From time series analysis to hydrodynamic modelling in a complex hydrosystem: applications for the hydrodynamic characterization and modelling of a karst aquifer with sparse data (Oeillal spring, France).

2020 ◽  
Author(s):  
Vianney Sivelle ◽  
Hervé Jourde
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Hydrological Cycle ◽  
Karst Aquifer ◽  
Normal Fault ◽  
Sparse Data ◽  
Distributed Model ◽  
Hydrodynamic Modelling ◽  
Series Analysis ◽  
Cross Correlation Analysis

<p>The Narbonne-Sigean sedimentary basin is composed by Oligocene sediment, alluvions from the Aude River and the Fontfroide-Montredon limestone massif. The significant tectonics during the Oligocene rifting brought all of these formations to the surface in the Malvesi area. A normal fault affects the area and causes the Jurassic unit to rise in the form of a horst: the Montlaures massif. The Oeillal spring is located on the south border of this massif. The karst spring outflows at 4 pools with different physico-chemical signature. Each of these pools is equipped with a CTD probe. Moreover, the area is monitored with piezometric and temperature measurements that allow characterizing each of the main geological formations near the Oeillal spring. Though measurements started more than ten years ago (2007), continuous monitoring is available on one hydrological cycle, only. Indeed, only sparse data are available over the period 2007-2018, which required proposing a methodology to allow the optimal use of the available data in the modelling workflow.  The present study thus focuses on this methodology and on the use of numerical tools such as time series analysis (auto and cross-correlation analysis, spectral analysis) to determine a suitable modelling approach (lumped or distributed model) adapted to the hydrodynamic modelling of karst springs with sparse data.</p>

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

Provincial Level Time Series Analysis : Community Mobility and COVID-19 Daily Cases in Turin, Italy (Preprint)

2021 ◽  
Author(s):  
Dayana Benny
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Correlation ◽  
Provincial Level ◽  
Epidemic Curve ◽  
The Past ◽  
Series Analysis ◽  
Community Mobility ◽  
Cross Correlation Analysis ◽  
Restrictive Measures

BACKGROUND Turin, a province in the Piedmont region sees second highest new COVID-19 infections in Northern part of Italy as of March 31, 2021. During the first wave of pandemic, many restrictive measures were introduced in this province. There are many studies that conducted time series analysis of various regions in Italy, but studies that are analysing the data in province level are limited. Also, no applications of Cross Correlation Function(CCF) have been proposed to analyse relationships between COVID-19 new cases and community mobility at the provincial level in Italy. OBJECTIVE The goal of this time series analysis is to find how the restrictive measures in Turin province, Italy impacted community mobility and helped in flattening the epidemic curve during the first wave of the pandemic. METHODS A simple time series analysis is conducted in this study to analyse whether there is an association between COVID-19 daily cases and community mobility. In this study, we analysed whether the time series of the parameter that estimates the reproduction of infection in the outbreak is related to the past lags of community mobility time series by performing cross-correlation analysis. RESULTS Multiple regression is carried out in which the R0 variable is a linear function of past lags 6, 7, 8, and 1 of the community mobility variable and all coefficients are statistically significant where P = 0.024043, 2.69e-05, 0.045350 and 0.000117 respectively. The cross-correlation between data fitted from the significant past lags of community mobility and transformed basic reproduction number (R0) time-series is obtained in such a manner that the R0 of a day is related to the past lags of community mobility in Turin province. CONCLUSIONS Our analysis shows that the restrictive measures are having an impact on community mobility during the first wave of COVID-19 and it can be related to the reported secondary cases of COVID-19 in Turin province at that time. Through further improvement, this simple model could serve as preliminary research for developing right preventive methods during the early stages of an epidemic.

Karst aquifer response by time series analysis applications in Jonggrangan Karst, Java Island, Indonesia

2019 ◽  
Vol 78 (13) ◽  
Author(s):  
Ikhwan Arbi Kurniawan ◽  
Tjahyo Nugroho Adji ◽  
Afid Nurkholis ◽  
Eko Haryono ◽  
Hamzah Fatoni ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Karst Aquifer ◽  
Series Analysis

scholarly journals Performance Consistency of International Soccer Teams in Euro 2012: a Time Series Analysis

2013 ◽  
Vol 38 ◽  
pp. 213-226 ◽  
Author(s):  
Mohsen Shafizadeh ◽  
Marc Taylor ◽  
Carlos Lago Peñas
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Performance Indicators ◽  
Final Stage ◽  
Cross Correlation ◽  
Positive Correlation ◽  
Series Analysis ◽  
Cross Correlation Analysis ◽  
Soccer Teams ◽  
Consistent Performance

Abstract The purpose of this study was to examine the consistency of performance in successive matches for international soccer teams from Europe which qualified for the quarter final stage of EURO 2012 in Poland and Ukraine. The eight teams that reached the quarter final stage and beyond were the sample teams for this time series analysis. The autocorrelation and cross-correlation functions were used to analyze the consistency of play and its association with the result of match in sixteen performance indicators of each team. The results of autocorrelation function showed that based on the number of consistent performance indicators, Spain and Italy demonstrated more consistency in successive matches in relation to other teams. This appears intuitive given that Spain played Italy in the final. However, it is arguable that other teams played at a higher performance levels at various parts of the competition, as opposed to performing consistently throughout the tournament. The results of the cross-correlation analysis showed that in relation to goal-related indicators, these had higher associations with the match results of Spain and France. In relation to the offensive-related indicators, France, England, Portugal, Greece, Czech Republic and Spain showed a positive correlation with the match result. In relation to the defensive-related indicators, France, England, Greece and Portugal showed a positive correlation with match results. In conclusion, in an international soccer tournament, the successful teams displayed a greater degree of performance consistency across all indicators in comparison to their competitors who occasionally would show higher levels of performance in individual games, yet not consistently across the overall tournament. The authors therefore conclude that performance consistency is more significant in international tournament soccer, versus occasionally excelling in some metrics and indicators in particular games.

Hydrodynamic Characterization of a Karst Aquifer in the Brazilian Semi-Arid Region with Time Series Analysis of Hydrology Data

2011 ◽  
Author(s):  
Marques Guilherme Fernandes ◽  
Stela Cota ◽  
Paulo Vicente Braga, Jr. ◽  
Leila Menegasse Velásquez ◽  
Paulo Rodrigues ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arid Region ◽  
Karst Aquifer ◽  
Series Analysis ◽  
Semi Arid Region ◽  
Semi Arid

scholarly journals Time series analysis applications for karst aquifer characterisation in Pindul Cave karst system, Indonesia

2019 ◽  
Vol 48 (1) ◽  
Author(s):  
Afid Nurkholis ◽  
Tjahyo Nugroho Adji ◽  
Eko Haryono ◽  
Ahmad Cahyadi ◽  
Slamet Suprayogi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Karst Aquifer ◽  
Karst System ◽  
Series Analysis
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