scholarly journals Implication of Radon Monitoring for Earthquake Surveillance Using Statistical Techniques: A Case Study of Wenchuan Earthquake

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Aftab Alam ◽  
Nanping Wang ◽  
Guofeng Zhao ◽  
Adnan Barkat
Keyword(s):  
Time Series ◽  
Wenchuan Earthquake ◽  
Selection Criterion ◽  
Chaotic Regime ◽  
Earthquake Activity ◽  
Deterministic Analysis ◽  
Earthquake Prediction Research ◽  
Induced Changes ◽  
Soil Gases ◽  
Monitoring Stations

The seismotectonically induced changes in groundwater radon (Rn) are considered to be strong imputes for the surveillance of imminent major earthquakes. Groundwater facilitates the migration of soil gases as a result of tectonic stresses. In this regard, a radon time series is statistically analysed to identify the radon anomalies possibly induced by Wenchuan earthquake. The statistical analysis mainly involves the deterministic analysis of the Rn data and residual Rn analysis using a criterion x¯±2σ of anomaly selection having a confidence interval of 95%. The deterministic analysis reveals that the Rn time series follows a persistent trend 0.5≤H≤1 which confirms the absence of a chaotic regime. On the other hand, the residual Rn shows a notable upsurge straddling the time of the Wenchuan earthquake in the form of pre- and post earthquake changes at monitoring stations having RE/RD≤0.3. The residual Rn level passes the anomaly selection criterion x¯±2σ and is declared as a tectonically induced Rn anomaly. Contrary to this, the response of distant monitoring stations (RE/RD>0.3) to this particular earthquake further validates the link between Rn and earthquake activity. In a nutshell, the present study highlights the potential implications of earthquake-induced radon anomalies for earthquake prediction research.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

scholarly journals Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 925 ◽  
Author(s):  
Stephen Guth ◽  
Themistoklis P. Sapsis
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Extreme Event ◽  
Binary Classification ◽  
Selection Criterion ◽  
Area Under The Curve ◽  
Wave Model ◽  
Training Dataset ◽  
Ocean Engineering ◽  
Selection Of

The ability to characterize and predict extreme events is a vital topic in fields ranging from finance to ocean engineering. Typically, the most-extreme events are also the most-rare, and it is this property that makes data collection and direct simulation challenging. We consider the problem of deriving optimal predictors of extremes directly from data characterizing a complex system, by formulating the problem in the context of binary classification. Specifically, we assume that a training dataset consists of: (i) indicator time series specifying on whether or not an extreme event occurs; and (ii) observables time series, which are employed to formulate efficient predictors. We employ and assess standard binary classification criteria for the selection of optimal predictors, such as total and balanced error and area under the curve, in the context of extreme event prediction. For physical systems for which there is sufficient separation between the extreme and regular events, i.e., extremes are distinguishably larger compared with regular events, we prove the existence of optimal extreme event thresholds that lead to efficient predictors. Moreover, motivated by the special character of extreme events, i.e., the very low rate of occurrence, we formulate a new objective function for the selection of predictors. This objective is constructed from the same principles as receiver operating characteristic curves, and exhibits a geometric connection to the regime separation property. We demonstrate the application of the new selection criterion to the advance prediction of intermittent extreme events in two challenging complex systems: the Majda–McLaughlin–Tabak model, a 1D nonlinear, dispersive wave model, and the 2D Kolmogorov flow model, which exhibits extreme dissipation events.

scholarly journals On the Chaotic Nature of Solar Activity

1993 ◽  
Vol 132 ◽  
pp. 13-20
Author(s):  
J. Kurths ◽  
U. Feudel ◽  
W. Jansen
Keyword(s):  
Time Series ◽  
Steady State ◽  
Solar Activity ◽  
Time Series Analysis ◽  
Dynamical Behaviour ◽  
Chaotic Regime ◽  
Dynamo Model ◽  
Series Analysis ◽  
Chaotic Nature ◽  
Low Dimensional

AbstractApplying modern techniques of time series analysis, there are serious indications that the dynamics of the global solar activity is a low dimensional chaos. A simple non-linear dynamo model is qualitatively studied exhibiting a rich dynamical behaviour from steady state via some bifurcation to a chaotic regime.

scholarly journals Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO<sub>2</sub> and CH<sub>4</sub>

2017 ◽  
Author(s):  
Abdelhadi El Yazidi ◽  
Michel Ramonet ◽  
Philippe Ciais ◽  
Gregoire Broquet ◽  
Isabelle Pison ◽  
...  
Keyword(s):  
Time Series ◽  
Detection Methods ◽  
Local Source ◽  
Observation System ◽  
High Mountain ◽  
Spike Detection ◽  
Automatic Data ◽  
Background Site ◽  
Local Emissions ◽  
Monitoring Stations

