A copula-based multivariate flood frequency analysis under climate change effects

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 2, 2025

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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Real-time error correction of multiple-hour-ahead flash flood forecasting based on the sliding runoff-rain data and deep learning models

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132918 - 132918

Published: Feb. 1, 2025

Language: Английский

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RFM_Trans: Runoff forecasting model for catchment flood protection using strategies optimized Transformer

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127228 - 127228

Published: March 1, 2025

Language: Английский

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Dual-modality Encoder-decoder Framework for Urban Real-time Rainfall-runoff Prediction

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: March 22, 2025

Language: Английский

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Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

Sustainability, Journal Year: 2024, Volume and Issue: 16(19), P. 8699 - 8699

Published: Oct. 9, 2024

The establishment of an accurate and reliable predictive model is essential for water resources planning management. Standalone models, such as physics-based hydrological models or data-driven have their specific applications, strengths, limitations. In this study, a hybrid (namely SWAT-Transformer) was developed by coupling the Soil Water Assessment Tool (SWAT) with Transformer to enhance monthly streamflow prediction accuracy. SWAT first constructed calibrated, then its outputs are used part inputs Transformer. By correcting errors using Transformer, two effectively coupled. Monthly runoff data at Yan’an Ganguyi stations on Yan River, first-order tributary Yellow River Basin, were evaluate proposed model’s performance. results indicated that performed well in predicting high flows but poorly low flows. contrast, able capture low-flow period information more accurately outperformed overall. SWAT-Transformer could correct predictions overcome limitations single model. integrating SWAT’s detailed physical process portrayal Transformer’s powerful time-series analysis, coupled significantly improved offer optimal resource management, which crucial sustainable economic societal development.

Language: Английский

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Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia)

Water, Journal Year: 2025, Volume and Issue: 17(6), P. 907 - 907

Published: March 20, 2025

Accurate forecasting of river flows is essential for effective water resource management, flood risk reduction and environmental protection. The ongoing effects climate change, in particular the shift precipitation patterns increasing frequency extreme weather events, necessitate development advanced models. This study investigates application long short-term memory (LSTM) neural networks predicting runoff Velika Morava catchment Serbia, representing a pioneering LSTM this region. uses daily runoff, temperature data from 1961 to 2020, interpolated using inverse distance weighting method. model, which was optimized trial-and-error approach, showed high prediction accuracy. For station, model mean square error (MSE) 2936.55 an R2 0.85 test phase. findings highlight effectiveness capturing nonlinear hydrological dynamics, temporal dependencies regional variations. underlines potential models improve management strategies Western Balkans.

Language: Английский

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HGS-At-LSTM: attention-based long short-term memory model combined with halving grid search optimizer for harmful algal bloom forecasting

International Journal of Data Science and Analytics, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Language: Английский

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Exploring ANFIS hybrid optimization for improved rainfall-runoff predictions: insights from Banjar River Catchment, India

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: May 15, 2025

Language: Английский

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Daily Runoff Prediction Based on FA-LSTM Model

Water, Journal Year: 2024, Volume and Issue: 16(16), P. 2216 - 2216

Published: Aug. 6, 2024

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, flood control, enabling decision-makers to implement timely effective measures enhance use efficiency minimize losses. To further the accuracy of prediction, this study proposes FA-LSTM model that integrates Firefly algorithm (FA) with long memory neural network (LSTM). The research focuses on historical daily data from Dahuangjiangkou Wuzhou Hydrology Stations Xijiang River Basin. is compared RNN, LSTM, GRU, SVM, RF models. was used carry out generalization experiment Qianjiang, Wuxuan, Guigang hydrology stations. Additionally, analyzes performance across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square (RMSE), coefficient determination (R2), Kling–Gupta (KGE)—are utilized process. results indicate that: (1) Compared models, exhibits best performance, coefficients (R2) reaching as high 0.966 0.971 at Stations, respectively, KGE 0.965 0.960, respectively. (2) conduct tests Wuxuan stations, its R2 are 0.96 or above, indicating has good adaptability stations strong robustness. (3) As period extends, show decreasing trend, but whole still showed feasible ability. introduced presents an new approach for prediction.

Language: Английский

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