ANALYSIS OF RADON TIME SERIES RECORDED IN SLOVAK AND CZECH CAVES FOR THE DETECTION OF ANOMALIES DUE TO SEISMIC PHENOMENA

2019 ◽  
Vol 186 (2-3) ◽  
pp. 428-432 ◽  
Author(s):  
Fabrizio Ambrosino ◽  
Lenka Thinová ◽  
Miloš Briestenský ◽  
Carlo Sabbarese
Keyword(s):  
Time Series ◽  
Continuous Monitoring ◽  
Meteorological Parameters ◽  
Activity Concentration ◽  
Support Vector ◽  
Mathematical Methods ◽  
Tectonic Structures ◽  
Radon Activity ◽  
Mode Decomposition ◽  
Active Tectonic

Abstract Anomalies in the radon (222Rn) releases in underground environments are one of the phenomena that can be observed before earthquake occurrence. Continuous measurements of radon activity concentration, and of meteorological parameters that influence the gas emission, were performed in three Slovak and Czech caves during 1-y period (1 July 2016–30 June 2017). The radon activity concentration in caves shows seasonal variations, with maxima reached during summer months. The anomalies in the radon time series are identified using a combination of three mathematical methods: multiple linear regression, empirical mode decomposition and support vector regression. The radon anomaly periods were compared with earthquake occurrences in Europe. Coincidences between both phenomena were found, since all monitored caves reflect contemporaneous local tectonic changes. The results indicate that radon continuous monitoring could assist a better understanding of radon emissions, along active tectonic structures, during seismic events.

scholarly journals A New GPT2w Model Improved by PSO-LSSVM for GNSS High-Precision Positioning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xuanxuan Zhang ◽  
Yamin Dang ◽  
Changhui Xu
Keyword(s):  
Time Series ◽  
High Precision ◽  
Meteorological Parameters ◽  
Meteorological Parameter ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Zenith Tropospheric Delay ◽  
Station Coordinates ◽  
Short Period ◽  
Gpt2w Model

Tropospheric delay is an important error affecting GNSS high-precision navigation and positioning, which will decrease the precision of navigation and positioning if it is not well corrected. Actually, tropospheric delay, especially in the zenith direction, is related to a series of meteorological parameters, such as temperature and pressure. To estimate the zenith tropospheric delay (ZTD) as accurately as possible, the paper proposes a new fused model using the least squares support vector machines (LSSVM) and the particle swarm optimization (PSO) to improve the precision and temporal resolution of meteorological parameters in global pressure and temperature 2 wet (GPT2w). The proposed model uses the time series of meteorological parameters from the GPT2w model as the initial value, and thus, the time series of the residuals can be obtained between the meteorological parameters from meteorological sensors (MS) and the GPT2w model. The long time series of meteorological parameters is the evident periodic signal. The GPT2w model describes its dominant frequency (harmonics), and the residuals thus can be seen as the short-period signal (nonharmonics). The combined PSO and LSSVM model (PSO-LSSVM) is used to predict the specific value of the short-period signal. The new GPT2w model, in which the meteorological parameter value is obtained by combining the estimated meteorological parameters residuals and the GPT2w-derived meteorological parameters, can be acquired. The GNSS network stations in Hong Kong throughout 2017-2018 are processed by the GNSS Processing and Analysis Software (GPAS), which is developed by the Chinese Academy of Surveying & Mapping, to estimate the zenith tropospheric delay and station coordinates using the new GPT2w model. Statistical results reveal that the accuracy of the new GPT2w model-derived ZTD was improved by 60% or more compared with that of the GPT2w-derived ZTD. In addition, the positioning accuracy of the GNSS station has been effectively improved up to 44.89%. Such results reveal that the new GPT2w model can greatly reduce the influence of nonharmonic components (short-period terms) of the meteorological parameter time series and achieve better accuracy than the GPT2w model.

Surface Deformation in Northeast Italy by using time series InSAR techniques with Sentinel-1 data

2020 ◽  
Author(s):  
Giulia Areggi ◽  
Cristiano Tolomei ◽  
Lorenzo Bonini ◽  
Giuseppe Pezzo
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Geodetic Data ◽  
Plate Motion ◽  
Tectonic Structures ◽  
Synthetic Aperture Radar Data ◽  
Area Of Interest ◽  
Spatial Coverage ◽  
Active Tectonic

<p>Geodetic data provide useful information on surface deformation over long period of time. Applying time series methods to geodetic data, several phenomena were studied. In particular, the potentials of geodetic data were exploited to detect and measure slow tectonic signals such as interseismic strain accumulation. During the interseismic period, when the faults are locked, an accumulation of deformation can occur in response to active tectonic stresses. Considering that such energy can be released through earthquakes, the estimation of surface deformation and the long-term strain rate reveals itself a useful approach for seismic hazard investigations. In this study, we used remote sensing Synthetic Aperture Radar data to evaluate the ground deformation in the Southeastern Alps (Northeastern Italy), an area characterized by an active convergent regime (Adria plate motion is ~ 2mm/yr) as well as several active tectonic structures. We used SAR images provided by Sentinel-1A/B satellites spanning the 2015-2019 temporal interval by applying the multi temporal Small Baseline Subset Interferometry (SBAS) technique. The method is based on a combination of a large number of interferograms characterized by small temporal and geometric baseline in order to reduce decorrelation effects and increase the spatial coverage over the area of interest. The outcomes consist of displacement time series and a mean ground velocity map for each coherent pixels with respect to the satellite Line-of-Sight (LoS). Some detected patterns can be attributed to subsidence phenomena, affecting the plain in the area under analysis, and due to the compaction of the sediments.</p>

scholarly journals Prediction of Wind Speed Using Hybrid Techniques

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6284
Author(s):  
Luis Lopez ◽  
Ingrid Oliveros ◽  
Luis Torres ◽  
Lacides Ripoll ◽  
Jose Soto ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Least Squares ◽  
Support Vector ◽  
Single Time ◽  
Hybrid Techniques ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Auto Regressive ◽  
Using Data

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).