Abstract. This study deals with the problem of identifying atmospheric data that are influenced by local emissions which cause spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS), and standard deviation of the background (SD), to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the ICOS (Integrated Carbon Observation System) European Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in Eastern France (OPE); a high mountain observatory in the south-west of France (PDM); a regional marine background site in Crete (FKL); and a marine clean-air background site in the southern hemisphere in Amsterdam island (AMS). This panel allows addressing the spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analyzed. All the methods were found to be able to detect short-term spikes (lasting from a few seconds to few minutes) in the time series. Analysis of the results of each method leads us to exclude the use of the COV method because of its requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or under-estimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the REBS methods after the best adjustment of parameter values. At PDM, measurements made by two analyzers 200 m from each other allow to confirm that the CH4 spikes identified in one of the time-series but not in the other correspond to a local source from a sewage treatment facility in one of the observatory buildings. From this experiment, we found that the REBS method underestimates the number of positive anomalies in the CH4 data caused by local sewage emissions. As a conclusion, we recommend the use of the SD method, which also appears as the easiest one to implement as automatic data processing, for the operational filtering of spikes in greenhouses gases time series at global and regional monitoring stations of networks like ICOS.

scholarly journals VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1096
Author(s):  
Edward Ming-Yang Wu ◽  
Shu-Lung Kuo
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Egarch Model ◽  
Southern Taiwan ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.

scholarly journals Implications of fisheries-induced changes in stock structure and reproductive potential for stock recovery of a sex-dimorphic species, North Sea plaice

2010 ◽  
Vol 67 (9) ◽  
pp. 1931-1938 ◽  
Author(s):  
Adriaan D. Rijnsdorp ◽  
Cindy J. G. van Damme ◽  
Peter R. Witthames
Keyword(s):  
Time Series ◽  
North Sea ◽  
Egg Production ◽  
Cohort Analysis ◽  
Reproductive Potential ◽  
Size Difference ◽  
Stock Structure ◽  
Induced Changes ◽  
Dimorphic Species ◽  
Stock Recovery

Abstract Rijnsdorp, A. D., van Damme, C. J. G., and Witthames, P. R. 2010. Implications of fisheries-induced changes in stock structure and reproductive potential for stock recovery of a sex-dimorphic species, North Sea plaice. – ICES Journal of Marine Science, 67: 1931–1938. A key assumption in stock assessment and stock forecasts often is that spawning-stock biomass (SSB) and egg production are proportional and that the reproductive potential is independent of stock structure (age composition and sex ratio). Based on a 60-year time-series of total egg production (TEP) of North Sea plaice, we demonstrate that this assumption could result in a biased perception of the temporal trend in reproductive potential. The time-series incorporates: (i) annual observations on maturity, growth, and condition, (ii) a predictive model for interannual variations in fecundity caused by variations in body condition and by the probability of being a recruit spawner, and (iii) a cohort analysis of sex-specific landings-at-age since 1948. Following an increase in fishing mortality rate, TEP declined by a factor of 7–8 from a peak in the 1970s to a minimum in 1999–2000. Concurrent with this decline, the contribution of recruit spawners and the size difference between spawning males and females decreased. The implications of phenotypic plasticity and fisheries-induced evolutionary changes in growth and maturation for the recovery potential of the plaice stock are discussed.

scholarly journals Time-series Variations in Nutrient Concentrations at Two Monitoring Stations in Tokyo Bay

2008 ◽  
Vol 31 (9) ◽  
pp. 559-564 ◽  
Author(s):  
Jota KANDA ◽  
Pachara CHOMTHAISON ◽  
Naho HORIMOTO ◽  
Yukuya YAMAGUCHI ◽  
Takashi ISHIMARU
Keyword(s):  
Time Series ◽  
Tokyo Bay ◽  
Nutrient Concentrations ◽  
Monitoring Stations

scholarly journals Processing and Extraction of Seasonal Tree Physiological Parameters from Stem Radius Time Series

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 765
Author(s):  
Simon Knüsel ◽  
Richard L. Peters ◽  
Matthias Haeni ◽  
Micah Wilhelm ◽  
Roman Zweifel
Keyword(s):  
Time Series ◽  
Environmental Conditions ◽  
R Package ◽  
Processing Parameters ◽  
Soil Drought ◽  
High Temporal Resolution ◽  
Stem Size ◽  
Physiological Variables ◽  
And Shifts ◽  
Induced Changes

Radial stem size changes, measured with automated dendrometers at intra-daily resolution, offer great potential to link environmental conditions with tree physiology at the seasonal scale. Such measurements need to be time-aligned, cleaned of outliers and shifts, gap-filled and analysed for reversible (water-related) and irreversible (growth-related) fractions to obtain physiologically meaningful data. Therefore, comprehensive tools are needed for reproducible data processing and analytics of dendrometer data. Here we present a transparent method, compiled in the R package treenetproc, to turn raw dendrometer data into clean, physiologically interpretable information, i.e., stem growth, tree water deficit, growth phenological phases, mean daily shrinkage and their respective timings. The removal of errors is facilitated by additional functions and supported with graphical visualizations. To ensure reproducible data handling, the processing parameters and induced changes to the raw data are documented in the output and, thus, are a step towards a standardized processing of automatically measured stem radius time series. We discuss examples, such as the seasonality of growth or the dependence of growth on atmospheric and soil drought. The presented growth and water-related physiological variables at high temporal resolution offer novel physiological insights into the seasonally varying responses of trees to changing environmental conditions.

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