Assessment of nonlinear trends and seasonal variations in global sea level using singular spectrum analysis and wavelet multiresolution analysis

Khelifa, Sofiane; Gourine, Bachir; Rami, Ali; Taibi, Habib

doi:10.1007/s12517-016-2584-6

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…To meet this challenge, an alternative technique, identification of a model constructed on the basis of the wavelet multiresolution analysis (MRA), is used in the present work. MRA has become one of the major tools in neural networks [7][8][9][10] and nonlinear system modeling [11][12][13][14][15][16][17][18]. Wavelet-based multiresolution decomposition has been proven to constitute a universal approximator for a wide range of function spaces in terms of linear combination of scaling and wavelet functions.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

et al. 2020

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Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

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Section: Introductionmentioning

confidence: 99%

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

et al. 2020

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“…In another study, continuous wavelet filtering, multi-resolution decomposition based on the maximal overlap discrete wavelet transform, auto-regressive-based decomposition, singular spectrum analysis, and empirical mode decomposition were all applied to the Baltic sea level time series to investigate the existence of long-term seasonal cycle changes [8]. Khelifa et al, [9] evaluated the global sea level anomaly time series using the singular spectrum analysis and the wavelet multiresolution analysis to look for signs of seasonality and trend. Moon and Lall (1995) [10] showed evidence of quasi-periodic interannual and interdecadal variability in the Great Salt Lake (GSL) volume fluctuations by performing Singular Spectrum Analysis on the lake's monthly volume changes.…”

Section: Introductionmentioning

confidence: 99%

Lake Volume Data Analyses: A Deep Look into the Shrinking and Expansion Patterns of Lakes Azuei and Enriquillo, Hispaniola

Moknatian

Piasecki

2019

Hydrology

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This paper presents the development of an evenly spaced volume time series for Lakes Azuei and Enriquillo both located on the Caribbean island of Hispaniola. The time series is derived from an unevenly spaced Landsat imagery data set which is then exposed to several imputation methods to construct the gap filled uniformly-spaced time series so it can be subjected to statistical analyses methods. The volume time series features both gradual and sudden changes the latter of which is attributed to North Atlantic cyclone activity. Relevant cyclone activity is defined as an event passing within 80 km and having regional monthly rainfall averages higher than a threshold value of 87 mm causing discontinuities in the lake responses. Discontinuities are accounted for in the imputation algorithm by dividing the time series into two sub-sections: Before/after the event. Using leave-p-out cross-validation and computing the NRMSE index the Stineman interpolation proves to be the best algorithm among 15 different imputation alternatives that were tested. The final time series features 16-day intervals which is subsequently resampled into one with monthly time steps. Data analyses of the monthly volume change time series show Lake Enriquillo’s seasonal periodicity in its behavior and also its sensitivity due to the occurrence of storm events. Response times feature a growth pattern lasting for one to two years after an extreme event, followed by a shrinking pattern lasting 5–7 years returning the lake to its original state. While both lakes show a remarkable long term increase in size starting in 2005, Lake Azuei is different in that it is much less sensitive to storm events and instead shows a stronger response to just changing seasonal rainfall patterns.

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Analysis of Dam Deformation Using Artificial Neural Networks Methods and Singular Spectrum Analysis

Gourine¹,

Khelifa²

2018

Advances in Science, Technology &Amp; Innovation

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Assessment of nonlinear trends and seasonal variations in global sea level using singular spectrum analysis and wavelet multiresolution analysis

Cited by 6 publications

References 36 publications

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Lake Volume Data Analyses: A Deep Look into the Shrinking and Expansion Patterns of Lakes Azuei and Enriquillo, Hispaniola

Analysis of Dam Deformation Using Artificial Neural Networks Methods and Singular Spectrum Analysis

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