DEVELOPMENT AND TESTING OF DEVICES FOR CONTINUOUS MONITORING OF RADON ACTIVITY CONCENTRATION IN WATERS

2019 ◽  
Vol 186 (2-3) ◽  
pp. 419-423
Author(s):  
Terézia Eckertová ◽  
Karol Holý ◽  
Monika Müllerová ◽  
Martin Bulko
Keyword(s):  
Laboratory Tests ◽  
Continuous Monitoring ◽  
Activity Concentration ◽  
Continuous Measurement ◽  
Water Sources ◽  
High Yield ◽  
Measuring Systems ◽  
Radon Measurement ◽  
Radon Activity ◽  
Earthquake Predictions

Abstract Continuous radon measurement in waters is an appropriate tool for the study of its variations as well as for the clarification and understanding of the factors that cause these changes. In addition, sudden changes in radon activity concentration (RAC) in groundwater can be used to identify geodynamic activities and earthquake predictions. In this paper, two measuring systems for continuous monitoring of RAC in waters are presented and tested. One of them was designed for water sources with a high yield; the second one operates with a constant volume of a sample using a different method of 222Rn release from water. We present our first laboratory tests of continuous measurement of RAC in tap waters as well as the variations of RAC observed during a week.

scholarly journals Prediction of Wind Speed Using Hybrid Techniques. Three locations: Colombia, Ecuador and Spain

2020 ◽  
Author(s):  
Luis Lopez ◽  
Ingrid Oliveros ◽  
Luis Torres ◽  
Lacides Ripoll ◽  
Jose Soto ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Least Squares ◽  
Support Vector ◽  
Least Mean Square ◽  
Single Time ◽  
Hybrid Techniques ◽  
Mode Decomposition ◽  
Auto Regressive ◽  
Using Data

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: 1) as a single time series containing all measurements, and 2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 hours in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-Squares Support Vector Machines, Empirical Mode Decomposition, and the Wavelet Transform. Also, the traditional and simple Auto-Regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a MATLAB implementation showed that the Least-squares Support Vector Machine using data as a single time series outperformed the other combinations, obtaining the least mean square error.

scholarly journals A combined model for short-term wind speed forecasting based on empirical mode decomposition, feature selection, support vector regression and cross-validated lasso

2021 ◽  
Vol 7 ◽  
pp. e732
Author(s):  
Tao Wang
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Support Vector Regression ◽  
Prediction Performance ◽  
Support Vector ◽  
Short Term ◽  
Combined Model ◽  
Wind Speed Forecasting ◽  
Mode Decomposition

Background The planning and control of wind power production rely heavily on short-term wind speed forecasting. Due to the non-linearity and non-stationarity of wind, it is difficult to carry out accurate modeling and prediction through traditional wind speed forecasting models. Methods In the paper, we combine empirical mode decomposition (EMD), feature selection (FS), support vector regression (SVR) and cross-validated lasso (LassoCV) to develop a new wind speed forecasting model, aiming to improve the prediction performance of wind speed. EMD is used to extract the intrinsic mode functions (IMFs) from the original wind speed time series to eliminate the non-stationarity in the time series. FS and SVR are combined to predict the high-frequency IMF obtained by EMD. LassoCV is used to complete the prediction of low-frequency IMF and trend. Results Data collected from two wind stations in Michigan, USA are adopted to test the proposed combined model. Experimental results show that in multi-step wind speed forecasting, compared with the classic individual and traditional EMD-based combined models, the proposed model has better prediction performance. Conclusions Through the proposed combined model, the wind speed forecast can be effectively improved.

An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting

2016 ◽  
Vol 25 (02) ◽  
pp. 1650005 ◽  
Author(s):  
Heng-Li Yang ◽  
Han-Chou Lin
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Linear Part ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Nonlinear Part ◽  
Financial Time ◽  
Stock Indices ◽  
Mode Decomposition ◽  
Original Time

Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.

scholarly journals Forecasting ASEAN Tourist Arrivals in Malaysia using Different Time Series Models

2019 ◽  
Vol 8 (6S3) ◽  
pp. 572-578
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Hybrid Model ◽  
Moving Average ◽  
Percentage Error ◽  
Support Vector ◽  
Time Series Models ◽  
Mode Decomposition ◽  
Asean Country ◽  
Forecasting Performance

In this study three time series models are used for forecasting monthly ASEAN tourist arrivals in Malaysia from January 1999 to December 2015. Brunei, Thailand and Vietnam of ASEAN country selected as case study. This paper compares the forecasting accuracy of seasonal autoregressive integrated moving average (SARIMA), Support Vector Machine (SVM) and Wavelet Support Vector Machine (WSVM) and Empirical Mode Decomposition with Wavelet Support Vector Machine (EMD_WSVM) using root mean square error (RMSE) and mean absolute percentage error (MAPE) criterion. Moreover, correlation test has also been carried out to strengthen decisions, and to check accuracy of various forecasting models. Based on the forecasting performance of all four models, hybrid model SARIMA and EMD_WSVM are found to be best models as compare to single model SVM and hybrid model WSVM.

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