Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement
Abstract The accuracy of peak streamflow simulation is often lower than that of normal streamflow simulation, posing a significant challenge. This study introduces stochastic resonance (SR) to enhance simulation accuracy, utilizing its ability to leverage noise energy to improve correlations between...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024WR039659 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850211967412731904 |
|---|---|
| author | Xungui Li Jian Sun Qiyong Yang Yi Tian Xiaoli Yang |
| author_facet | Xungui Li Jian Sun Qiyong Yang Yi Tian Xiaoli Yang |
| author_sort | Xungui Li |
| collection | DOAJ |
| description | Abstract The accuracy of peak streamflow simulation is often lower than that of normal streamflow simulation, posing a significant challenge. This study introduces stochastic resonance (SR) to enhance simulation accuracy, utilizing its ability to leverage noise energy to improve correlations between streamflow and meteorological factors. The proposed SR‐LSTM model, validated across major Chinese basins, demonstrates that SR effectively enhances the accuracy of streamflow simulations. By using SR, the Nash‐Sutcliffe efficiency increased from 0.70 to 0.79, and the kling‐gupta efficiency improved from 0.69 to 0.82. Furthermore, this study utilizes the global Caravan streamflow data set (including CAMELES, CAMELESBR, CAMELESAUS, and LamaH) comprising 1,244 station data points to validate the applicability of SR‐LSTM. Results indicate that SR improves accuracy at approximately 70% of 1,244 stations, particularly in regions with high‐quality data. Comparative analysis shows that incorporating SR enhances the performance of deep learning models, highlighting its potential for improving both global and peak streamflow simulation accuracy. These findings underscore the effectiveness of SR in enhancing streamflow simulation accuracy. |
| format | Article |
| id | doaj-art-8a178f551e674110961eaf0a5c19f19c |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-8a178f551e674110961eaf0a5c19f19c2025-08-20T02:09:27ZengWileyWater Resources Research0043-13971944-79732025-03-01613n/an/a10.1029/2024WR039659Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation EnhancementXungui Li0Jian Sun1Qiyong Yang2Yi Tian3Xiaoli Yang4State Key Laboratory of Featured Metal Materials and Life‐cycle Safety for Composite Structures Guangxi University Nanning ChinaState Key Laboratory of Featured Metal Materials and Life‐cycle Safety for Composite Structures Guangxi University Nanning ChinaState Key Laboratory of Featured Metal Materials and Life‐cycle Safety for Composite Structures Guangxi University Nanning ChinaState Key Laboratory of Featured Metal Materials and Life‐cycle Safety for Composite Structures Guangxi University Nanning ChinaState Key Laboratory of Featured Metal Materials and Life‐cycle Safety for Composite Structures Guangxi University Nanning ChinaAbstract The accuracy of peak streamflow simulation is often lower than that of normal streamflow simulation, posing a significant challenge. This study introduces stochastic resonance (SR) to enhance simulation accuracy, utilizing its ability to leverage noise energy to improve correlations between streamflow and meteorological factors. The proposed SR‐LSTM model, validated across major Chinese basins, demonstrates that SR effectively enhances the accuracy of streamflow simulations. By using SR, the Nash‐Sutcliffe efficiency increased from 0.70 to 0.79, and the kling‐gupta efficiency improved from 0.69 to 0.82. Furthermore, this study utilizes the global Caravan streamflow data set (including CAMELES, CAMELESBR, CAMELESAUS, and LamaH) comprising 1,244 station data points to validate the applicability of SR‐LSTM. Results indicate that SR improves accuracy at approximately 70% of 1,244 stations, particularly in regions with high‐quality data. Comparative analysis shows that incorporating SR enhances the performance of deep learning models, highlighting its potential for improving both global and peak streamflow simulation accuracy. These findings underscore the effectiveness of SR in enhancing streamflow simulation accuracy.https://doi.org/10.1029/2024WR039659streamflow simulationstochastic resonancelong short‐term memory neural networkenhancement effectnovel streamflow simulation modelnoise energy enhancement |
| spellingShingle | Xungui Li Jian Sun Qiyong Yang Yi Tian Xiaoli Yang Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement Water Resources Research streamflow simulation stochastic resonance long short‐term memory neural network enhancement effect novel streamflow simulation model noise energy enhancement |
| title | Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement |
| title_full | Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement |
| title_fullStr | Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement |
| title_full_unstemmed | Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement |
| title_short | Linking Stochastic Resonance With Long Short‐Term Memory Neural Network for Streamflow Simulation Enhancement |
| title_sort | linking stochastic resonance with long short term memory neural network for streamflow simulation enhancement |
| topic | streamflow simulation stochastic resonance long short‐term memory neural network enhancement effect novel streamflow simulation model noise energy enhancement |
| url | https://doi.org/10.1029/2024WR039659 |
| work_keys_str_mv | AT xunguili linkingstochasticresonancewithlongshorttermmemoryneuralnetworkforstreamflowsimulationenhancement AT jiansun linkingstochasticresonancewithlongshorttermmemoryneuralnetworkforstreamflowsimulationenhancement AT qiyongyang linkingstochasticresonancewithlongshorttermmemoryneuralnetworkforstreamflowsimulationenhancement AT yitian linkingstochasticresonancewithlongshorttermmemoryneuralnetworkforstreamflowsimulationenhancement AT xiaoliyang linkingstochasticresonancewithlongshorttermmemoryneuralnetworkforstreamflowsimulationenhancement |