Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models

Ecological Informatics, Journal Year: 2024, Volume and Issue: 85, P. 102933 - 102933

Published: Dec. 7, 2024

Language: Английский

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Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning

Applied Computing and Geosciences, Journal Year: 2024, Volume and Issue: 24, P. 100206 - 100206

Published: Nov. 9, 2024

Language: Английский

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Enhanced SWAT calibration through intelligent range-based parameter optimization

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 367, P. 121933 - 121933

Published: July 30, 2024

Language: Английский

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Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102755 - 102755

Published: Aug. 3, 2024

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models machine learning algorithms are the mainstream tools streamflow simulation. However, their inherent limitations, such as time-consuming large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve accuracy computational efficiency of simulation, which integrates Block-wise use TOPMODEL (BTOP) model into eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated using Latin hypercube sampling algorithm instead calibration reduce costs. Then, XGBoost combines with multi-source errors. which, serval input variable selection employed choose relevant inputs remove redundant information model. The validated compared standalone at three stations in Jialing River basin, China. results show that performance BTOP_XGB significantly better than models. NSE Beibei, Xiaoheba, Luoduxi increases by 54%, 21%, 83%, respectively. Meanwhile, time saved >90% original calibrated BTOP. less affected parameter sample sizes amounts, demonstrating robustness simplifies complexity enhances stability learning, jointly improving reliability provides potential shortcut over basins areas or limited observed data.

Language: Английский

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Streamflow prediction based on the Soil and Water Assessment Tool in the Pajeú river basin, Brazilian semiarid

Journal of South American Earth Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 105420 - 105420

Published: Feb. 1, 2025

Language: Английский

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Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning

Environments, Journal Year: 2025, Volume and Issue: 12(3), P. 94 - 94

Published: March 17, 2025

The increasing frequency and severity of hydrological extremes due to climate change necessitate accurate baseflow estimation effective watershed management for sustainable water resource use. Soil Water Assessment Tool (SWAT) is widely utilized modeling but shows limitations in simulation its uniform application the alpha factor across Hydrologic Response Units (HRUs), neglecting spatial temporal variability. To address these challenges, this study integrated SWAT with Tree-Based Pipeline Optimization (TPOT), an automated machine learning (AutoML) framework, predict HRU-specific factors. Furthermore, a user-friendly web-based program was developed improve accessibility practical optimized factors, supporting more predictions, even ungauged watersheds. proposed approach area significantly enhanced recession predictions compared traditional method. This improvement supported by key performance metrics, including Nash–Sutcliffe Efficiency (NSE), coefficient determination (R2), percent bias (PBIAS), mean absolute percentage error (MAPE). framework effectively improves accuracy practicality modeling, offering scalable innovative solutions face stress.

Language: Английский

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Evaluation of evapotranspiration data and gridded products using robust linear estimators in Colombia

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(4)

Published: March 19, 2025

Language: Английский

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Regionalization of hydrological cycle changes in 31 source catchments of Yellow River Basin considering multiple hydrological variables

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102340 - 102340

Published: April 3, 2025

Language: Английский

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The Impact of soil data on SWAT modeling: Effects, requirements, and future directions

Scientific African, Journal Year: 2025, Volume and Issue: unknown, P. 2694 - 2694

Published: April 1, 2025

Language: Английский

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Modelling water scarcity and water footprint of agricultural crops: A case from a semi-arid region in Morocco

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102455 - 102455

Published: May 10, 2025

Language: Английский

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