A hybrid monthly hydrological prediction model based on LSTM-EBLS and improved VMD DOI Creative Commons
Boya Zhou, Lehao Wang, Ying Han

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 11, 2024

Abstract Scarce of large sample data makes deep learning based monthly hydrological prediction still challenging. Compared with methods, broad learn-ing system (BLS) has the advantages fast operation and small suita-bility. While, using BLS alone to predict, accuracy is relatively low. Using weights between input vector output gate in long short-term memory (LSTM) as initial BLS, extended (EBLS) constructed temporal feature extraction module for prediction. Considering time-consuming problem resulting by variational mode decomposition (VMD), an improved version VMD (IVMD) presented this paper. Finally, a hybrid forecast model on LSTM, EBLS IVMD proposed. The trained validated prediction, results demonstrated that: (1) For multi-month ahead outperforms discussed state art models. Meawhile, peak fitting also enhanced. (2) CNN-LSTM structure, LSTM-EBLS improves accuracy. (3) Efficient parameter selection method high correlation signals further enhance computation efficiency.

Language: Английский

Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM DOI
Jun Guo, Yi Liu, Qiang Zou

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 624, P. 129969 - 129969

Published: July 20, 2023

Language: Английский

Citations

120
Deep transfer learning based on transformer for flood forecasting in data-sparse basins DOI

Yuanhao Xu,

Kairong Lin,

Caihong Hu

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 129956 - 129956

Published: July 19, 2023

Language: Английский

Citations

64
Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins DOI
Zhenglei Xie, Wei Xu, Bing Zhu

et al.

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

Published: March 11, 2025

Language: Английский

Citations

1
Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition DOI Creative Commons

Dong-mei Xu,

An-dong Liao,

Wenchuan Wang

et al.

Journal of Hydroinformatics, Journal Year: 2023, Volume and Issue: 26(1), P. 255 - 283

Published: Dec. 11, 2023

Abstract Accurate runoff prediction is vital in efficiently managing water resources. In this paper, a hybrid model combining complete ensemble empirical mode decomposition with adaptive noise, variational decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) proposed. Firstly, CEEMDAN used to decompose the original data, high-frequency component decomposed using VMD. Then, each input into LSTM optimized by CABES for prediction. Finally, results of individual predictions are combined reconstructed produce monthly predictions. The employed predict at Xiajiang hydrological station Yingluoxia station. A comprehensive comparison conducted other models including back propagation (BP), LSTM, etc. assessment model's performance uses four evaluation indexes. Results reveal that CEEMDAN-VMD-CABES-LSTM showcased highest forecast accuracy among all evaluated. Compared single root mean square error (RMSE) absolute percentage (MAPE) decreased 71.09 65.26%, respectively, RMSE MAPE 65.13 40.42%, respectively. R NSEC both sites near 1.

Language: Английский

Citations

20
Machine learning method is an alternative for the hydrological model in an alpine catchment in the Tianshan region, Central Asia DOI Creative Commons
Wenting Liang, Yaning Chen, Gonghuan Fang

et al.

Journal of Hydrology Regional Studies, Journal Year: 2023, Volume and Issue: 49, P. 101492 - 101492

Published: Aug. 1, 2023

Kaidu River catchment in the Tianshan Mountain, northwestern China. This paper compared applicability and accuracy of four machine learning models two hydrological ones to simulate daily streamflow extreme catchment. The are Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Random Forests (RF), Long Short-Term Memory (LSTM), while Soil Water Assessment Tool (SWAT) extended SWAT with a glacier dynamic module (SWAT-Glacier). LSTM achieved better model performance simulating than SWAT-Glacier, Kling-Gupta efficiency 0.92, 0.82, 0.80, respectively. Meanwhile, SVR, XGBoost, RF showed satisfactory performance, KGE 0.67, 0.71, 0.70, LSTM, SWAT-Glacier could well annual peak flow (i.e., maximum 1-day 5-day average streamflow) but failed mimic minimum 7-day streamflow, PBIAS exceeding 28%. Furthermore, all reproduce dates extremes. Nevertheless, using quantile loss function resulted significantly improved low indices, that mean squared error as function. Overall, be good alternative for data-scarce catchments.

Language: Английский

Citations

19
Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models DOI
Yiming Wei, Renchao Wang, Ping Feng

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(7), P. 2471 - 2488

Published: Feb. 10, 2024

Language: Английский

Citations

7
Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique DOI
Xi Yang, Zhihe Chen, Min Qin

et al.

Water Resources Management, Journal Year: 2023, Volume and Issue: 38(1), P. 269 - 286

Published: Dec. 1, 2023

Language: Английский

Citations

15
A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting DOI
Amina Khatun, Chandranath Chatterjee, Gaurav Sahu

et al.

Hydrological Sciences Journal, Journal Year: 2023, Volume and Issue: 68(3), P. 488 - 506

Published: Jan. 27, 2023

ABSTRACTA novel smoothing-based long short-term memory (Smooth-LSTM) framework for flood forecasting up to five days ahead is proposed, and compared with the benchmark LSTM (LSTM) model, an Artificial Neural Network (ANN) conceptual Nedbør Afstrømnings Model (MIKE11 NAM)-Hydrodynamic (HD) (MIKE) hydrological model. This was tested in typical middle Mahanadi River basin (India), which has a tropical monsoon-type climate. Variation of training loss indicated network higher learning ability at smaller batch sizes. The Smooth-LSTM model could predict streamflow Nash-Sutcliffe efficiency 0.82–0.87 lead time better reproduction observed crucial high peak floods, whereas corresponding MIKE, ANN model-based forecasts were acceptable only four-, three- one-day times, respectively. Overall, found be robust operational forecasting, lower uncertainty least sensitivity redundant input information.KEYWORDS: ANNflood forecastinglong (LSTM)MIKE 11 NAM-HDsmoothing windows Editor A. Castellarin Associate O. KisiEditor KisiAcknowledgementsThe authors sincerely thank Hirakud Dam Circle (HDC), Department Water Resources (Prachi Division), Odisha, Central Commission (CWC) India Meteorological (IMD) providing necessary datasets carry out study.Disclosure statementNo potential conflict interest reported by authors.Data availability statementAll data, models, code generated or used during study appear submitted article.Supplementary materialSupplemental data this article can accessed online https://doi.org/10.1080/02626667.2023.2173012

Language: Английский

Citations

11
Review of Machine Learning Methods for River Flood Routing DOI Open Access

Li Li,

Kyung Soo Jun

Water, Journal Year: 2024, Volume and Issue: 16(2), P. 364 - 364

Published: Jan. 22, 2024

River flood routing computes changes in the shape of a wave over time as it travels downstream along river. Conventional models, especially hydrodynamic require high quality and quantity input data, such measured hydrologic series, geometric hydraulic structures, hydrological parameters. Unlike physically based machine learning algorithms, which are data-driven do not much knowledge about underlying physical processes can identify complex nonlinearity between inputs outputs. Due to their higher performance, lower complexity, low computation cost, researchers introduced novel methods single application or hybrid achieve more accurate efficient routing. This paper reviews recent river

Language: Английский

Citations

4
Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System with Machine Learning and Statistical Models DOI Creative Commons
Stella Pantopoulou, M. Weathered, Darius Lisowski

et al.

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 10252 - 10264

Published: Jan. 1, 2025

Language: Английский

Citations

0