Are the recurring earthquake swarms in West-Bohemia rain triggered?

2021 ◽  
Author(s):  
Josef Vlcek ◽  
Roman Beránek ◽  
Tomáš Fischer
Keyword(s):  
Time Series ◽  
Water Level ◽  
Seismic Activity ◽  
Earthquake Swarm ◽  
Earthquake Swarms ◽  
Level Data ◽  
Water Level Data ◽  
Annual Periodicity ◽  
West Bohemia ◽  
Swarm Activity

<p>In past decades, a significant effort was spent to find the origin of recurring earthquake swarms in West-Bohemia/Vogtland. Widespread understanding accepts that crustal fluids migration along the fault zones is responsible for earthquake triggering in this area. Recently, a new model was suggested, which tests the hypothesis whether the diffusion of hydraulically induced pore pressure could be a valid trigger mechanism. In this approach the precipitation signal was transformed by diffusion equation to the hypocenter depth and statistically compared with the earthquake occurrence in time and concluded that at least 19% of the seismicity could have been triggered by rain. </p><p>In our study we apply a different approach to verify the validity of these results. We use two types of rain signal on the input which is compared with the time series of earthquake weekly rate for the past 25 years. To remove the strong episodic character of the swarm seismicity we use a declustered seismic catalog, which is characteristic by almost continuous seismic activity.</p><p>The rain signal is represented first by the precipitation data and second by the water level data in the Horka reservoir, which is located above the main focal zone of Nový Kostel. We test the possible relation to the earthquake swarm activity by cross correlating both the rain signal types and the seismicity rate. To amplify the possible seasonal periodicity of the data we stacked the explored time series data (precipitation, water level and seismic activity) according to their occurrence date in a single year. The results show that in any of the input data and seismicity do not correlate. </p><p>In the next step, we tested the possible (annual) periodicity of the data in question by the singular spectral analysis (SSA), which is a sensitive method to identify possible periodic signals in the presence of noise. While the water level data showed a striking peak for the period of 1 year, any indication of annual periodicity was never found in the seismicity data. Accordingly, we conclude that our analysis has shown no influence of the precipitation or the water level fluctuations in the Horka dam to the earthquake swarm activity in West Bohemia/Vogtland.</p>

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2022 ◽  
pp. 1077-1097
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

2021 ◽  
Vol 12 (3) ◽  
pp. 2427-2435
Author(s):  
Adib Mashuri Et.al
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Water Level ◽  
Time Scale ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Daily Time

This study focused on chaotic analysis of water level data in different elevations located in the highland and lowland areas. This research was conducted considering the uncertain water level caused by the river flow from highland to lowland areas. The analysis was conducted using the data collected from the four area stations along Pahang River on different time scales which were hourly and daily time series data. The resulted findings were relevant to be used by the local authorities in water resource management in these areas. Two methods were used for the analysis process which included Cao method and phase space plot. Both methods are based on phase space reconstruction that is referring to reconstruction of one dimensional data (water level data) to d-dimensional phase space in order to determine the dynamics of the system. The combination of parameters  and d is required in phase space reconstruction. Results showed that (i) the combination of phase space reconstruction’s parameters gave a higher value of parameters by using hourly time scale compared to daily time scale for different elevation; (ii) different elevation gave impact on the values of phase space reconstructions’ parameters; (iii) chaotic dynamics existed using Cao method and phase space plot for different elevation and time scale. Hence, water level data with different time scale from different elevation in Pahang River can be used in the development of prediction model based on chaos approach.

scholarly journals Stochastic modeling of Lake Van water level time series with jumps and multiple trends

2013 ◽  
Vol 10 (2) ◽  
pp. 2353-2371 ◽  
Author(s):  
H. Aksoy ◽  
N. E. Unal ◽  
E. Eris ◽  
M. I. Yuce
Keyword(s):  
Time Series ◽  
Water Level ◽  
Eastern Anatolia ◽  
Lake Van ◽  
Data Set ◽  
Lake Water Level ◽  
Trend Model ◽  
Level Data ◽  
Water Level Data ◽  
Surrounding Areas

Abstract. In 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey has risen up about 2 m. Analysis of the hydrometeorological shows that change in the water level is related to the water budget of the lake. In this study, a stochastic model is generated using the measured monthly water level data of the lake. The model is derived after removal of trend and periodicity in the data set. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. For the multiple-trend, the time series is first divided into homogeneous segments by means of SEGMENTER, segmentation software. Four segments are found meaningful practically each fitted with a trend line. Two models considering mono- and multiple-trend time series are developed. The multiple-trend model is found better for planning future development in surrounding areas of the lake.

scholarly journals Some possible correlations between electro-magnetic emission and seismic activity during West Bohemia 2008 earthquake swarm

2010 ◽  
Vol 2 (1) ◽  
pp. 145-160
Author(s):  
P. Kolář
Keyword(s):  
Seismic Activity ◽  
Earthquake Swarm ◽  
Seismic Events ◽  
Seismic Region ◽  
West Bohemia ◽  
Long Time ◽  
Electro Magnetic ◽  
Short Time ◽  
Swarm Activity

Abstract. There are long time lasting speculations about electro-magnetic emission phenomena (hereafter EME) connected with seismic activity. In the present work we study such relations in West Bohemia region during 2008 earthquake swarm. After brief characterization of the seismic region, we describe recording method and data analysis. We did not observe any direct link between EME and seismic events, however statistical analysis indicates that it could be some increase of EME activity in time 60 to 30 min before an event on periods 17–14 min, some gap in EME activity approximately 2 h after the event and a maximum 4 h after the events (note, that this result qualitatively correspond with observations from other seismic regions). Also global decrease of EME activity with the decay of the swarm activity was observed. However due to incomplete EME data and short time of observation these results must be understand as indication of possible correlation rather than reliable relation.

