Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.

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Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

Energies ◽

10.3390/en13154017 ◽

2020 ◽

Vol 13 (15) ◽

pp. 4017 ◽

Cited By ~ 2

Author(s):

Dukhwan Yu ◽

Wonik Choi ◽

Myoungsoo Kim ◽

Ling Liu

Keyword(s):

Power Generation ◽

Deep Learning ◽

Prediction Accuracy ◽

Short Term Memory ◽

Local Context ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

Long Short Term Memory ◽

Pv Power Generation

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.

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DLI: A Deep Learning-Based Granger Causality Inference

Complexity ◽

10.1155/2020/5960171 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Wei Peng

Keyword(s):

Deep Learning ◽

Granger Causality ◽

Prediction Accuracy ◽

Short Term Memory ◽

Target Variable ◽

Short Term ◽

Term Memory ◽

P Index ◽

Causality Inference ◽

Long Short Term Memory

Integrating autoencoder (AE), long short-term memory (LSTM), and convolutional neural network (CNN), we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference (DLI). Two contributions of the proposed DLI are to reveal the Granger causality between the bitcoin price and S&P index and to forecast the bitcoin price and S&P index with a higher accuracy. Experimental results demonstrate that there is a bidirectional but asymmetric Granger causality between the bitcoin price and S&P index. And the DLI performs a superior prediction accuracy by integrating variables that have causalities with the target variable into the prediction process.

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A comprehensive comparative analysis of deep learning tools for modeling failures in smart water taps

Water Science & Technology Water Supply ◽

10.2166/ws.2021.261 ◽

2021 ◽

Author(s):

N. M. Offiong ◽

Y. Wu ◽

D. Muniandy ◽

F. A. Memon

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Failure Prediction ◽

Water Withdrawal ◽

Short Term ◽

Term Memory ◽

Smart Water ◽

Long Short Term Memory

Abstract Predicting early-stage failure in smart water taps (SWT) and selecting the most efficient tools to build failure prediction models are many challenges that water institutions face. In this study, three Deep Learning (DL) algorithms, i.e., the Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), were selected to analyse and determine the most appropriate among them for failure prediction in SWTs. This study uses a historical dataset acquired from smart water withdrawal taps to determine the most efficient DL neural network architecture for failure prediction in the SWT, leading to a hybrid model's development. After a comprehensive evaluation of the three ML models, findings show that a hybrid combination of the CNN and Bi-LSTM (CNN-BiLSTM) models is a better solution for investigating failures in the SWT.

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Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction

Complexity ◽

10.1155/2020/6689304 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Kaicheng Feng ◽

Xiaobing Liu

Keyword(s):

Prediction Model ◽

Prediction Accuracy ◽

Prediction Models ◽

Short Term Memory ◽

Influence Factors ◽

Short Term ◽

Term Memory ◽

Attention Model ◽

Box Office ◽

Long Short Term Memory

To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment.

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Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models

Applied Sciences ◽

10.3390/app11198995 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8995

Author(s):

Eunju Lee ◽

Dohee Kim ◽

Hyerim Bae

Keyword(s):

Time Series ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Proposed Model ◽

External Variables ◽

Long Short Term Memory ◽

Better Than

The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression. However, these methods do not explain the complexity and variability of data caused by changes in the external environment, such as the global financial crisis and economic fluctuations. Deep learning can explore the inherent patterns of data and analyze the characteristics (time series, external environmental variables, and outliers); hence, the accuracy of deep learning-based volume prediction models is better than that of traditional models. However, this does not include the study of overall trends (upward, steady, or downward). In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. The proposed model explores external variables that are related to container volume, combining port volume time-series decomposition with external variables and deep learning-based multivariate long short-term memory (LSTM) prediction. The results indicate that the proposed model performs better than the traditional LSTM model and follows the trend simultaneously.

