Long short-term memory for predicting daily suspended sediment concentration

Kaveh, Keivan; Kaveh, Hamid; Bui, Minh Duc; Rutschmann, Peter

doi:10.1007/s00366-019-00921-y

Cited by 36 publications

(14 citation statements)

References 33 publications

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“…In turn, the models developed herein would lay the foundation for other deep learning studies (e.g., for other important variables relevant to water resources management using different types of deep learning approaches) in the Delta, which is a region with tremendous economic, social, and environmental significance. While there are previous studies that involve LSTM estimation of suspended sediment transport elsewhere, they focused on localized events; Kaveh et al (2021) used flow and sediment concentration from a single river gauge and AlDahoul et al (2021) used two gauges along a river to obtain sediment and flow data separately. The LSTM models in this study were trained with spatially diverse measurements from 12 stations.…”

Section: Discussionmentioning

confidence: 99%

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Kim¹,

He²,

Sandhu³

2022

Hydrological Processes

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Sedimentation is an important aspect of water resources management with many implications. Often, process-based methods are employed to predict and assess the amount of sediment in water, but there are still challenges because the mechanisms that govern sediment transport are not yet fully understood. Furthermore, complex domains make model calibration difficult. Thus, as a complementary tool, a machinelearning model was developed in the present study to emulate an existing processbased model in simulating suspended sediment concentration (SSC). It employs the long short-term memory (LSTM) networks, which are a type of artificial neural networks (ANNs) designed for supervised learning of a sequence of data (e.g., time series). The model was applied to the Sacramento-San Joaquin Delta (the Delta) of California, USA, which is characterized by an interconnected system of sloughs, waterways, and a tidal outlet. The model training was performed with historical records of flow, stage and SSC at various locations within the Delta. The study period was 2010 through 2016, but the training period (i.e., range of observed data used to train the model) was varied to assess the model's sensitivity to the inputs and to determine the optimum model setup. Comparison between the model-estimated SSC and the observation at 12 key locations within the Delta showed that the estimation accuracy of the LSTM model during the study period is comparable or superior to that of the Delta Simulation Model II-General Transport Model (DSM2-GTM), a process-based operational hydrodynamics and water quality model for the Delta. In terms of the ratio of the root-mean-square error to the standard deviation (RSR), LSTM models generally showed higher predictability than DSM2-GTM in all test cases investigated, with the lowest (most desirable) and highest (least desirable) LSTM-based RSR being 0.21 and 1.14, respectively. In comparison, the lowest and highest RSR values with DSM2-GTM were 0.26 and 3.70, respectively. The median LSTM-based RSR of all study locations is around 0.7 while its DSM2-GTM counterpart is about 1.0. LSTM models also yielded remarkably higher (more desirable) Nash-Sutcliffe Efficiency values. Moreover, visual inspection found that LSTM models better captured the timing and magnitude of peaks as well as the temporal variations in the SSC time series. The LSTM model's performance was further analysed with hydro-meteorological data (precipitation and wind speed) incorporated in

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Section: Discussionmentioning

confidence: 99%

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Kim¹,

He²,

Sandhu³

2022

Hydrological Processes

View full text Add to dashboard Cite

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“…Long short-term memory (LSTM) Kaveh et al (2021) considered LSTM to predict suspended sediment concentration in the Schuylkill River, USA. In their study, the LSTM expanded the recurring neural network with memory cells to store and output information instead of recurring modules, enabling the learning of long-term relationships.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Sediment load prediction in Johor river: deep learning versus machine learning models

et al. 2023

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Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life’s natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. The predictability of process-driven models may encounter various restrictions throughout the validation process. Given that data-driven models work on the assumption that the underlying physical process is not requisite, this opens up the avenue for AI-based model as alternative modeling. However, AI-based models, such as ANN and SVM, face problems, such as long-term dependency, which require alternative dynamic procedures. Since their performance as universal function approximation depends on their compatibility with the nature of the problem itself, this study investigated several distinct AI-based models, such as long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM), in predicting sediment transport in the Johor river. The collected historical daily sediment transport data from January 1, 2008, to December 01, 2018, through autocorrelation function, were used as input for the model. The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. With RMSE = 11.395, MAE = 18.094, and R2 = 0.914, LSTM outperformed other models in the comparison.

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“…LSTM is a particular type of RNN, which could process and analyze time series [27]. LSTM can learn long-term dependency information.…”

Section: Lstm Modelmentioning

confidence: 99%

Predictive Maintenance for Switch Machine Based on Digital Twins

et al. 2021

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As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance.

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Long short-term memory for predicting daily suspended sediment concentration

Cited by 36 publications

References 33 publications

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

Sediment load prediction in Johor river: deep learning versus machine learning models

Predictive Maintenance for Switch Machine Based on Digital Twins

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