Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models

Journal of Cleaner Production, Год журнала: 2024, Номер 444, С. 141228 - 141228

Опубликована: Фев. 13, 2024

Язык: Английский

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

Journal of Hydrology, Год журнала: 2024, Номер 639, С. 131638 - 131638

Опубликована: Июль 3, 2024

Язык: Английский

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Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Water, Год журнала: 2024, Номер 16(9), С. 1284 - 1284

Опубликована: Апрель 30, 2024

Considering the increased risk of urban flooding and drought due to global climate change rapid urbanization, imperative for more accurate methods streamflow forecasting has intensified. This study introduces a pioneering approach leveraging available network real-time monitoring stations advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned its computational efficacy in events with forecast horizon 7 days. novel integration groundwater level, precipitation, river discharge as predictive variables offers holistic view hydrological cycle, enhancing model’s accuracy. Our findings reveal 7-day period, STA-GRU demonstrates superior performance, notable improvement mean absolute percentage error (MAPE) values R-square (R2) alongside reductions root squared (RMSE) (MAE) metrics, underscoring generalizability reliability. Comparative analysis seven conventional deep models, including Long Short-Term Memory (LSTM), Convolutional Neural Network LSTM (CNNLSTM), (ConvLSTM), (STA-LSTM), (GRU), GRU (CNNGRU), STA-GRU, confirms power STA-LSTM models when faced long-term prediction. research marks significant shift towards an integrated deep-learning forecasting, emphasizing importance spatially temporally encompassing variability within watershed’s stream network.

Язык: Английский

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Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Water, Год журнала: 2024, Номер 16(11), С. 1552 - 1552

Опубликована: Май 28, 2024

Neural networks have become widely employed in streamflow forecasting due to their ability capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent runoff, water level, precipitation. This integrates the discrete wavelet transform (DWT) denoising, variational modal decomposition (VMD) sub-sequence extraction, gated recurrent unit (GRU) modeling individual sub-sequences. Our findings demonstrate that DWT–VMD–GRU model, utilizing rainfall time series as inputs, outperforms other models such GRU, long short-term memory (LSTM), DWT–GRU, DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During testing phase, model yielded RMSE, MAE, MAPE, NSE, KGE values of 245.5 m3/s, 200.5 0.033, 0.997, 0.978, respectively. Additionally, optimal sliding window durations different input combinations typically range from 1 3 months, with (using rainfall) achieving one-month window. The model’s accuracy enhances resource management, flood control, reservoir operation, supporting better-informed decisions efficient allocation.

Язык: Английский

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Spatio-temporal heterogeneity of ecological water level in Poyang Lake, China

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102694 - 102694

Опубликована: Июнь 18, 2024

Anthropogenic activities and climate change have caused physical ecological changes in lakes aggravated water level fluctuations, which are essential factors to consider for nutrient import, protection, biodiversity maintenance. Maintaining levels within a reasonable range is maintaining lake function health, because ecosystem stability compromised when fluctuations exceed specific thresholds. Thus, the (EWL) an important index aquatic habitats biodiversity. A method quantifying EWL of based on hydrological statistical analysis was constructed bridge gaps existing studies, considering both alteration spatio-temporal heterogeneity fluctuations. Taking Poyang Lake as example, has recently attracted increasing global attention owing its alterations subsequent problems, applicability rationality results were verified. The indicate that occurs at representative stations, jointly affected by anthropogenic this region. For instance, construction operation Three Gorges Project Hukou Xingzi station, drought further station. calculated showed obvious heterogeneity, consistent with topographic, geographical, climatic characteristics basin. And study verified through literature reviews satisfiability characteristic species requirements. proposed calculation simple feasible easy data acquisition, strong universality, broad application prospects, offering scientific basis quantitative reference resource management protection.

