Detecting Stationary Intervals for Random Waves Using Time Series Clustering

2014 ◽  
Author(s):  
Carolina Euán ◽  
Joaquín Ortega ◽  
Pedro C. Alvarez-Esteban
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Transition Period ◽  
Distance Matrix ◽  
Real Data ◽  
Wave Climate ◽  
Time Interval ◽  
Random Waves ◽  
Sea Waves ◽  
Wave Measurements

The problem of detecting changes in the state of the sea is very important for the analysis and determination of wave climate in a given location. Wave measurements are frequently statistically analyzed as a time series, and segmentation algorithms developed in this context are used to determine change-points. However, most methods found in the literature consider the case of instantaneous changes in the time series, which is not usually the case for sea waves, where changes take a certain time interval to occur. We propose a new segmentation method that allows for the presence of transition intervals between successive stationary periods, and is based on the analysis of distances of normalized spectra to detect clusters in the time series. The series is divided into 30-minutes intervals and the spectral density is estimated for each one. The normalized spectra are compared using the Total Variation distance and a hierarchical clustering method is applied to the distance matrix. The information obtained from the clustering algorithm is used to classify the intervals as belonging to a stationary or a transition period We present simulation studies to validate the method and examples of applications to real data.

Optimization of Hydraulic Efficiency of a Free Surface Semicircular Breakwater Using Wave Screens

2015 ◽  
Author(s):  
Hee Min Teh ◽  
Vengatesan Venugopal
Keyword(s):  
Free Surface ◽  
Wave Climate ◽  
Wave Transmission ◽  
Hydrodynamic Performance ◽  
Wave Transformation ◽  
Random Waves ◽  
Wave Flume ◽  
Wave Measurements ◽  
Test Models ◽  
Semicircular Breakwater

A free surface semicircular breakwater (SCB) with rectangular perforations has been developed to serve as a wave defence structure. Hydrodynamic performance of the breakwaters of various perforations has been thoroughly investigated through wave measurements in a wave flume under random waves. The SCBs were experimentally confirmed to be good anti-reflection wave structures; however, the level of wave transmission at the leeside of the SCBs was rather high particularly when immersed in limited depth and confronted by waves of longer period. This study aims at optimizing the hydraulic characteristics of the SCB by extending its draft by means of wave screens. Three test configurations have been identified in this study, namely (1) the SCB with front screen, (2) the SCB with rear screen, and (3) the SCB with double screens. For each wave screen, three porosities (i.e. 25, 40 and 50%) have been considered in the experiments. The models of shallow immersion depths have been tested in random waves of different characteristics in a wave flume. Wave transformation at different locations upstream and downstream of the test models has been recorded by wave probes. The hydraulic performance of the breakwater are quantified by the coefficients of wave transmission, reflection and energy dissipation, and the wave climate in the vicinity of the breakwater are presented in the form of a ratio relative to the incident wave height. The optimum design of SCB supplemented by truncated wave screen(s) is proposed at the end of the study.

MODELING AND ANALYSIS OF GENE EXPRESSION TIME-SERIES BASED ON CO-EXPRESSION

2005 ◽  
Vol 15 (04) ◽  
pp. 311-322 ◽  
Author(s):  
CARLA S. MÖLLER-LEVET ◽  
HUJUN YIN
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Clustering Algorithm ◽  
Real Data ◽  
Modeling And Analysis ◽  
Novel Approach ◽  
Gene Expression Time Series ◽  
Temporal Profiles ◽  
Expression Time

In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologicaly relevant clusters.

scholarly journals K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series

2019 ◽  
Vol 11 (18) ◽  
pp. 2161 ◽  
Author(s):  
Dong Peng ◽  
Ting Pan ◽  
Wen Yang ◽  
Heng-Chao Li
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Clustering Algorithm ◽  
Pattern Mining ◽  
Distance Matrix ◽  
Sar Image ◽  
Change Pattern ◽  
The Matrix ◽  
Distance Matrices ◽  
K Matrix

In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series based on the change detection matrix (CDM), here, we directly use the distance matrix to determine changed pixels and extract change patterns. The proposed scheme involves two steps: change detection in SAR image time series and change-pattern discovery. First, these distance matrices are constructed for each spatial position over the time series by a dissimilarity measurement. The changed pixels are detected by using a thresholding algorithm on the energy feature map of all distance matrices. Then, according to the change detection results in SAR image time series, the changed areas for pattern mining are determined. Finally, the proposed K-Matrix algorithm which clusters distance matrices by the matrix cross-correlation similarity is used to group all changed pixels into different change patterns. Experimental results on two datasets of TerraSAR-X image time series illustrate the effectiveness of the proposed method.

