scholarly journals Machine Learning Methods for Seismic Hazards Forecast

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 308 ◽  
Author(s):  
Valeri G. Gitis ◽  
Alexander B. Derendyaev
Keyword(s):  
Machine Learning ◽  
Training Sample ◽  
Time Interval ◽  
Minimum Area ◽  
Learning Methods ◽  
Web Based ◽  
Strong Earthquakes ◽  
Machine Learning Methods ◽  
Regression Approach ◽  
Spatio Temporal

In this paper, we suggest two machine learning methods for seismic hazard forecast. The first method is used for spatial forecasting of maximum possible earthquake magnitudes ( M m a x ), whereas the second is used for spatio-temporal forecasting of strong earthquakes. The first method, the method of approximation of interval expert estimates, is based on a regression approach in which values of M m a x at the points of the training sample are estimated by experts. The method allows one to formalize the knowledge of experts, to find the dependence of M m a x on the properties of the geological environment, and to construct a map of the spatial forecast. The second method, the method of minimum area of alarm, uses retrospective data to identify the alarm area in which the epicenters of strong (target) earthquakes are expected at a certain time interval. This method is the basis of an automatic web-based platform that systematically forecasts target earthquakes. The results of testing the approach to earthquake prediction in the Mediterranean and Californian regions are presented. For the tests, well known parameters of earthquake catalogs were used. The method showed a satisfactory forecast quality.

scholarly journals Natural language processing systems for data extraction and mapping on the basis of unstructured text blocks

2020 ◽  
Vol 26 (3) ◽  
pp. 53-61
Author(s):  
Pavel Kikin ◽  
Alexey Kolesnikov ◽  
Alexey Portnov ◽  
Denis Grischenko
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Mathematical Models ◽  
Optimal Algorithm ◽  
The State ◽  
Gradient Boosting ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
Spatio Temporal

The state of ecological systems, along with their general characteristics, is almost always described by indicators that vary in space and time, which leads to a significant complication of constructing mathematical models for predicting the state of such systems. One of the ways to simplify and automate the construction of mathematical models for predicting the state of such systems is the use of machine learning methods. The article provides a comparison of traditional and based on neural networks, algorithms and machine learning methods for predicting spatio-temporal series representing ecosystem data. Analysis and comparison were carried out among the following algorithms and methods: logistic regression, random forest, gradient boosting on decision trees, SARIMAX, neural networks of long-term short-term memory (LSTM) and controlled recurrent blocks (GRU). To conduct the study, data sets were selected that have both spatial and temporal components: the values of the number of mosquitoes, the number of dengue infections, the physical condition of tropical grove trees, and the water level in the river. The article discusses the necessary steps for preliminary data processing, depending on the algorithm used. Also, Kolmogorov complexity was calculated as one of the parameters that can help formalize the choice of the most optimal algorithm when constructing mathematical models of spatio-temporal data for the sets used. Based on the results of the analysis, recommendations are given on the application of certain methods and specific technical solutions, depending on the characteristics of the data set that describes a particular ecosystem

scholarly journals APPLICATION OF ENSEMBLE MACHINE LEARNING METHODS FOR PREDICTING THE TECHNICAL STATE OF AN OBJECT

2021 ◽  
Vol 23 (1) ◽  
pp. 111-114
Author(s):  
Yu.E. Kuvayskova ◽  
Keyword(s):  
Machine Learning ◽  
The State ◽  
Technical State ◽  
Supervised Machine Learning ◽  
Time Interval ◽  
Technical Documentation ◽  
Technical Object ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

