Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05172-3
Abstract: Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of… read more here.
Keywords: machine learning; ensemble learning; streamflow forecasting; super ensemble ... See more keywords
Long Term Streamflow Forecasting Using a Hybrid Entropy Model
Sign Up to like & getrecommendations! Published in 2018 at "Water Resources Management"
DOI: 10.1007/s11269-017-1878-0
Abstract: In this paper, the development and evaluation of an entropy based hybrid data driven model coupled with input selection approach and wavelet transformation is investigated for long-term streamflow forecasting with 10 years lead time. To develop… read more here.
Keywords: long term; streamflow forecasting; model; entropy model ... See more keywords
A novel algorithm for feature selection based on geographic distance metric: a case study of streamflow forecasting of Austria’s water resources
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Environmental Science and Technology"
DOI: 10.1007/s13762-019-02485-2
Abstract: This paper focuses on input variable selection—feature selection—methods with the artificial neural network for the streamflow forecasting of large basins that have a variety of numerous stations. The feature selection methods in the current hydrology… read more here.
Keywords: algorithm; feature selection; water; streamflow forecasting ... See more keywords
Ensemble streamflow forecasting based on variational mode decomposition and long short term memory
Sign Up to like & getrecommendations! Published in 2022 at "Scientific Reports"
DOI: 10.1038/s41598-021-03725-7
Abstract: Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to… read more here.
Keywords: streamflow forecasting; decomposition; gbrt; mode decomposition ... See more keywords
Assessing the performance and interpretability of the CNN-LSTM-Attention model for daily streamflow forecasting in typical basins of the eastern Qinghai-Tibet Plateau
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-024-84810-5
Abstract: Hydrological forecasting is of great significance to regional water resources management and reservoir operation. Climate change has increased the complexity and difficulty of hydrological forecasting. In this study, a hybrid explainable streamflow forecasting model based… read more here.
Keywords: cnn lstm; forecasting; lstm attention; model ... See more keywords
Improved daily streamflow forecasting for semi-arid environments using hybrid machine learning and multi-scale analysis techniques
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Hydroinformatics"
DOI: 10.2166/hydro.2024.263
Abstract: This study aimed to improve daily streamflow forecasting by combining machine learning (ML) models with signal decomposition techniques. Four ML models were hybridized with five families of maximum overlap discrete wavelet transforms (MODWTs). The hybrid… read more here.
Keywords: machine learning; model; standalone models; streamflow forecasting ... See more keywords
Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China
Sign Up to like & getrecommendations! Published in 2017 at "Entropy"
DOI: 10.3390/e19110597
Abstract: Monthly streamflow has elements of stochasticity, seasonality, and periodicity. Spectral analysis and time series analysis can, respectively, be employed to characterize the periodical pattern and the stochastic pattern. Both Burg entropy spectral analysis (BESA) and… read more here.
Keywords: entropy spectral; analysis; northwest china; streamflow forecasting ... See more keywords
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Sign Up to like & getrecommendations! Published in 2025 at "Hydrology and Earth System Sciences"
DOI: 10.5194/hess-29-785-2025
Abstract: Abstract. Deep learning models are increasingly being applied to streamflow forecasting problems. Their success is in part attributed to the large and hydrologically diverse datasets on which they are trained. However, common data selection methods… read more here.
Keywords: streamflow forecasting; training data; diversity; selection ... See more keywords