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

2018 ◽  
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

Rainfall-Runoff Modelling Based on Long Short-Term Memory (LSTM)

2019 ◽  
Author(s):  
WEIHONG LIAO ◽  
ZHAOKAI YIN ◽  
RUOJIA WANG ◽  
XIAOHUI LEI
Keyword(s):  
Short Term Memory ◽  
Rainfall Runoff ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Toward Downscaling Storage-Discharge Dynamics: Training Long Short-Term Memory (LSTM) model for Simulating Nonlinear Storage-Discharge Relations at The Rum River Watershed, MN

2021 ◽  
Author(s):  
Pai-Feng Teng ◽  
John Nieber
Keyword(s):  
Short Term Memory ◽  
Water Storage ◽  
Rainfall Runoff ◽  
Total Water ◽  
Short Term ◽  
Term Memory ◽  
Non Linear ◽  
Long Short Term Memory ◽  
The Relationship ◽  
Hydrological Variables

<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 (ΔS) 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 ΔS 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>

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

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.

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

2018 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Claire Brenner ◽  
Karsten Schulz ◽  
Mathew Herrnegger
Keyword(s):  
Short Term Memory ◽  
Regional Scale ◽  
Model Performance ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Soil Moisture Accounting ◽  
Long Short Term Memory

Abstract. Rainfall-runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a novel data driven approach, using the Long-Short-Term-Memory (LSTM) network, a special type of recurrent neural networks. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model, in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA + Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.

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