Solar Flare Prediction Based on the Fusion of Multiple Deep-learning Models

Abstract Solar flare formation mechanisms and their corresponding predictions have commonly been difficult topics in solar physics for decades. The traditional forecasting method manually constructs a statistical relationship between the measured values of solar active regions and solar flares that cannot fully utilize the information related to solar flares contained in observational data. In this article, we first used neural-network methods driven by the measured magnetogram and magnetic characteristic parameters of the sunspot group to learn the prediction model and predict solar flares. The prediction fusion model is based on a deep neural network, convolutional neural network, and bidirectional long short-term memory neural network and can predict whether a sunspot group will have a flare event above class M or class C in the next 24 or 48 hr. The real skill statistics (TSS) and F1 scores were used to evaluate the performances of our fusion model. The test results clearly show that this fusion model can make full use of the information related to solar flares and combine the advantages of each independent model to capture the evolution characteristics of solar flares, which is a much better performance than traditional statistical prediction models or any single machine-learning method. We also proposed two frameworks, namely F1_FFM and TSS_FFM, which optimize the F1 score and TSS score, respectively. The cross validation results show that they have their respective advantages in the F1 score and TSS score.

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Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Frontiers in Research Metrics and Analytics ◽

10.3389/frma.2021.673928 ◽

2021 ◽

Vol 6 ◽

Author(s):

Tahani Aljohani ◽

Alexandra I. Cristea

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Methodological Approach ◽

High Accuracy ◽

Directional Model ◽

Textual Representations ◽

The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

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A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Mathematics ◽

10.3390/math7100898 ◽

2019 ◽

Vol 7 (10) ◽

pp. 898 ◽

Cited By ~ 12

Author(s):

Suhwan Ji ◽

Jongmin Kim ◽

Hyeonseung Im

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Learning Methods ◽

The Public ◽

Price Prediction ◽

Profitability Analysis ◽

The Media ◽

Long Short Term Memory

Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.

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A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures

PROMET - Traffic&Transportation ◽

10.7307/ptt.v33i4.3709 ◽

2021 ◽

Vol 33 (4) ◽

pp. 593-608

Author(s):

Chuhao Zhou ◽

Peiqun Lin ◽

Xukun Lin ◽

Yang Cheng

Keyword(s):

Neural Network ◽

Road Network ◽

Large Scale ◽

Prediction Models ◽

Short Term Memory ◽

Traffic Data ◽

Traffic Operations ◽

Forecast Horizon ◽

Temporal Characteristics ◽

Long Short Term Memory

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN.

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Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

Computers ◽

10.3390/computers9040099 ◽

2020 ◽

Vol 9 (4) ◽

pp. 99

Author(s):

Sultan Daud Khan ◽

Louai Alarabi ◽

Saleh Basalamah

Keyword(s):

Neural Network ◽

Deep Learning ◽

Time Scale ◽

Prediction Models ◽

Time Series Data ◽

Short Term Memory ◽

Economic Recession ◽

Series Data ◽

Economic Activities ◽

Novel Approach

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.

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Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

Journal of Advanced Transportation ◽

10.1155/2020/8894060 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Shaobo Wang ◽

Pan Zhao ◽

Biao Yu ◽

Weixin Huang ◽

Huawei Liang

Keyword(s):

Neural Network ◽

Knowledge Base ◽

Prior Knowledge ◽

Prediction Models ◽

Short Term Memory ◽

A Priori ◽

Urban Environments ◽

Intelligent Vehicles ◽

Trajectory Prediction ◽

Reasoning System

An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.

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Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147376 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7376

Author(s):

Finn Stevenson ◽

Kentaro Hayasi ◽

Nicola Luigi Bragazzi ◽

Jude Dzevela Kong ◽

Ali Asgary ◽

...

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Prediction Models ◽

Short Term Memory ◽

Alert System ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

The Impact ◽

Early Alert

The impact of the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic has been and is still vast, affecting not only global human health and stretching healthcare facilities, but also profoundly disrupting societal and economic systems worldwide. The nature of the way the virus spreads causes cases to come in further recurring waves. This is due a complex array of biological, societal and environmental factors, including the novel nature of the emerging pathogen. Other parameters explaining the epidemic trend consisting of recurring waves are logistic–organizational challenges in the implementation of the vaccine roll-out, scarcity of doses and human resources, seasonality, meteorological drivers, and community heterogeneity, as well as cycles of strengthening and easing/lifting of the mitigation interventions. Therefore, it is crucial to be able to have an early alert system to identify when another wave of cases is about to occur. The availability of a variety of newly developed indicators allows for the exploration of multi-feature prediction models for case data. Ten indicators were selected as features for our prediction model. The model chosen is a Recurrent Neural Network with Long Short-Term Memory. This paper documents the development of an early alert/detection system that functions by predicting future daily confirmed cases based on a series of features that include mobility and stringency indices, and epidemiological parameters. The model is trained on the intermittent period in between the first and the second wave, in all of the South African provinces.

