Application of long short-term memory (LSTM) networks for rainfall-runoff simulation in Vu Gia–Thu Bon catchment, Vietnam

<p>Flooding is one of the most financially devastating natural hazards in the world. Studying storage-discharge relations can have the potential to improve existing flood forecasting systems, which are based on rainfall-runoff models. This presentation will assess the non-linear relation between daily water storage (&#916;S) and discharge (Q) simulated by physical-based hydrological models at the Rum River Watershed, a HUC8 watershed in Minnesota, between 1995-2015, by training Long Short-Term Memory (LSTM) networks and other machine learning (ML) algorithms. Currently, linear regression models do not adequately represent the relationship between the simulated total &#916;S and total Q at the HUC-8 watershed (R<sup>2</sup> = 0.3667). Since ML algorithms have been used for predicting the outputs that represent arbitrary non-linear functions between predictors and predictands, they will be used for improving the accuracy of the non-linear relation of the storage-discharge dynamics. This research will mainly use LSTM networks, the time-series deep learning neural network that has already been used for predicting rainfall-runoff relations. The LSTM network will be trained to evaluate the storage-discharge relationship by comparing two sets of non-linear hydrological variables simulated by the semi-distributed Hydrological Simulated Program-Fortran (HSPF): the relationship between the simulated discharges and input hydrological variables at selected HUC-8 watersheds, including air temperatures, cloud covers, dew points, potential evapotranspiration, precipitations, solar radiations, wind speeds, and total water storage, and the dynamics between simulated discharge and input variables that do not include the total water storage. The result of this research will lay the foundation for assessing the accuracy of downscaled storage-discharge dynamics by applying similar methods to evaluate the storage-discharge dynamics at small-scaled, HUC-12 watersheds. Furthermore, its results have the potentials for us to evaluate whether downscaling of storage-discharge dynamics at the HUC-12 watershed can improve the accuracy of predicting discharge by comparing the result from the HUC-8 and the HUC-12 watersheds.</p>

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Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Hydrology and Earth System Sciences ◽

10.5194/hess-25-5517-2021 ◽

2021 ◽

Vol 25 (10) ◽

pp. 5517-5534

Author(s):

Thomas Lees ◽

Marcus Buechel ◽

Bailey Anderson ◽

Louise Slater ◽

Steven Reece ◽

...

Keyword(s):

Great Britain ◽

Conceptual Models ◽

Short Term Memory ◽

Rainfall Runoff ◽

Short Term ◽

Term Memory ◽

Advantages And Disadvantages ◽

North East ◽

Time Series Modelling ◽

Long Short Term Memory

Abstract. Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have demonstrated the applicability of LSTM-based models for rainfall–runoff modelling; however, LSTMs have not been tested on catchments in Great Britain (GB). Moreover, opportunities exist to use spatial and seasonal patterns in model performances to improve our understanding of hydrological processes and to examine the advantages and disadvantages of LSTM-based models for hydrological simulation. By training two LSTM architectures across a large sample of 669 catchments in GB, we demonstrate that the LSTM and the Entity Aware LSTM (EA LSTM) models simulate discharge with median Nash–Sutcliffe efficiency (NSE) scores of 0.88 and 0.86 respectively. We find that the LSTM-based models outperform a suite of benchmark conceptual models, suggesting an opportunity to use additional data to refine conceptual models. In summary, the LSTM-based models show the largest performance improvements in the north-east of Scotland and in south-east of England. The south-east of England remained difficult to model, however, in part due to the inability of the LSTMs configured in this study to learn groundwater processes, human abstractions and complex percolation properties from the hydro-meteorological variables typically employed for hydrological modelling.

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Comments to "Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"

10.5194/hess-2020-540-rc1 ◽

2020 ◽

Author(s):

Jens Kiesel

Keyword(s):

Short Term Memory ◽

Rainfall Runoff ◽

Short Term ◽

Term Memory ◽

Multiple Timescales ◽

Runoff Prediction ◽

Memory Network ◽

Long Short Term Memory

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Rainfall-Runoff modelling using Long Short-Term Memory (LSTM) networks

10.31223/osf.io/qv5jz ◽

2018 ◽

Cited By ~ 1

Author(s):

Frederik Kratzert ◽

Daniel Klotz ◽

Claire Brenner ◽

Karsten Schulz ◽

mathew herrnegger

Keyword(s):

Short Term Memory ◽

Rainfall Runoff ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

Sustainability ◽

10.3390/su132313384 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13384

Author(s):

Majid Mirzaei ◽

Haoxuan Yu ◽

Adnan Dehghani ◽

Hadi Galavi ◽

Vahid Shokri ◽

...

Keyword(s):

Spatial Distribution ◽

Short Term Memory ◽

Data Driven ◽

Rainfall Runoff ◽

Short Term ◽

Runoff Simulation ◽

Physically Based ◽

Long Short Term Memory ◽

Using Data ◽

Modelling Approach

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

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