Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning

Kang, Hoon; Yang, Seunghyeok; Huang, Jianying; Oh, Jeill

doi:10.1007/s12555-019-0984-6

Cited by 62 publications

(28 citation statements)

References 20 publications

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“…This design allows biLSTM to discover additional patterns that cannot be found by LSTM with only one recurrent layer (Siami-Namini et al 2019). In addition, the data used in our study is time series, and biLSTM has shown an improvement over LSTM for general time series forecasting (Althelaya et al 2018;Kang et al 2020). As our experimental results show later, biLSTM also outperforms LSTM in SEP prediction.…”

Section: Prediction Methodsmentioning

confidence: 99%

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Abduallah

Jordanova

Líu

et al. 2022

ApJS

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Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.

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Section: Prediction Methodsmentioning

confidence: 99%

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Abduallah

Jordanova

Líu

et al. 2022

ApJS

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“…Bidirectional models have more parameters and can capture the information from both directions of the sequence (Schuster & Paliwal, 1997). Studies have shown significant improvement of model performance in many sequence learning fields including natural language process (Huang et al, 2015), speech recognition (Graves et al, 2013), and rainfallrunoff modeling (Kang et al, 2020;Lees et al, 2021) The advantage of the LSTM and BiLSTM is that the model parameters will not be affected by the sequence length, but they have a drawback that the computation speed will reduce when the sequence length increase since they need to be calculated sequentially. GTCN and BiGTCN naturally requires more layers to capture long-term information.…”

Section: Sequence Modeling Using Deep Learning In Rainfall-runoffmentioning

confidence: 99%

Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

Xiang¹,

Demir²

2022

Preprint

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Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

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“…Figure 2 represents the LSTM networks used in this article. First, for within-subject classification, a bi-LSTM layer of eight hidden layers was used with two maximum epochs and three mini-batch sizes (Kang et al, 2020). Second, the bi-LSTM layer of 16 hidden layers was used for across-subject classification with three maximum epochs and three mini-batch sizes, see Table 2.…”

Section: Lstmmentioning

confidence: 99%

Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

Yoo

Santosa

Kim

2021

Front. Hum. Neurosci.

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This study aims to decode the hemodynamic responses (HRs) evoked by multiple sound-categories using functional near-infrared spectroscopy (fNIRS). The six different sounds were given as stimuli (English, non-English, annoying, nature, music, and gunshot). The oxy-hemoglobin (HbO) concentration changes are measured in both hemispheres of the auditory cortex while 18 healthy subjects listen to 10-s blocks of six sound-categories. Long short-term memory (LSTM) networks were used as a classifier. The classification accuracy was 20.38 ± 4.63% with six class classification. Though LSTM networks’ performance was a little higher than chance levels, it is noteworthy that we could classify the data subject-wise without feature selections.

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Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning

Cited by 62 publications

References 20 publications

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

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