopted perform secondary portions. Then, meteorological variables processed Relief algorithm variance inflation factor features as inputs, individual subsequences preliminary outputs bidirectional gated recurrent unit extreme models. Random forests (RF) introduced nonlinear predicted sub‐models obtain final results. The proposed outperforms various evaluation metrics. Meanwhile, due opaque nature models, shapley assess contribution selected variable long‐term trend runoff. could serve essential reference for precise warning.

Language: Английский

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Optimal dispatching rules of hydropower reservoir in flood season considering flood resources utilization: A case study of Three Gorges Reservoir in China

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 388, P. 135975 - 135975

Published: Jan. 10, 2023

Language: Английский

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Improved monthly runoff time series prediction using the SOA–SVM model based on ICEEMDAN–WD decomposition

Journal of Hydroinformatics, Journal Year: 2023, Volume and Issue: 25(3), P. 943 - 970

Published: March 27, 2023

Abstract In runoff prediction, the prediction accuracy is often affected by non-linear and non-stationary characteristics of series. this study, a coupled forecasting model proposed that decomposes original series an improved complete ensemble Empirical Mode Decomposition (EMD) (ICEEMDAN) combined with wavelet decomposition (WD) then forecasts monthly using support vector machine (SVM) optimized seagull optimization algorithm (SOA). method, Intrinsic Function (IMF) Residual (Res) are obtained decomposing ICEEMDAN. The WD method used to perform quadratic high-frequency components decomposed ICEEMDAN make as smooth possible. Then input into SOA-SVM for prediction. Finally, results each component superimposed reconstructed obtain final results. RMSE, Mean Absolute Percentage Error (MAPE), Nash-Sutcliffe Efficiency Coefficient (NSEC), R selected evaluate compared model, EMD-SOA-SVM CEEMDAN-SOA-SVM other models. applied forecast Hongjiadu Manwan Reservoirs. When benchmarking models, ICEEMDAN-WD-SOA-SVM attains smallest Root Square (RMSE) MAPE largest NSEC R. has best effect, highest accuracy, lowest error.

Language: Английский

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A hybrid framework based on LSTM for predicting karst spring discharge using historical data

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 130946 - 130946

Published: Feb. 28, 2024

Language: Английский

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Evaluation and Interpretation of Runoff Forecasting Models Based on Hybrid Deep Neural Networks

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(6), P. 1987 - 2013

Published: Feb. 29, 2024

Language: Английский

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SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 9, 2024

Accurate runoff forecasting is of great significance for water resource allocation flood control and disaster reduction. However, due to the inherent strong randomness sequences, this task faces significant challenges. To address challenge, study proposes a new SMGformer forecast model. The model integrates Seasonal Trend decomposition using Loess (STL), Informer's Encoder layer, Bidirectional Gated Recurrent Unit (BiGRU), Multi-head self-attention (MHSA). Firstly, in response nonlinear non-stationary characteristics sequence, STL used extract sequence's trend, period, residual terms, multi-feature set based on 'sequence-sequence' constructed as input model, providing foundation subsequent models capture evolution runoff. key features are then captured layer. Next, BiGRU layer learn temporal information these features. further optimize output MHSA mechanism introduced emphasize impact important information. Finally, accurate achieved by transforming through Fully connected verify effectiveness proposed monthly data from two hydrological stations China selected, eight compare performance results show that compared with Informer 1th step MAE decreases 42.2% 36.6%, respectively; RMSE 37.9% 43.6% NSE increases 0.936 0.975 0.487 0.837, respectively. In addition, KGE at 3th 0.960 0.805, both which can maintain above 0.8. Therefore, accurately sequence extend effective period

Language: Английский

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A runoff prediction approach based on machine learning, ensemble forecasting and error correction: A case study of source area of Yellow River

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133190 - 133190

Published: March 1, 2025

Language: Английский

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Analyzing variation of water inflow to inland lakes under climate change: Integrating deep learning and time series data mining

Environmental Research, Journal Year: 2024, Volume and Issue: 259, P. 119478 - 119478

Published: June 24, 2024

Language: Английский

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