Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder

Jahangir, Mohammad Sina; Quilty, John

doi:10.1016/j.jhydrol.2023.130498

Cited by 4 publications

(3 citation statements)

References 70 publications

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“…CC BY 4.0 License. Sina Jahangir & Quilty, 2023;Vu et al, 2023). Most often, these approaches are applied to rainfall-runoff modelling due to the availability of long-term runoff data.…”

Section: Introduction 35mentioning

confidence: 99%

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Chidepudi,

Massei,

Jardani

et al. 2024

Preprint

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Abstract. In this study, we used deep learning models with recurrent structure neural networks to simulate large-scale groundwater level (GWL) fluctuations in northern France. We developed a multi-station collective training for GWL simulations, using both “dynamic” variables (i.e. climatic) and static aquifer characteristics. This large-scale approach offers the possibility of incorporating dynamic and static features to cover more reservoir heterogeneities in the study area. Further, we investigated the performance of relevant feature extraction techniques such as clustering and wavelet transform decomposition, intending to simplify network learning using regionalised information. Several modelling performance tests were conducted. Models specifically trained on different types of GWL, clustered based on the spectral properties of the data, performed significantly better than models trained on the whole dataset. Clustering-based modelling reduces complexity in the training data and targets relevant information more efficiently. Applying multi-station models without prior clustering can lead the models to learn the dominant station behavior preferentially, ignoring unique local variations. In this respect, wavelet pre-processing was found to partially compensate clustering, bringing out common temporal and spectral characteristics shared by all available time series even when these characteristics are “hidden” because of too small amplitude. When employed along with prior clustering, thanks to its capability of capturing essential features across all time scales (high and low), wavelet decomposition used as a pre-processing technique provided significant improvement in model performance, particularly for GWLs dominated by low-frequency variations. This study advances our understanding of GWL simulation using deep learning, highlighting the importance of different model training approaches, the potential of wavelet preprocessing, and the value of incorporating static attributes.

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“…CC BY 4.0 License. Sina Jahangir & Quilty, 2023;Vu et al, 2023). Most often, these approaches are applied to rainfall-runoff modelling due to the availability of long-term runoff data.…”

Section: Introduction 35mentioning

confidence: 99%

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Chidepudi,

Massei,

Jardani

et al. 2024

Preprint

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show abstract

“…In the context of streamflow forecasting, the utilization of groundwater level data has become increasingly significant, underpinned by the hydrological principle of the interaction between groundwater and surface water. The variation in groundwater levels not only reflects the recharge of groundwater resources following precipitation infiltration but also indicates the contribution of groundwater to river flow, especially in predicting and mitigating drought impacts [4,5].…”

Section: Introductionmentioning

confidence: 99%

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Zhang,

Zhou,

Deng

et al. 2024

Water

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Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

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“…CC BY 4.0 License. Sina Jahangir & Quilty, 2023;Vu et al, 2023). Most often, these approaches are applied to rainfall-runoff modelling due to the availability of long-term runoff data.…”

Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder

Cited by 4 publications

References 70 publications

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

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