scholarly journals Comparison between Periodic Tracer Tests and Time-Series Analysis to Assess Mid- and Long-Term Recharge Model Changes Due to Multiple Strong Seismic Events in Carbonate Aquifers

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3073
Author(s):  
Davide Fronzi ◽  
Diego Di Curzio ◽  
Sergio Rusi ◽  
Daniela Valigi ◽  
Alberto Tazioli
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Tracer Tests ◽  
Seismic Events ◽  
Spring Discharge ◽  
Massif Central ◽  
Carbonate Aquifers ◽  
Series Analysis ◽  
Hydrodynamic Behaviour

Understanding the groundwater flow in carbonate aquifers represents a challenging aspect in hydrogeology, especially when they have been struck by strong seismic events. It has been proved that large earthquakes change springs hydrodynamic behaviour showing transitory or long-lasting variations and making their management much more difficult. This is the case of Sibillini Massif (central Italy), which has been hit by the well-known 2016–2017 seismic period. This work aims to improve the knowledge of carbonate aquifers groundwater circulation and their possible changes in the hydrodynamic behaviour, during and after a series of strong seismic events. The goal has been achieved by comparing long-time tracer tests and transient time-series analysis, based on a sliding-window approach. This approach allowed investigating transient variations in the carbonate aquifers recharge system, highlighting the changes of relationships between the inflow contributions to the spring discharge in the area. As a result, the seismically triggered pore pressure distribution, and the hydraulic conductivity variations, because of the ground shaking and the fault systems activation, account for all the mid- and long-term modifications in the recharge system of Sibillini aquifers, respectively. These outcomes provide valuable insights to the knowledge of aquifer response under similar hydrogeological conditions, that are vital for water management.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Assessment of Long-Term Sensor Radiometric Degradation Using Time Series Analysis

2014 ◽  
Vol 52 (5) ◽  
pp. 2960-2976 ◽  
Author(s):  
Wonkook Kim ◽  
Tao He ◽  
Dongdong Wang ◽  
Changyong Cao ◽  
Shunlin Liang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis

Time series analysis of microbiome and metabolome at multiple body sites in steady long-term isolation confinement

Gut ◽  
2020 ◽  
pp. gutjnl-2020-320666
Author(s):  
Qiang Feng ◽  
Xiang Lan ◽  
Xiaoli Ji ◽  
Meihui Li ◽  
Shili Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis

Time series analysis of long-term data sets of atmospheric mercury concentrations

2004 ◽  
Vol 380 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Christian Temme ◽  
Ralf Ebinghaus ◽  
J�rgen W. Einax ◽  
Alexandra Steffen ◽  
William H. Schroeder
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Atmospheric Mercury ◽  
Data Sets ◽  
Series Analysis ◽  
Term Data

scholarly journals Time-series analysis of long-term photometry of BM Canum Venaticorum

2017 ◽  
Vol 338 (4) ◽  
pp. 453-463
Author(s):  
L. Siltala ◽  
L. Jetsu ◽  
T. Hackman ◽  
G. W. Henry ◽  
L. Immonen ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Series Analysis
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