Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning

Global navigation satellite systems (GNSS) provide globally distributed station coordinate time series that can be used for a variety of applications such as the definition of a terrestrial reference frame. A reliable estimation of the coordinate time series trends gives valuable information about station movements during the measured time period. Detecting discontinuities of various origins in such time series is crucial for accurate and robust velocity estimation. At present, there is no fully automated standard method for detecting discontinuities. Instead, discontinuity-catalogues are frequently used, which provide information about when a device was changed or an earthquake occurred. However, it is known that these catalogues suffer from incompleteness. This study investigates the suitability of machine learning classification algorithms that are fully data-driven to detect discontinuities caused by earthquakes in station coordinate time series without the need for external information. For this study, Japan was selected as a testing area. Ten different machine learning algorithms have been tested. It is found that Random Forest achieves the best performance with an F1 score of 0.77, a recall of 0.78, and a precision of 0.76. Overall, 525 of 565 recorded earthquakes in the test data were correctly classified. It is further highlighted that splitting the time series into chunks of 21 days leads to the best performance. Furthermore, it is beneficial to combine the three (normalized) components of the GNSS solution into one sample, and that adding the value range as an additional feature improves the result. Thus, this work demonstrates how it is possible to use machine learning algorithms to detect discontinuities in GNSS time series.

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Detecting earthquakes in GNSS station coordinate time series using machine learning algorithms

10.5194/egusphere-egu21-1975 ◽

2021 ◽

Author(s):

Laura Crocetti ◽

Matthias Schartner ◽

Benedikt Soja

Keyword(s):

Machine Learning ◽

Time Series ◽

Statistical Methods ◽

Large Scale ◽

Learning Algorithms ◽

High Accuracy ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Coordinate Time ◽

Coordinate Time Series

<p>Earthquakes are natural hazards that occur suddenly and without much notice. The most established method of detecting earthquakes is to use a network of seismometers. Nowadays, station positions of the global navigation satellite system (GNSS) can be determined with a high accuracy of a few centimetres or even millimetres. This high accuracy, together with the dense global coverage, makes it possible to also use GNSS station networks to investigate geophysical phenomena such as earthquakes. Absolute ground movements caused by earthquakes are reflected in the GNSS station coordinate time series and can be characterised using statistical methods or machine learning techniques.</p><p>In this work, we have used thousands of time series of GNSS station positions distributed all over the world to detect and classify earthquakes. We apply a variety of machine learning algorithms that enable large-scale processing of the time series in order to identify spatio-temporal patterns. Several machine learning algorithms, including Random Forest, Nearest Neighbours, and Multi-Layer Perceptron, are compared against each other, as well as against classical statistical methods, based on their performance on detecting earthquakes from the station coordinate time series.</p>

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Exploitation of time series Sentinel-2 data and different machine learning algorithms for detailed tree species classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3098817 ◽

2021 ◽

pp. 1-1

Author(s):

Yanbiao Xi ◽

Chunying Ren ◽

Qingjiu Tian ◽

Yongxing Ren ◽

Xinyu Dong ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Tree Species ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Species Classification ◽

Tree Species Classification ◽

Sentinel 2

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Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs

Animals ◽

10.3390/ani11010241 ◽

2021 ◽

Vol 11 (1) ◽

pp. 241

Author(s):

Dongwon Seo ◽

Sunghyun Cho ◽

Prabuddha Manjula ◽

Nuri Choi ◽

Young-Kuk Kim ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Fixation Index ◽

Machine Learning Classification ◽

Genetic Components ◽

Marker Combination ◽

A Genome ◽

Minimum Number ◽

Native Chickens

A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.

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PSIX-15 Assessment of machine learning algorithms for prediction of Aleutian disease in American mink

Journal of Animal Science ◽

10.1093/jas/skab235.484 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 264-265

Author(s):

Duy Ngoc Do ◽

Guoyu Hu ◽

Younes Miar

Keyword(s):

Machine Learning ◽

Random Forest ◽

Linear Models ◽

American Mink ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Enzyme Linked Immunosorbent Assay ◽

Linear Discriminant ◽

Machine Learning Classification

Abstract American mink (Neovison vison) is the major source of fur for the fur industries worldwide and Aleutian disease (AD) is causing severe financial losses to the mink industry. Different methods have been used to diagnose the AD in mink, but the combination of several methods can be the most appropriate approach for the selection of AD resilient mink. Iodine agglutination test (IAT) and counterimmunoelectrophoresis (CIEP) methods are commonly employed in test-and-remove strategy; meanwhile, enzyme-linked immunosorbent assay (ELISA) and packed-cell volume (PCV) methods are complementary. However, using multiple methods are expensive; and therefore, hindering the corrected use of AD tests in selection. This research presented the assessments of the AD classification based on machine learning algorithms. The Aleutian disease was tested on 1,830 individuals using these tests in an AD positive mink farm (Canadian Centre for Fur Animal Research, NS, Canada). The accuracy of classification for CIEP was evaluated based on the sex information, and IAT, ELISA and PCV test results implemented in seven machine learning classification algorithms (Random Forest, Artificial Neural Networks, C50Tree, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) using the Caret package in R. The accuracy of prediction varied among the methods. Overall, the Random Forest was the best-performing algorithm for the current dataset with an accuracy of 0.89 in the training data and 0.94 in the testing data. Our work demonstrated the utility and relative ease of using machine learning algorithms to assess the CIEP information, and consequently reducing the cost of AD tests. However, further works require the inclusion of production and reproduction information in the models and extension of phenotypic collection to increase the accuracy of current methods.

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