scholarly journals Timing of the solar wind propagation delay between L1 and Earth based on machine learning

2021 ◽  
Author(s):  
Carsten Baumann ◽  
Aoife E. McCloskey
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Wind Speed ◽  
Space Weather ◽  
Solar Wind Speed ◽  
Propagation Delay ◽  
Learning Approach ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Gb Model

Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents  are severe threats originating from space weather. Having knowledge of potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the Advanced Composition Explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min with respect to the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50 % and 15 % respectively. To increase the confidence in the predictions of the trained GB model, we perform a operational validation, provide drop-column feature importance and analyse the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.

Solar wind propagation delay predictions between L1 and Earth based on machine learning

2021 ◽  
Author(s):  
Carsten Baumann ◽  
Aoife E. McCloskey
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
Wind Speed ◽  
Lead Time ◽  
Solar Wind Speed ◽  
Propagation Delay ◽  
Learning Model ◽  
Interplanetary Shocks ◽  
Feature Sets ◽  
Machine Learning Model

<p>GNSS positioning errors, spacecraft operations failures and power outages potentially originate from space weather in general and the solar wind interaction with the geomagnetic field in particular. Depending on the solar wind speed, information from L1 solar wind monitor spacecraft only give a lead time to take safety measures between 20 and 90 minutes.  This very short lead time requires end users to have the most reliable warnings when potential impacts will actually occur. In this study we present a machine learning algorithm that is suitable to predict the solar wind propagation delay between Lagrangian point L1 and the Earth.  This work introduces the proposed algorithm and investigates its operational applicability to a realtime scenario.</p><p>The propagation delay is measured from interplanetary shocks passing the Advanced Composition Explorer (ACE) first and their sudden commencements within the magnetosphere later, as recorded by ground-based magnetometers. Overall 380 interplanetary shocks with data ranging from 1998 to 2018 builds up the database that is used to train the machine learning model. We investigate two different feature sets. The training of one machine learning model DSCOVR real time solar wind (RTSW) like data which contains all three components of solar wind speed is used. For the other machine learning model ACE RTSW like data which only provide bulk solar wind speed will be used for training. Both feature sets also contain the position of the spacecrafts. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the different features have to be fed into the algorithms only and the evaluation of the propagation delay can be continuous.</p><p>Both machine learning models will be validated against a simple convective solar wind propagation delay model as it is also used in operational space weather centers. For this purpose time periods will be investigated where L1 spacecraft and Earth satellites just outside the magnetosphere probe the same features of the interplanetary magnetic field. This method allows a detailed validation of the solar wind propagation delay apart from the technique that relies on interplanetary shocks.</p>

Prediction and understanding of soft proton contamination in XMM-Newton: a machine learning approach

2021 ◽  
Author(s):  
Elena Kronberg ◽  
Fabio Gastaldello ◽  
Stein Haaland ◽  
Artem Smirnov ◽  
Max Berrendorf ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Solar Wind ◽  
Southern Hemisphere ◽  
Solar Wind Speed ◽  
Flank Region ◽  
Learning Approach ◽  
X Ray ◽  
Machine Learning Approach ◽  
Field Lines

<p>One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes flying mainly in the magnetosphere are soft protons with few tens to hundreds of keV concentrated. One such telescope is the X-ray Multi-Mirror Mission (XMM-Newton) by ESA. Its observing time lost due to the contamination is  about 40%. This affects all the major broad science goals of XMM, ranging from cosmology to astrophysics of neutron stars and black holes. The soft proton background could dramatically impact future X-ray missions such Athena and SMILE missions. Magnetopsheric processes that trigger this background are still poorly understood. We use a machine learning approach to delineate related important parameters and to develop a model to predict the background contamination using 12 years of XMM observations. As predictors we use the location of XMM, solar and geomagnetic activity parameters. We revealed that the contamination is most strongly related to the distance in southern direction, ZGSE, (XMM observations were in the southern hemisphere), the solar wind velocity and the location on the magnetospheric magnetic field lines. We derived simple empirical models for the best two individual predictors and a machine learning model which utilizes an ensemble of the predictors (Extra Trees Regressor) and gives better performance. Based on our analysis, future X-Ray missions in the magnetosphere should minimize observations during  times  associated with high solar wind speed  and avoid closed magnetic field lines, especially at the dusk flank region at least in the southern hemisphere. </p>

scholarly journals Modeling of apartment prices in a Colombian context from a machine learning approach with stable-important attributes

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 63-72
Author(s):  
Jorge Iván Pérez Rave ◽  
Favián González Echavarría ◽  
Juan Carlos Correa Morales
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Approach ◽  
Predictive Capability ◽  
Predictive Capacity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Property Price ◽  
Object Of Study ◽  
Online Pricing

The objective of this work is to develop a machine learning model for online pricing of apartments in a Colombian context. This article addresses three aspects: i) it compares the predictive capacity of linear regression, regression trees, random forest and bagging; ii) it studies the effect of a group of text attributes on the predictive capability of the models; and iii) it identifies the more stable-important attributes and interprets them from an inferential perspective to better understand the object of study. The sample consists of 15,177 observations of real estate. The methods of assembly (random forest and bagging) show predictive superiority with respect to others. The attributes derived from the text had a significant relationship with the property price (on a log scale). However, their contribution to the predictive capacity was almost nil, since four different attributes achieved highly accurate predictions and remained stable when the sample change.