To ensure the reliable functioning of a technical object, it is necessary to predict its state for the upcoming time interval. Let the technical state of the object be characterized at a certain point in time by a set of parameters established by the technical documentation for the object. It is assumed that for certain values of these parameters, the object may be in a good or faulty state. It is required by the values of these parameters to estimate the state of the object in the upcoming time interval. Supervised machine learning methods can be applied to solve this problem. However, to obtain good results in predicting the state of an object, it is necessary to choose the correct training model. One of the disadvantages of machine learning models is high bias and too much scatter. In this paper, to reduce the scatter of the model, it is proposed to use ensemble machine learning methods, namely, the bagging procedure. The main idea of the ensemble of methods is that with the right combination of weak models, more accurate and robust models can be obtained. The purpose of bagging is to create an ensemble model that is more reliable than the individual models that make up it. One of the big advantages of bagging is its concurrency, since different ensemble models are trained independently of each other. The effectiveness of the proposed approach is shown by the example of predicting the technical state of an object by eight parameters of its functioning. To assess the effectiveness of the application of ensemble machine learning methods for predicting the technical state of an object, the quality criteria of binary classification are used: accuracy, completeness, and F-measure. It is shown that the use of ensemble machine learning methods can improve the accuracy of predicting the state of a technical object by 4% –9% in comparison with basic machine learning methods. This approach can be used by specialists to predict the technical condition of objects in many technical applications, in particular, in aviation.

scholarly journals Machine Learning Methods for Content - Classification and Categorization

2021 ◽  
pp. 184-189
Author(s):  
Prabhat Kubal ◽  
Prof. Surabhi Thorat ◽  
Prof. Swati Maurya
Keyword(s):  
Machine Learning ◽  
Everyday Life ◽  
Adverse Consequence ◽  
Learning Methods ◽  
Web Based ◽  
Machine Learning Methods ◽  
Content Classification

These days online gatherings and web-based media stages have furnished people with the necessary resources to advance their contemplations and put themselves out there free paying little heed to the kind of language used to communicate those thoughts, in certain examples these internet based remarks contain express language which might hurt the peruser. We likewise evaluate the class irregularity issues related with the dataset by utilizing inspecting procedures and misfortune. Models we applied yield high in general exactness with moderately minimal expense. To diminish the adverse consequence of poisonous remark in everyday life we have endeavored to plan a Toxic Language detector.

Statistical-based dynamic machine learning models for nonlinear spatio-temporal processes

2018 ◽  
Author(s):  
◽  
Patrick McDermott
Keyword(s):  
Machine Learning ◽  
Uncertainty Quantification ◽  
Process Model ◽  
Point Estimation ◽  
Dynamical Process ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Statistical Framework ◽  
Temporal Models ◽  
Spatio Temporal

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] One of the most vital aspects of any spatio-temporal model is characterizing the dynamics of the process. In both a spatio-temporal forecasting and data assimilation setting, the dynamical process model often determines the success of a given methodology. Although in some cases mechanistic processes can motivate a dynamical process model, frequently the dynamics involve complex nonlinear behavior that is difficult to specify a priori. This nonlinearity is generally induced by interactions between the process at various spatial locations or between various scales of variability. Over the past few decades, machine learning methods have been shown to be successful for predicting or classifying high-dimensional complex nonlinear multivariate processes. Even though spatio-temporal processes share many of these same characteristics, only recently have machine learning methods been applied to spatio-temporal processes. Furthermore, uncertainty quantification is almost always ignored in the machine learning literature, with a focus only on point estimation prediction or classification. The computational burden of implementing these models within an uncertainty quantification framework often discourages the use of a more traditional statistical framework. Yet, uncertainty quantification is critical for nonlinear spatio-temporal processes where unique behaviors such as non-Gaussianity and extremes are often found. This dissertation focuses on retaining components of machine learning methods for dynamical processes that have been important for their success, while placing them within a formal statistical framework that is computationally feasible. Specifically, a Bayesian framework is employed so the uncertainties associated with the investigated processes can rigorously be quantified. The developed Bayesian machine learning based statistical dynamical spatio-temporal models are shown to outperform a suite of competing methods in terms of both forecast accuracy and uncertainty quantification for multiple nonlinear spatio-temporal processes.

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