An architecture for emergency event prediction using LSTM recurrent neural networks

Cortez, Bitzel; Carrera, Berny; Kim, Young‐Jin; Jung, Jae‐Yoon

doi:10.1016/j.eswa.2017.12.037

Cited by 144 publications

(55 citation statements)

References 24 publications

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“…The ADAM algorithm is used to optimize the weights in each layer which exhibits faster convergence than the conventional stochastic gradient descent [29]. ADAM is a first-order based gradient descent optimization algorithm that is computationally efficient, and is suitable for optimizing models with a large set of parameters.…”

Section: Gradient Descent Algorithmmentioning

confidence: 99%

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Gao

Hong

et al. 2019

Applied Sciences

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Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46-13.46%.

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Section: Gradient Descent Algorithmmentioning

confidence: 99%

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Gao

Hong

et al. 2019

Applied Sciences

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“…e LSTM has been widely used in prediction. Cortez et al [22] proposed the prediction model for emergency event on the basis of LSTM architecture, and made a comparative analysis on the effectiveness of LSTM and traditional time series. e LSTM network was applied to predict out-ofsample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015 [23].…”

Section: Literature Reviewmentioning

confidence: 99%

The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

Gao

Chang

Fang

et al. 2019

Journal of Advanced Transportation

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The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.

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“…The results showed that only the PAM method returned with consistent clusters and was proven to be a robust spatial clustering method for a huge dataset. The PAM clustering has also been researched as a robust method before and compared with other spatial clustering methods, such as k-means and DBSCAN [46]. Sewage outlets from all wastewater-generating factories through the whole administrative region of each province or municipality in the YRB were considered together, because the information published online about river basins and wastewater discharge regulations was either limited or unclear (see online open monitoring information platforms of the specially monitored enterprises of the 7 ARs).…”

Section: Spatial Zoning Of the Wastewater-generating Factories And Thmentioning

confidence: 99%

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Miao

Guo

et al. 2019

Water

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Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

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An architecture for emergency event prediction using LSTM recurrent neural networks

Cited by 144 publications

References 24 publications

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM

The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

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