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Low-dimensional Convolutional Neural Network for Solar Flares GOES Time-series Classification

The Astrophysical Journal Supplement Series ◽

10.3847/1538-4365/ac37bc ◽

2022 ◽

Vol 258 (1) ◽

pp. 12

Author(s):

Vlad Landa ◽

Yuval Reuveni

Keyword(s):

Neural Network ◽

Time Series ◽

Solar Flare ◽

Convolutional Neural Network ◽

Solar Flares ◽

Time Series Data ◽

Series Data ◽

Time Series Classification ◽

X Ray ◽

Low Dimensional

Abstract Space weather phenomena such as solar flares have a massive destructive power when they reach a certain magnitude. Here, we explore the deep-learning approach in order to build a solar flare-forecasting model, while examining its limitations and feature-extraction ability based on the available Geostationary Operational Environmental Satellite (GOES) X-ray time-series data. We present a multilayer 1D convolutional neural network to forecast the solar flare event probability occurrence of M- and X-class flares at 1, 3, 6, 12, 24, 48, 72, and 96 hr time frames. The forecasting models were trained and evaluated in two different scenarios: (1) random selection and (2) chronological selection, which were compared afterward in terms of common score metrics. Additionally, we also compared our results to state-of-the-art flare-forecasting models. The results indicates that (1) when X-ray time-series data are used alone, the suggested model achieves higher score results for X-class flares and similar scores for M-class as in previous studies. (2) The two different scenarios obtain opposite results for the X- and M-class flares. (3) The suggested model combined with solely X-ray time-series fails to distinguish between M- and X-class magnitude solar flare events. Furthermore, based on the suggested method, the achieved scores, obtained solely from X-ray time-series measurements, indicate that substantial information regarding the solar activity and physical processes are encapsulated in the data, and augmenting additional data sets, both spatial and temporal, may lead to better predictions, while gaining a comprehensive physical interpretation regarding solar activity. All source codes are available at https://github.com/vladlanda.

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Combining structured and unstructured data for predictive models: a deep learning approach

10.1101/2020.08.10.20172122 ◽

2020 ◽

Author(s):

Dongdong Zhang ◽

Changchang Yin ◽

Jucheng Zeng ◽

Xiaohui Yuan ◽

Ping Zhang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Medical Information ◽

Prediction Models ◽

Short Term Memory ◽

Heterogeneous Data ◽

Structured Data ◽

Unstructured Data ◽

Data Types ◽

Clinical Notes

Background: The broad adoption of Electronic Health Records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods based on machine learning and deep learning have become increasingly popular in medical informatics. However, while many research studies utilize temporal structured data on predictive modeling, they typically neglect potentially valuable information in unstructured clinical notes. Integrating heterogeneous data types across EHRs through deep learning techniques may help improve the performance of prediction models. Methods: In this research, we proposed 2 general-purpose multi-modal neural network architectures to enhance patient representation learning by combining sequential unstructured notes with structured data. The proposed fusion models leverage document embeddings for the representation of long clinical note documents and either convolutional neural network or long short-term memory networks to model the sequential clinical notes and temporal signals, and one-hot encoding for static information representation. The concatenated representation is the final patient representation which is used to make predictions. Results: We evaluate the performance of proposed models on 3 risk prediction tasks (i.e., in-hospital mortality, 30-day hospital readmission, and long length of stay prediction) using derived data from the publicly available Medical Information Mart for Intensive Care III dataset. Our results show that by combining unstructured clinical notes with structured data, the proposed models outperform other models that utilize either unstructured notes or structured data only. Conclusions: The proposed fusion models learn better patient representation by combining structured and unstructured data. Integrating heterogeneous data types across EHRs helps improve the performance of prediction models and reduce errors.

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Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

Energies ◽

10.3390/en13112857 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2857 ◽

Cited By ~ 1

Author(s):

Yufei Wang ◽

Li Zhu ◽

Hua Xue

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Prediction Models ◽

Short Term Memory ◽

Single Step ◽

Power Prediction ◽

Short Term ◽

Model Based ◽

Emotional Neural Network

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.

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Multi-Step Short-Term Wind Speed Prediction Using a Residual Dilated Causal Convolutional Network with Nonlinear Attention

Energies ◽

10.3390/en13071772 ◽

2020 ◽

Vol 13 (7) ◽

pp. 1772 ◽

Cited By ~ 2

Author(s):

Kumar Shivam ◽

Jong-Chyuan Tzou ◽

Shang-Chen Wu

Keyword(s):

Neural Network ◽

Wind Speed ◽

Convolutional Neural Network ◽

Real World ◽

Prediction Models ◽

Short Term Memory ◽

Short Term ◽

Wind Speed Prediction ◽

Proposed Model ◽

Speed Prediction

Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.

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