Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model

Xu, Wenxin; Troin, Magali; Zhang, X. C.

doi:10.1007/s11269-022-03216-y

Cited by 20 publications

(8 citation statements)

References 74 publications

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“…This is because the inflow in DJKR is mainly contributed by short‐duration and high‐intensity rainfall events, and the connection between surface water and groundwater is weak due to the relief amplitudes and slopes of the basin, leading to low auto‐correlations of monthly streamflow series. This makes it challenging for CNN‐GRU models to efficiently learn the relationship between monthly streamflow and potential predictors (Xu, Chen, & Zhang, 2022). Besides, it is difficult for CNN‐GRU models to predict the peak flows accurately when training samples are insufficient, especially when training period length is ≤25 years (Xu, Chen, & Zhang, 2022).…”

Section: Resultsmentioning

confidence: 99%

“…This makes it challenging for CNN‐GRU models to efficiently learn the relationship between monthly streamflow and potential predictors (Xu, Chen, & Zhang, 2022). Besides, it is difficult for CNN‐GRU models to predict the peak flows accurately when training samples are insufficient, especially when training period length is ≤25 years (Xu, Chen, & Zhang, 2022).…”

Section: Resultsmentioning

confidence: 99%

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Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions

Xu,

Chen,

Corzo

et al. 2024

Water Resources Research

Self Cite

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This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model‐simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN‐GRU) as the DL model, is applied to predict 1‐, 3‐, and 6‐month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN‐GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN‐GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision‐makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions

Xu,

Chen,

Corzo

et al. 2024

Water Resources Research

Self Cite

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“…A comparison was also made with literature studies investigating the impact of climatic factors and catchment characteristics on the accuracy of river discharge forecasts. Xu et al 41 investigated the spatial and temporal scale effects on the predictive performance of the monthly streamflow prediction, based on a hybrid DL model based on the CNN and GRU algorithms applied to many watersheds around globe. The authors showed how the hybrid DL model performs better on large drainage areas, in agreement with the present study.…”

Section: Discussionmentioning

confidence: 99%

Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm

Nunno

Marinis

Granata

2023

Sci Rep

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In recent years, the growing impact of climate change on surface water bodies has made the analysis and forecasting of streamflow rates essential for proper planning and management of water resources. This study proposes a novel ensemble (or hybrid) model, based on the combination of a Deep Learning algorithm, the Nonlinear AutoRegressive network with eXogenous inputs, and two Machine Learning algorithms, Multilayer Perceptron and Random Forest, for the short-term streamflow forecasting, considering precipitation as the only exogenous input and a forecast horizon up to 7 days. A large regional study was performed, considering 18 watercourses throughout the United Kingdom, characterized by different catchment areas and flow regimes. In particular, the predictions obtained with the ensemble Machine Learning-Deep Learning model were compared with the ones achieved with simpler models based on an ensemble of both Machine Learning algorithms and on the only Deep Learning algorithm. The hybrid Machine Learning-Deep Learning model outperformed the simpler models, with values of R2 above 0.9 for several watercourses, with the greatest discrepancies for small basins, where high and non-uniform rainfall throughout the year makes the streamflow rate forecasting a challenging task. Furthermore, the hybrid Machine Learning-Deep Learning model has been shown to be less affected by reductions in performance as the forecasting horizon increases compared to the simpler models, leading to reliable predictions even for 7-day forecasts.

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“…The application of CNN models for streamflow prediction has received more attention over the last few years, and they have been found to be relatively fast, accurate, and stable alternatives among the growing family of deep learning algorithms [78,[83][84][85].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas

Forghanparast

Mohammadi

2022

Water

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Predicting streamflow in intermittent rivers and ephemeral streams (IRES), particularly those in climate hotspots such as the headwaters of the Colorado River in Texas, is a necessity for all planning and management endeavors associated with these ubiquitous and valuable surface water resources. In this study, the performance of three deep learning algorithms, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Self-Attention LSTM models, were evaluated and compared against a baseline Extreme Learning Machine (ELM) model for monthly streamflow prediction in the headwaters of the Texas Colorado River. The predictive performance of the models was assessed over the entire range of flow as well as for capturing the extreme hydrologic events (no-flow events and extreme floods) using a suite of model evaluation metrics. According to the results, the deep learning algorithms, especially the LSTM-based models, outperformed the ELM with respect to all evaluation metrics and offered overall higher accuracy and better stability (more robustness against overfitting). Unlike its deep learning counterparts, the simpler ELM model struggled to capture important components of the IRES flow time-series and failed to offer accurate estimates of the hydrologic extremes. The LSTM model (K.G.E. > 0.7, R2 > 0.75, and r > 0.85), with better evaluation metrics than the ELM and CNN algorithm, and competitive performance to the SA–LSTM model, was identified as an appropriate, effective, and parsimonious streamflow prediction tool for the headwaters of the Colorado River in Texas.

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Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model

Cited by 20 publications

References 74 publications

Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions

Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions

Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm

Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas

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