scholarly journals Can machine learning model with static features be fooled: an adversarial machine learning approach

2020 ◽  
Vol 23 (4) ◽  
pp. 3233-3253 ◽  
Author(s):  
Rahim Taheri ◽  
Reza Javidan ◽  
Mohammad Shojafar ◽  
P. Vinod ◽  
Mauro Conti
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Learning Approach ◽  
Machine Learning Model ◽  
Machine Learning Approach

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

scholarly journals Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Kazuya Fujihara ◽  
Yasuhiro Matsubayashi ◽  
Mayuko Harada Yamada ◽  
Masahiko Yamamoto ◽  
Toshihiro Iizuka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Logistic Regression ◽  
Gold Standard ◽  
Learning Approach ◽  
Insulin Initiation ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
General Physicians

BACKGROUND Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings. OBJECTIVE The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes. METHODS Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists’ registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 for the dichotomous classification. Accuracy, recall, and area under the receiver operating characteristic curve (AUC) were calculated to compare the ability of machine learning models to make decisions regarding insulin initiation to the decision-making ability of logistic regression and general physicians. By comparing the decision-making ability of machine learning and logistic regression to that of general physicians, 7 cases were chosen based on patient information as the gold standard based on the agreement of 8 of the 9 specialists. RESULTS The AUCs, accuracy, and recall of logistic regression were higher than those of machine learning (AUCs of 0.89-0.90 for logistic regression versus 0.67-0.74 for machine learning). When the examination was limited to cases receiving insulin, discrimination by machine learning was similar to that of logistic regression analysis (recall of 0.05-0.68 for logistic regression versus 0.11-0.52 for machine learning). Accuracies of logistic regression, a machine learning model (downsampling ratio of 1:8), and general physicians were 0.80, 0.70, and 0.66, respectively, for 43 randomly selected cases. For the 7 gold standard cases, the accuracies of logistic regression and the machine learning model were 1.00 and 0.86, respectively, with a downsampling ratio of 1:8, which were higher than the accuracy of general physicians (ie, 0.43). CONCLUSIONS Although we found no superior performance of machine learning over logistic regression, machine learning had higher accuracy in prediction of insulin initiation than general physicians, defined by diabetes specialists’ choice of the gold standard. Further study is needed before the use of machine learning–based decision support systems for insulin initiation can be incorporated into clinical practice.

scholarly journals Echo state network model for analyzing solar-wind effects on the AU and AL indices

2022 ◽  
Vol 40 (1) ◽  
pp. 11-22
Author(s):  
Shin'ya Nakano ◽  
Ryuho Kataoka
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Wind Speed ◽  
Solar Wind Speed ◽  
Synthetic Data ◽  
Nonlinear Effects ◽  
Echo State Network ◽  
Solar Wind Density ◽  
Machine Learning Model ◽  
Solar Wind Parameters

Abstract. The properties of the auroral electrojets are examined on the basis of a trained machine-learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

scholarly journals Automated doubt identification from informal reflections through hybrid sentic patterns and machine learning approach

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Siaw Ling Lo ◽  
Kar Way Tan ◽  
Eng Lieh Ouh
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Personalized Learning ◽  
Feedback Mechanism ◽  
Pattern Detection ◽  
Learning Approach ◽  
Timely Manner ◽  
Learner Centered ◽  
Machine Learning Model ◽  
Machine Learning Approach

AbstractDo my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ informal reflections. The encouraging results clearly show that the hybrid approach has the potential to be adopted in the real-world doubt detection. Using reflections as a feedback mechanism and automated doubt detection can pave the way to a promising approach for learner-centered teaching and personalized learning.

scholarly journals Several special conditions in the solar wind fora supersubstorm appearance.

2020 ◽  
Author(s):  
I.V. Despirak ◽  
◽  
A.A. Lubchich ◽  
N.G. Kleimenova ◽  
◽  
...  
Keyword(s):  
Solar Wind ◽  
Wind Speed ◽  
Data Base ◽  
Space Weather ◽  
Large Scale ◽  
Dynamic Pressure ◽  
Solar Wind Speed ◽  
Weather Conditions ◽  
The Other ◽  
Solar Wind Parameters

Analysis of the space weather conditions associated with supersubstorms (SSS) was carried out. Two magnetic storms, on 11 April and on 18 April 2001 have been studied and compared. During the first storm, there were registered twoevents of the supersubstorms with intensity of the SML index ~2000-3000 nT, whereas during the second storm there were observed two intense substorms with SML ~ 1500 nT. Solar wind conditions before appearance of the SSSs and intense substorms were compared. For this purpose, the OMNI data base, the catalog of large-scale solar wind phenomena and the data from the magnetic ground-based stations of the SuperMAG network (http://supermag.jhuapl.edu/) were combined. It was shown that the onsets of the SSS event were preceded by strong jumps in the dynamic pressure and density of the solar wind, which were observed against the background of the high solar wind speed and high values of the southern ВZcomponent of the IMF. Comparison with the usual substorms showed thatsome solar wind parameters were higher before SSSs, then before usual substorms: the dynamic pressure, the speed and the magnitude of IMF. On the other hand, the PC index values was the same for these all substorms, that leads to the conclusion about the possible independence of SSS appearance on the level of solar energy penetrated to the magnetosphere.

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