Transformer Based Water Level Prediction in Poyang Lake, China

Water, Journal Year: 2023, Volume and Issue: 15(3), P. 576 - 576

Published: Feb. 1, 2023

Water level is an important indicator of lake hydrology characteristics, and its fluctuation significantly affects ecosystems. In recent years, deep learning models have shown their superiority in the long-time range prediction processes, while application with attention mechanism for water very rare. this paper, taking Poyang Lake as a case study, transformer neural network model applied to examine performance prediction, explore effects Yangtze River on fluctuations, analyze influence hyper-parameters (window size layers) lead time accuracy. The result indicated that performs well simulating variations can reflect temporal variation characteristics Lake. testing stage, RMSE values were recorded 0.26–0.70 m, NSE are higher than 0.94. Moreover, inflow has great Lake, especially flood receding periods. contribution rate 80% 270%, respectively. Additionally, hyper-parameters, such window layers, simulation 90 d layer 6 most suitable may affect accuracy prediction. With varied from one seven days, was high 0.46–0.73 value increased 1.37 m 1.82 15 30 constructed paper first be forecasting showed efficiency However, few studies tried use coupling hydrological processes. It suggested used long sequence time-series processes other lakes test performance, providing further scientific evidence control floods management resources.

Language: Английский

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Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation

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

Published: May 16, 2024

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex processes including the spatio-temporal distribution rainfall, spatial heterogeneity watershed sub-surface characteristics, and runoff generation routing behaviours. Data-driven offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection a decline prediction stability as lead time extends. This study introduces hybrid model, RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), Transformer architecture. Applied typical Jingle middle reaches Yellow River, this model utilises rainfall data from basin sites simulate flood processes, its outcomes compared against those RS-LSTM, RS-Transformer, RS-BP, RS-MLP models. It was evaluated Nash-Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Absolute (MAE), Bias percentage metrics. At 1-h during calibration validation, RS-LSTM-Transformer achieved NSE, RMSE, MAE, values 0.970, 14.001m

Language: 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, Journal Year: 2024, Volume and Issue: 643, P. 131996 - 131996

Published: Sept. 16, 2024

Language: Английский

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Probing the limit of hydrologic predictability with the Transformer network

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 637, P. 131389 - 131389

Published: May 19, 2024

Language: Английский

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Interpretable CEEMDAN-FE-LSTM-transformer hybrid model for predicting total phosphorus concentrations in surface water

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 629, P. 130609 - 130609

Published: Jan. 5, 2024

Language: Английский

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A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China

Hydrology Research, Journal Year: 2024, Volume and Issue: 55(2), P. 180 - 204

Published: Jan. 10, 2024

Abstract Accurate streamflow prediction is crucial for effective water resource management. However, reliable remains a considerable challenge because of the highly complex, non-stationary, and non-linear processes that contribute to at various spatial temporal scales. In this study, we utilized convolutional neural network (CNN)–Transformer–long short-term memory (LSTM) (CTL) model prediction, which replaced embedding layer with CNN extract partial hidden features, added an LSTM correlations on scale. The CTL incorporated Transformer's ability global information, CNN's LSTM's capture correlations. To validate its effectiveness, applied it in Shule River basin northwest China across 1-, 3-, 6-month horizons compared performance Transformer, CNN, LSTM, CNN–Transformer, Transformer–LSTM. results demonstrated outperformed all other models terms predictive accuracy Nash–Sutcliffe coefficient (NSE) values 0.964, 0.912, 0.856 ahead prediction. best among five comparative were 0.908, 0.824, 0.778, respectively. This indicated outstanding alternative technique where surface data are limited.

Language: Английский

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Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 52, P. 101744 - 101744

Published: March 15, 2024

Yellow River Basin in China, where streamflow dynamics were significantly impacted by human activities. We introduced a deep learning-based method, i.e., Data Integration (DI) with Long Short-Term Memory (LSTM), which leverages Global Flood Awareness System (GloFAS) data. Multiscale (Catchment, River) attributes incorporated into the DI LSTM to represent disturbances on land surface. employed this method reconstruct daily series 60 human-regulated catchments across Basin, and identified sensitivity of model multiscale attributes. Our findings revealed that achieved favourable performance estimation, highest Kling-Gupta efficiency (KGE) reaching up 0.9, outperforming Regular model, was forced meteorological variables. can enhance performance, particularly large significant A two-step validation demonstrated high accuracy reconstructed data as KGEs for estimation 40 are over 0.6. In summary, shows great potential reconstructing arid regions. The contribute valuable insights monitoring changing hydrological conditions, especially regions lacking extensive networks.

Language: Английский

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A hydrological process-based neural network model for hourly runoff forecasting

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 176, P. 106029 - 106029

Published: April 3, 2024

Language: Английский

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Streamflow prediction in ungauged catchments through use of catchment classification and deep learning

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 639, P. 131638 - 131638

Published: July 3, 2024

Language: Английский

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Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets

Water, Journal Year: 2024, Volume and Issue: 16(13), P. 1904 - 1904

Published: July 3, 2024

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements artificial intelligence the availability large, high-quality datasets. This review explores current state ML hydrology, emphasizing utilization extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, GRACE. These provide critical data for modeling various parameters, including streamflow, precipitation, groundwater levels, flood frequency, particularly data-scarce regions. We discuss type methods used significant successes achieved through those models, highlighting their enhanced predictive accuracy integration diverse sources. The also addresses challenges inherent applications, heterogeneity, spatial temporal inconsistencies, issues regarding downscaling LSH, need incorporating human activities. In addition to discussing limitations, this article highlights benefits utilizing high-resolution compared traditional ones. Additionally, we examine emerging trends future directions, real-time quantification uncertainties improve model reliability. place a strong emphasis on citizen science IoT collection hydrology. By synthesizing latest research, paper aims guide efforts leveraging large techniques advance enhance water resource management practices.

Language: Английский

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