Comparison of two methods for optimizing existing groundwater monitoring networks: application to the Bacchiglione Basin, Italy

2020 ◽  
Author(s):  
Andrea Sottani ◽  
Mara Meggiorin ◽  
Luís Ribeiro ◽  
Andrea Rinaldo
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Groundwater Monitoring ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Pearson Correlation ◽  
Sampling Time ◽  
Time Interval ◽  
Monitoring Networks ◽  
Simple Method

<p>In the presence of a groundwater monitoring network (GMN) of sensors aimed at measuring the hydraulic head in a given domain, the statistical analysis of time series not only provides insight into the general aquifer behaviour, but it can also return parameters useful to optimize and enhance the GMN’s efficiency.</p><p>Several methods to design new GMNs are available, but few of them are useful for optimizing existing networks. This study compares two methods in order to define pros and cons of their applicability and effectiveness.</p><p>They are carried out for the case study of the alluvial basin of the Bacchiglione river, near Vicenza (Veneto, Italy). The existing network comprises 92 groundwater data-loggers, installed in wells screening mostly the unconfined aquifer.</p><p>The first simple method, here proposed, is based on the Pearson correlation coefficient and the microscale parameter, which shows the time interval in which data are perfectly correlated. The coefficients were calculated between detrended time series. Firstly, based on the correlation coefficient threshold of 0.95, areas of intercorrelated couples are defined. They are characterized by similar hydrological behaviour, therefore it is sufficient to constantly monitor only one location in each area, while other interesting correlated points can be measured manually at longer sampling time. The microscale can be used to estimate this sampling time in order to see the water table trend (between 7 and 78 days in this domain), even if shorter oscillations are obviously missed and some peaks could remain unseen. This way, extra sensors can be moved to other critical areas, in order to improve the system knowledge.</p><p>The second method defines the seasonal Mann Kendall (sMK) test for detecting monotonic trends, that are used into Principal Component Analysis (PCA). Finally, a Hierarchical Clustering Analysis is carried out to group sensors with similar factors of the PCA. This method is more articulated than the previous one and entails some informed choices to be made about the distance measure and the clustering algorithm. Thanks to the sMK test and the PCA, a high insight of the system is achieved, however the clustering result may strongly variate depending on the expert’s knowledge and expectation.</p><p>The two proposed statistical analyses of hydrogeological data provide integrative decision support to improve representativeness and effectiveness of monitoring networks aimed at both qualitative and quantitative groundwater control.</p>

Wind speed data analysis for predictions of sea waves in Bitung Coastal Waters

2013 ◽  
pp. 35 ◽  
Author(s):  
Joanes E Koagouw ◽  
Gybert E Mamuaya ◽  
Adrie A Tarumingkeng ◽  
P A Angmalisang
Keyword(s):  
Data Analysis ◽  
Wind Speed ◽  
Coastal Waters ◽  
Transition Period ◽  
Marine Resources ◽  
Land Uses ◽  
Sea Waves ◽  
North West ◽  
North Sulawesi ◽  
The Waves

Coastal area of Bitung Municipality is one of the economical activities centers in North Sulawesi Province such as for land-uses and the exploitation of natural resources. Those activities are exaggerating day bay day and tended to be uncontrollable. The excess of those conditions, it has been recorded the change of waves in Bitung waters that has impacts to coastal areas and can affect the utilization of coastal and marine resources. This research was aimed to observe waves altitude variations in Bitung waters with Svedrup Munk and Bretchsneider (SMB) method that had been used to predict waves altitudes. The results showed that the wind speed during West Season was 0.33 m and were dominant to the East, while during East season was 0.91m from South-East to North-West, and then on transition period (March to May) was 1.08m from South-East to East. The results of those wind speed to the waves altitudes in Bitung waters is discussed in this paper© Pesisir pantai Kota Bitung merupakan salah satu pusat aktivitas ekonomi (misalnya pemanfaatan lahan dan eksploitasi sumberdaya) di Provinsi Sulawesi Utara. Aktivitas tersebut semakin hari semakin meningkat dan memiliki kecenderungan tidak terkontrol. Akibat dari keadaan tersebut, telah terjadi perubahan fenomena gelombang di perairan Bitung yang berdampak pada keberadaan daerah pesisir pantai di mana hal ini dapat mengganggu aktivitas pemanfaatan sumberdaya pesisir dan laut tersebut. Penelitian ini bertujuan untuk mengetahui variasi tinggi gelombang di perairan Bitung dengan menggunakan metode Svedrup Munk and Bretchsneider (SMB) yang biasa digunakan untuk peramalan tinggi gelombang signifikan. Hasil penelitian menunjukkan bahwa kecepatan angin pada Musim Barat sebesar 0,33 meter dan dominan ke arah Timur, sementara pada Musim Timur sebesar 0,91 meter dari arah Tenggara ke Barat Laut, serta pada Musim Peralihan (antara bulan Maret-Mei) adalah sebesar 1,08 meter dari arah Tenggara dan Timur. Pengaruh kecepatan angin tersebut terhadap gelombang laut di perairan Bitung dibahas dalam tulisan ini©