Accuracy Validation of Flood Forecasting Method Based on Time Series Analysis Using Observed Water Level and Rainfall Data

2020 ◽  
Author(s):  
Naoki Koyama ◽  
Tadashi Yamada
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Water Level ◽  
First Grade ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Flood Prediction ◽  
Series Analysis ◽  
Level Data ◽  
Water Level Data

<p>The aim of this paper is to verify the accuracy of the real-time flood prediction model, using the time-series analysis. Forecast information of water level is important information that encourages residents to evacuate. Generally, flood forecasting is conducted by using runoff analysis. However, in developing countries, there are not enough hydrological data in a basin. Therefore, this study assumes where poor hydrologic data basin and evaluates it through reproducibility and prediction by using time series analysis which statistical model with the water level data and rainfall data. The model is applied to the one catchment of the upper Tone River basin, one of the first grade river in Japan. This method is possible to reproduce hydrograph, if the observation stations exist several points in the basin. And using the estimated parameters from past flood events, we can apply this method to predict the water level until the flood concentration time which the reference point and observation station. And until this time, the peak water level can be predicted with the accuracy of several 10cm. Prediction can be performed using only water level data, but by adding rainfall data, prediction can be performed for a longer time.</p>

scholarly journals USING TIME SERIES OF SENTINEL-1 IMAGES TO PRODUCE DRY BATHYMETRY OF RIVERS

2020 ◽  
Vol V-3-2020 ◽  
pp. 387-394 ◽  
Author(s):  
P. Maillard
Keyword(s):  
Time Series ◽  
Water Level ◽  
Cross Section ◽  
Cross Sections ◽  
Discharge Data ◽  
River Bank ◽  
Manual Intervention ◽  
Optical Images ◽  
Level Data ◽  
Water Level Data

Abstract. A method is presented to produce river cross section profiles from a time series of Sentinel-1 images paired with water level data. Four programs are presented that generate river width data and cross section profiles from SAR or optical images. The programs generate a river bank and island width database with minimum manual intervention. Water level data from in situ stations are interpolated to match the width data and create elevation points from which the cross section profiles are produced. The method is fully described and tested on the São Francisco River in Brazil. The width data are plotted against discharge data to compare their progression. Over 1700 cross sections were produced and classified by their shape. Potential and limitations are presented.

scholarly journals Timeline Visualization Uncovers Gaps in Archived Tsunami Water Level Data

2021 ◽  
Vol 3 ◽  
Author(s):  
Aaron D. Sweeney
Keyword(s):  
Time Series ◽  
Water Level ◽  
Time Series Data ◽  
Deep Ocean ◽  
Rapid Identification ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Data Gaps ◽  
Coastal Water Level

We demonstrate that data abstraction via a timeline visualization is highly effective at allowing one to discover patterns in the underlying data. We describe the rapid identification of data gaps in the archival time-series records of deep-ocean pressure and coastal water level observations collected to support the NOAA Tsunami Program and successful measures taken to rescue these data. These data gaps had persisted for years prior to the development of timeline visualizations to represent when data were collected. This approach can be easily extended to all types of time-series data and the author recommends this type of temporal visualization become a routine part of data management, whether one collects data or archives data.

scholarly journals Stochastic modeling of Lake Van water level time series with jumps and multiple trends

2013 ◽  
Vol 17 (6) ◽  
pp. 2297-2303 ◽  
Author(s):  
H. Aksoy ◽  
N. E. Unal ◽  
E. Eris ◽  
M. I. Yuce
Keyword(s):  
Time Series ◽  
Water Level ◽  
Lake Water ◽  
Linear Trend ◽  
Lake Van ◽  
Lake Water Level ◽  
Trend Model ◽  
Level Data ◽  
Water Level Data ◽  
Surrounding Areas

Abstract. In the 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey, has risen up about 2 m. Analysis of the hydrometeorological data shows that change in the water level is related to the water budget of the lake. In this study, stochastic models are proposed for simulating monthly water level data. Two models considering mono- and multiple-trend time series are developed. The models are derived after removal of trend and periodicity in the dataset. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. In the so-called mono-trend model, the time series is treated as a whole under the hypothesis that the lake water level has an increasing trend. In the second model (so-called multiple-trend), the time series is divided into a number of segments to each a linear trend can be fitted separately. Application on the lake water level data shows that four segments, each fitted with a trend line, are meaningful. Both the mono- and multiple-trend models are used for simulation of synthetic lake water level time series under the hypothesis that the observed mono- and multiple-trend structure of the lake water level persist during the simulation period. The multiple-trend model is found better for planning the future infrastructural projects in surrounding areas of the lake as it generates higher maxima for the simulated lake water level.

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2020 ◽  
Vol 10 (3) ◽  
pp. 1-19
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

Sign in / Sign up

Export Citation Format

Share Document