Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 634, P. 131117 - 131117

Published: March 24, 2024

Global climate change has led to an increase in the frequency and scale of extreme weather events worldwide, there is urgent need develop better-performing hydrological models improve accuracy streamflow simulations facilitate water resource planning management. The Soil Water Assessment Tool (SWAT) a notable physical foundation widely used research. However, it uses simplified vegetation growth model, introducing uncertainty into simulation results. This study focused on improving model based remotely sensed phenological leaf area index (LAI) data. Phenological data were define dormancy, LAI replaced corresponding simulated by original model. approach improved describing dynamics. Then, enhanced SWAT was coupled with bidirectional long short-term memory (BiLSTM) validate processes upstream Hei River. During validation, performance simulating (R2 = 0.835, NSE 0.819) better than that 0.821, 0.805). In terms evapotranspiration, demonstrated even greater advantages. verification period, compared those R2 values for daily-scale increased from 0.196 −0.269 0.777 0.732, respectively. monthly-scale 0.782 0.678 0.906 0.851, Simultaneously, levels two coupling approaches prediction compared, i.e., direct BiLSTM (SWAT-BiLSTM) (enhanced SWAT-BiLSTM). results showed SWAT-BiLSTM always performed during entire especially which could more accurately predict peak changes. deep learning accuracy.

Language: Английский

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Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 52, P. 101716 - 101716

Published: Feb. 24, 2024

Language: Английский

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Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 631, P. 130841 - 130841

Published: Feb. 5, 2024

Constrained by the sparsity of observational streamflow data, large-scale catchments face pressing challenges in prediction and flood management amid climate change. Deep learning excels simulation performance while flow lag information data-driven approaches is barely highlighted. In this study, we introduce a time-lag informed deep framework for catchments. Central to utilization between upstream downstream subbasins, enabling precise forecasting at outlet driven data. Taking monsoon-influenced Dulong-Irrawaddy River Basin (DIRB) as study area, determined peak (PFL) days relative annual scale (RAFS) defined subbasins. By incorporating with historical data different time intervals, developed optimal model DIRB. This was then applied evaluate processes 2008 2009, using selected indicators. The results indicate that led significant improvements, notably LSTM_PFL_RAFS Hkamti sub-basin which achieved Kling-Gupta Efficiency (KGE) 0.891 (Nash-Sutcliffe efficiency coefficient, NSE, 0.904), surpassing LSTM's 0.683 (NSE, 0.785). Further integration specific interval, model, H(15)_PFL utilizes reached an impressive KGE 0.948 0.940). outperformed standard LSTM accurately simulating key characteristics, including flows, initiation times, durations 2009 events. Notably, provides valuable 15-day lead forecasting, extending window emergency response preparations. Future research incorporates additional essential catchment features into holds great potential unraveling complex mechanisms hydrological responses human activities

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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Improving a hydrological model by coupling it with an LSTM water use forecasting model

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131215 - 131215

Published: April 18, 2024

Language: Английский

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GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 25, 2025

Language: Английский

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A state-of-the-art review of long short-term memory models with applications in hydrology and water resources

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112352 - 112352

Published: Oct. 1, 2024

Language: Английский

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Hydrological Projections in the Third Pole Using Artificial Intelligence and an Observation‐Constrained Cryosphere‐Hydrology Model

Earth s Future, Journal Year: 2024, Volume and Issue: 12(4)

Published: April 1, 2024

Abstract The water resources of the Third Pole (TP), highly sensitive to climate change and glacier melting, significantly impact food security millions in Asia. However, projecting future spatial‐temporal runoff changes for TP's mountainous basins remains a formidable challenge. Here, we've leveraged long short‐term memory model (LSTM) craft grid‐scale artificial intelligence (AI) named LSTM‐grid. This has enabled production hydrological projections seven major river TP. LSTM‐grid integrates monthly precipitation, air temperature, total mass (total_GMC) data at 0.25‐degree grid. Training employed gridded historical evapotranspiration sets generated by an observation‐constrained cryosphere‐hydrology headwaters TP during 2000–2017. Our results demonstrate LSTM grid's effectiveness usefulness, exhibiting Nash‐Sutcliffe Efficiency coefficient exceeding 0.92 verification periods (2013–2017). Moreover, monsoon region exhibited higher rate increase compared those westerlies region. Intra‐annual indicated notable increases spring runoff, especially where meltwater contributes runoff. Additionally, aptly captures before after turning points highlighting growing influence precipitation on reaching maximum total_GMC. Therefore, offers fresh perspective understanding spatiotemporal distribution high‐mountain glacial regions tapping into AI's potential drive scientific discovery provide reliable data.

Language: Английский

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Exploring a spatiotemporal hetero graph-based long short-term memory model for multi-step-ahead flood forecasting

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 130937 - 130937

Published: Feb. 27, 2024

Language: Английский

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Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 86, P. 425 - 442

Published: Dec. 7, 2023

In this study, two deep learning approaches, bidirectional long short-term memory (BiLSTM) and (LSTM), were used along with adaptive boosting general regression neural network to forecast multi-step-ahead pan evaporation in arid climate stations Iran (Ahvaz Yazd). Lagged time series of meteorological data input the machine models. Two feature selection methods, i.e., Boruta extra tree XGBoost, select significant inputs reduce number model complexity. Different statistical metrics investigate performance. The results demonstrated that Boruta-extra-tree-based models more accurate than XGBoost-based Compared techniques, combination BiLSTM enabled one-day-ahead forecasting for both sites (Root Mean Square Error (RMSE) = 1.6857, Ahvaz station, RMSE 1.3996 Yazd station). proposed was up 30 days ahead stations. showed Boruta-BiLSTM could accurately

Language: Английский

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