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A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)

Multimedia Tools and Applications ◽

10.1007/s11042-021-11176-5 ◽

2021 ◽

Author(s):

Samit Baral ◽

Abeer Alsadoon ◽

P. W. C. Prasad ◽

Sarmad Al Aloussi ◽

Omar Hisham Alsadoon

Keyword(s):

Cardiac Arrest ◽

Deep Learning ◽

Sepsis Patient ◽

Short Term Memory ◽

Early Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden

Österreichische Wasser- und Abfallwirtschaft ◽

10.1007/s00506-021-00768-y ◽

2021 ◽

Author(s):

Claire Brenner ◽

Jonathan Frame ◽

Grey Nearing ◽

Karsten Schulz

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Sich Eine ◽

Short Term ◽

Term Memory ◽

Neuronales Netzwerk ◽

Long Short Term Memory

ZusammenfassungDie Verdunstung ist ein entscheidender Prozess im globalen Wasser‑, Energie- sowie Kohlenstoffkreislauf. Daten zur räumlich-zeitlichen Dynamik der Verdunstung sind daher von großer Bedeutung für Klimamodellierungen, zur Abschätzung der Auswirkungen der Klimakrise sowie nicht zuletzt für die Landwirtschaft.In dieser Arbeit wenden wir zwei Machine- und Deep Learning-Methoden für die Vorhersage der Verdunstung mit täglicher und halbstündlicher Auflösung für Standorte des FLUXNET-Datensatzes an. Das Long Short-Term Memory Netzwerk ist ein rekurrentes neuronales Netzwerk, welchen explizit Speichereffekte berücksichtigt und Zeitreihen der Eingangsgrößen analysiert (entsprechend physikalisch-basierten Wasserbilanzmodellen). Dem gegenüber gestellt werden Modellierungen mit XGBoost, einer Entscheidungsbaum-Methode, die in diesem Fall nur Informationen für den zu bestimmenden Zeitschritt erhält (entsprechend physikalisch-basierten Energiebilanzmodellen). Durch diesen Vergleich der beiden Modellansätze soll untersucht werden, inwieweit sich durch die Berücksichtigung von Speichereffekten Vorteile für die Modellierung ergeben.Die Analysen zeigen, dass beide Modellansätze gute Ergebnisse erzielen und im Vergleich zu einem ausgewerteten Referenzdatensatz eine höhere Modellgüte aufweisen. Vergleicht man beide Modelle, weist das LSTM im Mittel über alle 153 untersuchten Standorte eine bessere Übereinstimmung mit den Beobachtungen auf. Allerdings zeigt sich eine Abhängigkeit der Güte der Verdunstungsvorhersage von der Vegetationsklasse des Standorts; vor allem wärmere, trockene Standorte mit kurzer Vegetation werden durch das LSTM besser repräsentiert, wohingegen beispielsweise in Feuchtgebieten XGBoost eine bessere Übereinstimmung mit den Beobachtung liefert. Die Relevanz von Speichereffekten scheint daher zwischen Ökosystemen und Standorten zu variieren.Die präsentierten Ergebnisse unterstreichen das Potenzial von Methoden der künstlichen Intelligenz für die Beschreibung der Verdunstung.

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Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment

Environmental Science and Pollution Research ◽

10.1007/s11356-021-12877-y ◽

2021 ◽

Author(s):

Sarmad Dashti Latif

Keyword(s):

Compressive Strength ◽

Deep Learning ◽

Short Term Memory ◽

Strength Prediction ◽

Concrete Compressive Strength ◽

Prediction Modeling ◽

Short Term ◽

Term Memory ◽

Sustainable Environment ◽

Long Short Term Memory

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Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

Remote Sensing ◽

10.3390/rs13101953 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1953

Author(s):

Seyed Majid Azimi ◽

Maximilian Kraus ◽

Reza Bahmanyar ◽

Peter Reinartz

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Object Tracking ◽

Short Term Memory ◽

Aerial Imagery ◽

Future Research ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.

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