Sebastián Múnera

Data-based methods and algorithms for the analysis of sandbar behavior with exogenous variables

Sandbars are natural features generated in the nearshore zones by the interaction between the sea and the coast. The short-term processes that drive sandbar behavior are waves and sediment transport. The interaction between waves and the coast is highly nonlinear and, traditionally, process-based models (e.g., evolution models) have been used for modelling and analyzing sandbar behavior in the short term. However, medium- to long-term predictions are not always possible with these models due to their exponential error accumulation. Data-driven models emerge as an alternative to process-based models as they do not need insight on the physical knowledge of the model, but they extract knowledge from patterns found in the data. In this paper, we apply the data-driven techniques: EMD (empirical mode decomposition) method and ARNN (autoregressive neural networks) on sandbar and wave time series from the coast of Cartagena de Indias, Colombia. The former is used for analyzing the relationship between sandbar and wave conditions in a graphical way; and the latter is used for deriving nonlinear simple/partial cross/auto-correlation coefficients. Evidence of nonlinear dependencies is detected between the present state of sandbar location and the past states of wave conditions. HighlightsData-driven techniques were used to analyze sandbar data.Neural networks helped to detect nonlinear relations between sandbar and waves.It was found that sandbar behavior has memory.Empirical mode decomposition helps to visualize complex sandbar behavior.Memory and external waves produce an elastic effect in sandbar movement.

A Trajectory Model to Deal with Transmission Failures

A trajectory records the evolution of the position of a moving object in a space during a time interval. In Spaccapietras trajectory model, trajectories are segmented in subintervals called stops and moves. On the other hand, during some periods failures can occur in the transmission of data of the trajectory causing missings of information. In this paper, we extend Spaccapietras model by incorporating the missing information as a component of a trajectory. We consider this issue not only with regard to the object position but also with regard to other attributes of the trajectory (complementary attributes). We propose a classification for these attributes, depending on whether they are constant or variable during the stops and the moves. We also propose two algorithms: i) to convert a sequence of observations of a trajectory into stops, moves and missings. ii) to check that the data recorded for the attributes whose value must be constant is consistent.

Un modelo para la representación de ausencia de información en trayectorias

Una trayectoria representa la evolucion de la posicion de un objeto movil en un espacio durante un periodo. Cuando se estan registrando los datos de una trayectoria pueden ocurrir fallas en la transmision generando ausencias de informacion. En este articulo, se extiende el modelo de trayectorias de Spaccapietra al incorporar la ausencia de informacion como un componente de una trayectoria. La ausencia de informacion se considera con respecto a la posicion del objeto y a otros atributos de la trayectoria (atributos complementarios). Se propone una clasificacion para estos atributos, dependiendo de si son constantes o variables durante los stops y los moves (los stops y los moves son partes de una trayectoria segun el modelo de Spaccapietra). Ademas se proponen dos algoritmos: i) para convertir una secuencia de observaciones de una trayectoria en stops , moves y ausencias y ii) para verificar que la informacion almacenada de los atributos cuyos valores deben ser constantes sea consistente. Para validar la propuesta, se experimento con datos reales correspondientes a la trayectoria de un avion.