Assessing the effect of rating curve uncertainty in streamflow simulation on Kulfo watershed, Southern Ethiopia DOI Creative Commons
Nahom Bekele Mena,

Elias Gebeyehu Ayele,

Henok Gubula Chora

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(9), P. 4199 - 4219

Published: Sept. 1, 2024

ABSTRACT Accurate streamflow simulation and comprehending its associated uncertainty are crucial for effective water resource management. However, the of rating curves from which data is derived remains poorly understood. This study aims to simulate under curve conditions. The bootstrap resampling technique (BSRT) was used establish estimate uncertainty. Furthermore, it integrated with standalone hybrid models (GRU, Bi-LSTM, Conv1D-LSTM), assess effect this on simulation. Different lag times rainfall discharge as input DL models. Despite complexity, Conv1D-LSTM model did not outperform Bi-LSTM model. slightly outperforms GRU Moreover, significantly propagates simulation, particularly in high-flow regions. Consequently, uncertainties related Kulfo River led a about 17.8 m3 s−1, representing 22% at peak discharge. performance evaluated using different metrics (RMSE, MAE, NSE, R2). findings underscore importance considering enhance management practices support informed decision-making area.

Language: Английский

Advanced Framework for Predicting Rainfall-Runoff: Comparative Evaluation of AI Models for Enhanced Forecasting Accuracy DOI
Hadi Sanikhani, Mohammad Reza Nikpour,

Fatemeh Jamshidi

et al.

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

Published: Jan. 17, 2025

Language: Английский

Citations

1
Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling DOI Creative Commons
Özgür Kişi, Salim Heddam, Kulwinder Singh Parmar

et al.

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

Published: March 3, 2025

Abstract Accurate rainfall-runoff modeling is crucial for effective watershed management, hydraulic infrastructure safety, and flood mitigation. However, predicting remains challenging due to the nonlinear interplay between hydro-meteorological topographical variables. This study introduces a hybrid Gaussian process regression (GPR) model integrated with K-means clustering (GPR-K-means) short-term forecasting. The Orgeval in France serves as area, providing hourly precipitation streamflow data spanning 1970–2012. performance of GPR-K-means compared standalone GPR principal component (PCR) models across four forecasting horizons: 1-hour, 6-hour, 12-hour, 24-hour ahead. results reveal that significantly improves accuracy all lead times, Nash-Sutcliffe Efficiency (NSE) approximately 0.999, 0.942, 0.891, 0.859 forecasts, respectively. These outperform other ML models, such Long Short-Term Memory, Support Vector Machines, Random Forest, reported literature. demonstrates enhanced reliability robustness forecasting, emphasizing its potential broader application hydrological modeling. Furthermore, this provides novel methodology combining Bayesian techniques surface hydrology, contributing more accurate timely prediction.

Language: Английский

Citations

1
Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity DOI
Xu Yang, Heng Li,

Yuqian Hu

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106374 - 106374

Published: March 1, 2025

Language: Английский

Citations

0
Investigation of the effect of climatic parameters in machine learning algorithms for streamflow predicting in Hamoon Helmand Catchment, Iran DOI
Shabnam Vakili, Seyed Morteza Mousavi

Arabian Journal of Geosciences, Journal Year: 2025, Volume and Issue: 18(5)

Published: April 9, 2025

Language: Английский

Citations

0
Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models DOI

Alexander Ley,

Helge Bormann, Markus Casper

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102426 - 102426

Published: April 29, 2025

Language: Английский

Citations

0
Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models DOI Open Access
Jiajia Yue,

Li Zhou,

Juan Du

et al.

Water, Journal Year: 2024, Volume and Issue: 16(15), P. 2161 - 2161

Published: July 31, 2024

Runoff simulation is essential for effective water resource management and plays a pivotal role in hydrological forecasting. Improving the quality of runoff forecasting continues to be highly relevant research area. The complexity terrain scarcity long-term observation data have significantly limited application Physically Based Models (PBMs) Qinghai–Tibet Plateau (QTP). Recently, Long Short-Term Memory (LSTM) network has been found learning dynamic characteristics watersheds outperforming some traditional PBMs simulation. However, extent which LSTM works data-scarce alpine regions remains unclear. This study aims evaluate applicability basins QTP, as well performance transfer-based (T-LSTM) regions. Lhasa River Basin (LRB) Nyang (NRB) were areas, model was compared that by relying solely on meteorological inputs. results show average values Nash–Sutcliffe efficiency (NSE), Kling–Gupta (KGE), Relative Bias (RBias) B-LSTM 0.80, 0.85, 4.21%, respectively, while corresponding G-LSTM 0.81, 0.84, 3.19%. In comparison PBM- Block-Wise use TOPMEDEL (BTOP), an enhancement 0.23, 0.36, −18.36%, respectively. both basins, outperforms BTOP model. Furthermore, transfer learning-based at multi-watershed scale demonstrates that, when input are somewhat representative, even if amount limited, T-LSTM can obtain more accurate than models specifically calibrated individual watersheds. result indicates effectively improve applied

Language: Английский

Citations

3
Associations between deep learning runoff predictions and hydrogeological conditions in Australia DOI Creative Commons
Stephanie Clark, Jasmine B.D. Jaffrés

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132569 - 132569

Published: Dec. 1, 2024

Language: Английский

Citations

2
Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction DOI
Chao Deng, Peiyuan Sun, Xin Yin

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121299 - 121299

Published: June 1, 2024

Language: Английский

Citations

1
Water Inflow Forecasting Based on Visual MODFLOW and GS-SARIMA-LSTM Methods DOI Open Access
Yang Zhao,

Donglin Dong,

Yuqi Chen

et al.

Water, Journal Year: 2024, Volume and Issue: 16(19), P. 2749 - 2749

Published: Sept. 27, 2024

Mine water inflow is a significant safety concern in coal mine operations. Accurately predicting the volume of vital for ensuring and environmental protection. This study focused on Laohutai mining area Liaoning, China, to reduce reliance hydrogeological parameters prediction process. An integrated approach combining grid search (GS) with Seasonal Autoregressive Integrated Moving Average (SARIMA) Long Short-Term Memory (LSTM) model was proposed, its results were compared Visual MODFLOW. The used optimize SARIMA model, modeling linear component nine years data, remaining six months data validation. Subsequently, residuals from input into LSTM capture nonlinear features enhance generalization capability stability by introducing Dropout, EarlyStopping, Adam optimizer. effectively handles long-term trends seasonal fluctuations while overcoming limitations capturing periodicity complex time series data. indicated that GC-SARIMA-LSTM performs better than MODFLOW numerical simulation software inflow. Therefore, without parameters, can serve as an effective tool short-term prediction, advancing application deep learning forecasting providing reliable technical support hazard prevention.

Language: Английский

Citations

1
Strategies for Implementing Deep Learning Techniques for Rainfall-Runoff Modeling in a River Having Sparse Data DOI

Divya Chandran,

N. R. Chithra

Published: Jan. 1, 2024

Language: Английский

Citations

0