Evaluation and Application of the PT-JPL Physical Model Optimized with XGBoost Algorithm in Latent Heat Flux Estimation

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

Published: March 28, 2025

Language: Английский

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Application of semi-supervised models for groundwater level simulation in arid regions with small sample sizes

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 381, P. 125215 - 125215

Published: April 9, 2025

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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A Hydrodynamic Model and Data-Driven Evolutionary Multi-Objective Optimization Algorithm Based Optimal Operation Method for Multi-barrage Flood Control

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(11), P. 4323 - 4341

Published: April 23, 2024

Language: Английский

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Exploring hydrological system performance for alpine low flows in local and continental prediction systems

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 56, P. 102056 - 102056

Published: Nov. 26, 2024

Language: Английский

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Comment on egusphere-2024-2134

Published: Dec. 26, 2024

Abstract. An increasing number of studies have shown the prowess Long Short-Term Memory (LSTM) networks for hydrological modelling and forecasting. One commonly cited drawback these methods, however, is requirement large amounts training data to properly reproduce streamflow events. For maximum annual streamflow, this can be problematic since they are by definition less common than mid- or low-flows, leading under-representation in model’s set and, ultimately, parameterization. This study investigates six methods improve peak simulation skill LSTM models used extend observation time series flood frequency analysis (FFA). Methods include adding meteorological variables, providing simulations from a distributed model, oversampling events, multihead attention mechanisms, “donor” catchments combining some elements single model. Furthermore, results compared those obtained model HYDROTEL. The performed on 88 province Quebec using leave-one-out cross-validation implementation an FFA applied observations as well simulations. Results show that LSTM-based able simulate (for simple implementation) better (with hybrid LSTM-hydrological implementations) Multiple pathways forward further ability predict provided discussed.

Language: Английский

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Development of A Distributed Modeling Framework Considering Spatiotemporally Varying Hydrological Processes for Sub-Daily Flood Forecasting in Semi-Humid and Semi-Arid Watersheds

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(10), P. 3725 - 3754

Published: April 24, 2024

Language: Английский

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