2014 

Saulquin, B., et al. "Multiscale EventBased Mining in Geophysical Time Series: Characterization and Distribution of Significant TimeScales in the Sea Surface Temperature Anomalies Relatively to ENSO Periods from 1985 to 2009." IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.. 7.8 (2014): 3543–3552.
Résumé: In this paper, onedimensional (1D) geophysical time series are regarded as series of significant timescale events. We combine a waveletbased analysis with a Gaussian mixture model to extract characteristic timescales of 486 144 detected events in the Sea Surface Temperature Anomaly (SSTA) observed from satellite at global scale from 1985 to 2009. We retrieve four lowfrequency characteristic timescales of Nino Southern Oscillation (ENSO) in the 1.5 to 7year range and show their spatial distribution. Highfrequency (HF) SSTA event spatial distribution shows a dependency to the ENSO regimes, pointing out that the ENSO signal also involves specific signatures at these timescales. These finescale signatures can hardly be retrieved from global EOF approaches, which tend to exhibit uppermost the lowfrequency influence of ENSO onto the SSTA. In particular, we observe at global scale a major increase by 11% of the number of SSTA HF events during Nino periods, with a local maximum of 80% in Europe. The methodology is also used to highlight an ENSOinduced frequency shift during the major 19972000 ENSO event in the intertropical Pacific. We observe a clear shift from the high frequencies toward the 3.36year scale with a maximum shift occurring 2 months before the ENSO maximum of energy at 3.36year scale.
MotsClés: algorithm; climatechange; Distribution of the sea surface temperature anomalies events related to the ENSO periods; eventbased mining in large geophysical datasets (big data); frequency; geophysical time series as series of significant timescale events; models; monsoon variability; ocean; pacific; patterns; predictability; wavelet analysis


2012 

Carteron, A., et al. "Assessing the efficiency of clustering algorithms and goodnessoffit measures using phytoplankton field data." Ecol. Inform.. 9 (2012): 64–68.
Résumé: Investigation of patterns in beta diversity has received increased attention over the last years particularly in light of new ecological theories such as the metapopulation paradigm and metacommunity theory. Traditionally, beta diversity patterns can be described by cluster analysis (i.e. dendrograms) that enables the classification of samples. Clustering algorithms define the structure of dendrograms, consequently assessing their performance is crucial. A common, although not always appropriate approach for assessing algorithm suitability is the cophenetic correlation coefficient c. Alternatively the 2norm has been recently proposed as an increasingly informative method for evaluating the distortion engendered by clustering algorithms. In the present work, the 2norm is applied for the first time on field data and is compared with the cophenetic correlation coefficient using a set of 105 pairwise combinations of 7 clustering methods (e.g. UPGMA) and 15 (dis)similarity/distance indices (e.g. Jaccard index). In contrast to the 2norm, cophenetic correlation coefficient does not provide a clear indication on the efficiency of the clustering algorithms for all combinations. The two approaches were not always in agreement in the choice of the most faithful algorithm. Additionally, the 2norm revealed that UPGMA is the most efficient clustering algorithm and Ward's the least. The present results suggest that goodnessoffit measures such as the 2norm should be applied prior to clustering analyses for reliable beta diversity measures. (C) 2012 Elsevier B.V. All rights reserved.


2010 

Duboz, R., et al. "Application of an evolutionary algorithm to the inverse parameter estimation of an individualbased model." Ecological Modelling. 221 (2010): 840–849.
Résumé: Inverse parameter estimation of individualbased models (IBMs) is a research area which is still in its infancy, in a context where conventional statistical methods are not well suited to confront this type of models with data. In this paper, we propose an original evolutionary algorithm which is designed for the calibration of complex IBMs, i.e. characterized by high stochasticity, parameter uncertainty and numerous nonlinear interactions between parameters and model output. Our algorithm corresponds to a variant of the populationbased incremental learning (PBIL) genetic algorithm, with a specific “optimal individual” operator. The method is presented in detail and applied to the individualbased model OSMOSE. The performance of the algorithm is evaluated and estimated parameters are compared with an independent manual calibration. The results show that automated and convergent methods for inverse parameter estimation are a significant improvement to existing ad hoc methods for the calibration of IBMs.
MotsClés: algorithms; and; calibration; ecosystem; estimation; Evolutionary; genetic; Individualbased; marine; model; Parameter