Язык: Английский

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A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models

Journal of Hydrology, Год журнала: 2024, Номер 640, С. 131668 - 131668

Опубликована: Июль 17, 2024

Язык: Английский

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Streamflow Prediction in Human‐Regulated Catchments Using Multiscale Deep Learning Modeling With Anthropogenic Similarities

Water Resources Research, Год журнала: 2024, Номер 60(9)

Опубликована: Сен. 1, 2024

Abstract Accurate streamflow prediction in human‐regulated catchments remains a formidable challenge due to the complex disturbance of hydrological processes. To consider human modeling, this study introduces novel static attribute collection that combines river‐reach attributes with catchment attributes, referred as multiscale attributes. The is assembled into two deep learning (DL) methods, is, Long Short‐Term Memory (named Multiscale LSTM) and Differentiable Parameter Learning (DPL) model, performance evaluated across 95 United States (USA) 24 Yellow River Basin China. In USA, LSTM DPL models achieve similar median Kling‐Gupta Efficiency (KGE) 0.78 0.71, respectively. However, Basin, KGE values are 0.58 for 0.24 DPL. These results highlight DL models' ability leverage improved compared traditional predominantly influenced by river‐scale encompassing factors such connectivity status index (CSI), degree regulation (DOR), sediment trapping (SED), number dams. Additionally, satellite‐derived mean maximum river width (Width), slope water surface elevation (WSE) from Surface Water Ocean Topography Database (SWORD) contribute valuable insights anthropogenic influences. Moreover, our highlights significance selecting appropriate training data period, which emerges most dominant factor affecting model catchments. diversity during period enables capture broad spectrum signatures within these Consequently, emphasizes advantages underscores considering both natural enhance predictions environments.

Язык: Английский

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Intelligent Prediction of Manta Ray Flow Field Based on a Denoising Probabilistic Diffusion Model

Acta Physica Sinica, Год журнала: 2025, Номер 74(10), С. 0 - 0

Опубликована: Янв. 1, 2025

The manta ray is a large marine species that exhibits both highly efficient gliding and agile flapping capabilities. It can autonomously switch between various motion modes, such as gliding, flapping, group swimming, based on ocean currents seabed conditions. To address the computational resource time constraints of traditional numerical simulation methods in modeling ray's 3D large-deformation flow field, this study proposes novel generative artificial intelligence approach denoising probabilistic diffusion model (surf-DDPM). This method predicts surface field by inputting set parameter variables. Initially, we establish for ray’s mode using immersed boundary spherical function gas kinetic scheme (IB-SGKS), generating an unsteady dataset comprising 180 sets under frequency conditions 0.3-0.9 Hz amplitude 0.1-0.6 body lengths. Data augmentation then performed. Subsequently, Markov chain noise process neural network generation are constructed. A pretrained embeds parameters step labels into data, which fed U-Net training. Notably, Transformer incorporated architecture to enable handling long-sequence data. Finally, examine impact hyperparameters performance visualize predicted pressure velocity fields multi-flapping postures were not included training set, followed quantitative analysis prediction accuracy, uncertainty, efficiency. results demonstrate proposed achieves fast accurate predictions characterized extensive high-dimensional upsampling. minimum PSNR SSIM values 35.931 dB 0.9524, respectively, with all data falling within 95% interval. Compared CFD simulations, AI enhances efficiency single-condition simulations 99.97%.

Язык: Английский

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Long-term prediction of Poyang Lake water level by combining multi-scale isometric convolution network with quantile regression

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102365 - 102365

Опубликована: Апрель 17, 2025

Язык: Английский

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Waterway Regulation Effects on River Hydrodynamics and Hydrological Regimes: A Numerical Investigation

Water, Год журнала: 2025, Номер 17(9), С. 1261 - 1261

Опубликована: Апрель 23, 2025

As a critical intervention for enhancing inland navigation efficiency, waterway regulation projects profoundly modify riverine hydrodynamic conditions while optimizing navigability. This study employs the MIKE21 model to establish two-dimensional numerical framework assessing hydrological alterations induced by channel in Hui River, China. Through comparative simulations of pre- and post-project scenarios across dry, normal, wet years, research quantifies impacts on water levels, flow velocity distribution, geomorphic stability. Results reveal that dredging realignment reduced upstream levels up 0.26 m during drought conditions, concentrating velocities main 0.5 m/s. However, localized restructuring triggered bank erosion risks at cut-off bends sedimentation anchorage basins. The integrated analysis demonstrates although measures enhance flood conveyance capacity, they disrupt sediment transport equilibrium, destabilize riparian ecosystems, compromise monitoring consistency. To mitigate these trade-offs, proposes design optimizations—including ecological revetments adaptive strategies—coupled with enhanced habitat restoration. These findings provide scientific foundation balancing improvements sustainable management fluvial systems.

Язык: Английский

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