Sensors 2011, 11(4), 3939-3961; doi:10.3390/s110403939

Time-Frequency Analyses of Tide-Gauge Sensor Data

Received: 21 February 2011; in revised form: 19 March 2011 / Accepted: 31 March 2011 / Published: 1 April 2011
(This article belongs to the Section Physical Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors’ data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented.
Keywords: tide-gauge sensors; sea level; time series; spectral analysis; time-frequency analysis; LSSA; neural networks; wavelet transform; cross wavelet transform; wavelet coherence
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MDPI and ACS Style

Erol, S. Time-Frequency Analyses of Tide-Gauge Sensor Data. Sensors 2011, 11, 3939-3961.

AMA Style

Erol S. Time-Frequency Analyses of Tide-Gauge Sensor Data. Sensors. 2011; 11(4):3939-3961.

Chicago/Turabian Style

Erol, Serdar. 2011. "Time-Frequency Analyses of Tide-Gauge Sensor Data." Sensors 11, no. 4: 3939-3961.

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