scholarly journals Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

2021 ◽  
Vol 13 (11) ◽  
pp. 2075
Author(s):  
J. David Ballester-Berman ◽  
Maria Rastoll-Gimenez
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Oil Palm ◽  
Time Interval ◽  
Potential Candidate ◽  
Initial Growth ◽  
Natural Forests ◽  
Wet Season ◽  
Radar Signatures ◽  
The Times

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

scholarly journals Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua
Keyword(s):  
Time Series ◽  
Default Risk ◽  
Real Life ◽  
Statistical Error ◽  
Real Data ◽  
Sampling Period ◽  
Calibration Procedure ◽  
Industry Standards ◽  
Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

scholarly journals Regional Downscaling of Copernicus ERA5 Wave Data for Coastal Engineering Activities and Operational Coastal Services

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 859
Author(s):  
Giorgio Bellotti ◽  
Leopoldo Franco ◽  
Claudia Cecioni
Keyword(s):  
Time Series ◽  
National Level ◽  
Wave Model ◽  
Wave Climate ◽  
Tyrrhenian Sea ◽  
Computational Domain ◽  
Multiple Wave ◽  
Space Resolution ◽  
Wave Data ◽  
Fundamental Information

Hindcasted wind and wave data, available on a coarse resolution global grid (Copernicus ERA5 dataset), are downscaled by means of the numerical model SWAN (simulating waves in the nearshore) to produce time series of wave conditions at a high resolution along the Italian coasts in the central Tyrrhenian Sea. In order to achieve the proper spatial resolution along the coast, the finite element version of the model is used. Wave data time series at the ERA5 grid are used to specify boundary conditions for the wave model at the offshore sides of the computational domain. The wind field is fed to the model to account for local wave generation. The modeled sea states are compared against the multiple wave records available in the area, in order to calibrate and validate the model. The model results are in quite good agreement with direct measurements, both in terms of wave climate and wave extremes. The results show that using the present modeling chain, it is possible to build a reliable nearshore wave parameters database with high space resolution. Such a database, once prepared for coastal areas, possibly at the national level, can be of high value for many engineering activities related to coastal area management, and can be useful to provide fundamental information for the development of operational coastal services.

scholarly journals Long-Term Observations of Beach Variability at Hasaki, Japan

2020 ◽  
Vol 8 (11) ◽  
pp. 871
Author(s):  
Masayuki Banno ◽  
Satoshi Nakamura ◽  
Taichi Kosako ◽  
Yasuyuki Nakagawa ◽  
Shin-ichi Yanagishima ◽  
...  
Keyword(s):  
Morphological Change ◽  
High Frequency ◽  
Climate Changes ◽  
Wave Climate ◽  
Time Interval ◽  
Observation Data ◽  
Beach Profile ◽  
Water Level Variations ◽  
Monthly Variations

Long-term beach observation data for several decades are essential to validate beach morphodynamic models that are used to predict coastal responses to sea-level rise and wave climate changes. At the Hasaki coast, Japan, the beach profile has been measured for 34 years at a daily to weekly time interval. This beach morphological dataset is one of the longest and most high-frequency measurements of the beach morphological change worldwide. The profile data, with more than 6800 records, reflect short- to long-term beach morphological change, showing coastal dune development, foreshore morphological change and longshore bar movement. We investigated the temporal beach variability from the decadal and monthly variations in elevation. Extremely high waves and tidal anomalies from an extratropical cyclone caused a significant change in the long-term bar behavior and foreshore slope. The berm and bar variability were also affected by seasonal wave and water level variations. The variabilities identified here from the long-term observations contribute to our understanding of various coastal phenomena.

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