scholarly journals Stage level, volume, and time-frequency information content of Lake Tana using stochastic and wavelet analysis methods

2010 ◽  
Vol 7 (4) ◽  
pp. 5525-5546
Author(s):  
Y. Chebud ◽  
A. Melesse
Keyword(s):  
Monte Carlo ◽  
Wavelet Analysis ◽  
Information Content ◽  
Lake Level ◽  
Stochastic Simulations ◽  
Stochastic Methods ◽  
Multiple Time Scales ◽  
Lake Tana ◽  
Time Frequency ◽  
Analysis Methods

Abstract. Lake Tana is the largest fresh water body situated in the north western highlands of Ethiopia. It serves for local transport, electric power generation, fishing, ecological restoration, recreational purposes, and dry season irrigation supply. Evidence show, the lake has dried at least once at about 15 000–17 000 BP (before present) due to a combination of high evaporation and low precipitation events. Past attempts to observe historical fluctuation of Lake Tana based on simplistic water balance approach of inflow, out-flow and storage have failed to capture well known events of drawdown and rise of the lake that have happened in the last 44 years. This study is aimed at simulating the lake level, specifically extreme events of the lake variation using stochastic approaches. Fourty-four years of daily, monthly and mean annual lake level data has showed a Gaussian variation with goodness of fit at 0.01 significant levels of the Konglomorov-Simrnov test. Three stochastic methods were employed, namely perturbations approach, Monte-Carlo methods and wavelet analysis, and the results were compared with the stage level measurements. The stochastic simulations predicted the lake stage level of the 1972, 1984 and 2002/2003 historical droughts 99% of the time. The information content (frequency) of fluctuation of Lake Tana for various periods was resolved using Wigner's Time-Frequency Decomposition method. The wavelet analysis agreed with the perturbations and Monte Carlo simulations resolving the time (1970s, 1980s and 2000s) in which low frequency and high spectral power fluctuation has occurred. In summary, the Monte-Carlo and perturbations methods have shown their superiority for risk analysis over deterministic methods while wavelet analysis has met reconstructing stage level historical record at multiple time scales. A further study is recommended on dynamic forecasting of the Lake Tana stage level using a combined approach of the perturbation and wavelet analysis methods.

scholarly journals Stochastic Methods in Risk Analysis

2017 ◽  
Vol 12 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Vladimíra Osadská
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Analytical Methods ◽  
State Of The Art ◽  
Practical Experience ◽  
Monte Carlo Sampling ◽  
Stochastic Methods ◽  
Analysis Methods ◽  
The Impact

Abstract In this paper, we review basic stochastic methods which can be used to extend state-of-the-art deterministic analytical methods for risk analysis. We can conclude that the standard deterministic analytical methods highly depend on the practical experience and knowledge of the evaluator and therefore, the stochastic methods should be introduced. The new risk analysis methods should consider the uncertainties in input values. We present how large is the impact on the results of the analysis solving practical example of FMECA with uncertainties modelled using Monte Carlo sampling.

Nappes d'huile: prediction de leurs deplacements comme agent efficace d'un plan de mesure d'urgence

1979 ◽  
Vol 14 (1) ◽  
pp. 89-109
Author(s):  
B. Coupal ◽  
M. de Broissia
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Finite Elements ◽  
Mass Balance ◽  
Stochastic Method ◽  
Stochastic Methods ◽  
Deterministic Approach ◽  
First Order ◽  
The Third ◽  
Comme Agent

Abstract The movement of oil slicks on open waters has been predicted, using both deterministic and stochastic methods. The first method, named slick rose, consists in locating an area specifying the position of the slick during the first hours after the spill. The second method combines a deterministic approach for the simulation of current parameters to a stochastic method simulating the wind parameters. A Markov chain of the first order followed by a Monte Carlo approach enables the simulation of both phenomena. The third method presented in this paper describes a mass balance on the spilt oil, solved by the method of finite elements. The three methods are complementary to each other and constitute an important point for a contingency plan.

scholarly journals Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

2021 ◽  
Author(s):  
Fabio Sabetta ◽  
Antonio Pugliese ◽  
Gabriele Fiorentino ◽  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Stochastic Simulations ◽  
Model Parameters ◽  
Motion Model ◽  
Arias Intensity ◽  
Time Frequency ◽  
Ground Motion Model ◽  
Ground Motion Records

AbstractThis work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.

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