1,720,954 research outputs found
Hindcasting the Occurrence Time of Major Earthquakes using Machine Learning and Time Series Analysis
No full textAn earthquake is an intense shaking of the ground that typically occurs when tectonic plates move beneath the surface of the Earth. Scientists analyze historical seismic records and geophysical and atmospheric signs to build models estimating the probability of major earthquakes by detecting patterns and anomalies in data such as ground deformation and seismic waves. This study would help in predicting earthquakes to minimize risks of people and buildings. The present study investigates the devastating earthquakes along the Chilean subduction zone in South America, linking non-seismic data to machine learning predictions of Outgoing Longwave Radiation (OLR) and Relative Humidity (RH) anomalies.
Tectonic activity is highly variable in space, therefore the study region must be defined. The first stage which is getting the clusters done by the proposed method Local Maxima-based Spatio Cluster Analysis Network (LMSCAN). In this clustering method, the main quake are taken into consideration to classify the data into the seismology parameters of interest (magnitude and latitude) and the grouping of microshocks. To assess the efficacy of the clustering process, the effectiveness of their proposed technique will be measured against of conventional clustering algorithms such as K-means, Agglomerative Hierarchical Clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
By providing detection of Outgoing Longwave Radiation (OLR) as non-seismic precursor, the Singular Spectrum Analysis - Percentile-median Absolute Deviation Method (SSA-PADM) has been proposed, proving a new technique, for predicting the anomaly activity up to 6 months prior to the occurrence of major earthquakes. The existing techniques which include Isolation Forest, 2 Sigma, Median Absolute Deviation and Percentile algorithms are compared for performance against this technique on detecting anomalies preceding a major earthquake.
Moreover, the correlation of atmospheric parameters, OLR and RH are also implicated as predictors of the estimated seismic events through the Proposed Atmospheric and Radiative Anomaly Detection (ARAD) approach. The analysis explores the relationship between the drop of RH flux index value related to the raise of the flux index of OLR near the epicenter as a possible precursor of major earthquakes, with advance times from 3 to 40 days. Accuracy improvements are found compared to wellknown methods like One-Class SVM (Support Vector Machine), Elliptic Envelope and Isolation Forest.
With OLR and RH considered reliable predictors, the atmospheric variables are being forecasted with a hybrid machine learning model called Multi-Layer Perceptron with Expanded Window Cross-Validation (MLP-EWCV). The established methods, including Extreme Gradient Boosting (XGBoost), Random Forest Regressor, and Support Vector Regression (SVR), were used for comparison in order to evaluate the improvement in hindcasting accuracy and efficiency offered by the MLP-EWCV model. The study of anomalous behaviour of OLR and RH may help to detect anomalies before earthquakes, thereby possibly functioning as an early warning for disaster management systems. The current research study attempts to understand Chile (South America) possible micro shocks with respect to tectonic model as a whole because it falls in the inter plate region where usually micro shocks are observed just before the major earthquakes
Hindcasting the Occurrence Time of Major Earthquakes using Machine Learning and Time Series Analysis
No full textAn earthquake is an intense shaking of the ground that typically occurs when tectonic plates move beneath the surface of the Earth. Scientists analyze historical seismic records and geophysical and atmospheric signs to build models estimating the probability of major earthquakes by detecting patterns and anomalies in data such as ground deformation and seismic waves. This study would help in predicting earthquakes to minimize risks of people and buildings.
The present study investigates the devastating earthquakes along the Chilean subduction zone in South America, linking non-seismic data to machine learning predictions of Outgoing Longwave Radiation (OLR) and Relative Humidity (RH) anomalies. Tectonic activity is highly variable in space, therefore the study region must be defined. The first stage which is getting the clusters done by the proposed method Local Maxima-based Spatio Cluster Analysis Network (LMSCAN). In this clustering method, the main quake are taken into consideration to classify the data into the seismology parameters of interest (magnitude and latitude) and the grouping of microshocks. To assess the efficacy of the clustering process, the effectiveness of their proposed technique will be measured against of conventional clustering algorithms such as K-means, Agglomerative Hierarchical Clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
By providing detection of Outgoing Longwave Radiation (OLR) as non-seismic precursor, the Singular Spectrum Analysis - Percentile-median Absolute Deviation Method (SSA-PADM) has been proposed, proving a new technique, for predicting the anomaly activity up to 6 months prior to the occurrence of major earthquakes.
The existing techniques which include Isolation Forest, 2 Sigma, Median Absolute Deviation and Percentile algorithms are compared for performance against this technique on detecting anomalies preceding a major earthquake. Moreover, the correlation of atmospheric parameters, OLR and RH are also implicated as predictors of the estimated seismic events through the Proposed Atmospheric and Radiative Anomaly Detection (ARAD) approach. The analysis explores the relationship between the drop of RH flux index value related to the raise of the flux index of OLR near the epicenter as a possible precursor of major earthquakes, with advance times from 3 to 40 days.
Accuracy improvements are found compared to wellknown methods like One-Class SVM (Support Vector Machine), Elliptic Envelope and Isolation Forest. With OLR and RH considered reliable predictors, the atmospheric variables are being forecasted with a hybrid machine learning model called Multi-Layer Perceptron with Expanded Window Cross-Validation (MLP-EWCV). The established methods, including Extreme Gradient Boosting (XGBoost), Random Forest Regressor, and Support Vector Regression (SVR), were used for comparison in order to evaluate the improvement in hindcasting accuracy and efficiency offered by the MLP-EWCV model.
The study of anomalous behaviour of OLR and RH may help to detect anomalies before earthquakes, thereby possibly functioning as an early warning for disaster management systems. The current research study attempts to understand Chile (South America) possible micro shocks with respect to tectonic model as a whole because it falls in the inter plate region where usually micro shocks are observed just before the major earthquakes
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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