Structural Time Series Modeling: an Application to Environmental Variables

A structural time series model is one which is set up in terms of components which have a direct interpretation. In this paper, the discussion focuses on the dynamic modeling procedure based on the state space approach (associated to the Kalman filter), in the context of surface water quality monitoring, in order to analyze and evaluate the temporal evolution of the environmental variables, and thus identify trends or possible changes in water quality (change point detection). The approach is applied to environmental time series: time series of surface water quality variables in a river basin. The statistical modeling procedure is applied to monthly values of physico- chemical variables measured in a network of 8 water monitoring sites over a 15-year period (1999-2014) in the River Ave hydrological basin located in the Northwest region of Portugal.

Contribution to the study of the Ria de Aveiro inlet morphodynamics

Over the years it was observed at the Ria de Aveiro lagoon inlet, near the head of the north breakwater, a depth increase that might threaten the stability of this structure. A trend of accretion in the navigation channel of this lagoon is observed, endangering the navigation in this region. In order to understand the origin of these and other trends observed, the knowledge of the sediment transport in the study area is imperative. The main aim of this work is understanding the dominant physical processes in the sediment transport of sediment at the Ria de Aveiro lagoon inlet and adjacent area, improving knowledge of this region morphodynamics. The methodology followed in this study consisted in the analyzes of the topohydrographic surveys performed by the Administration of the Aveiro Harbor, and in the numerical simulations results performed with the morphodynamic modeling system MORSYS2D. The analysis of the surveys was performed by studying the temporal evolution of the bathymetry. The numerical analysis was based on the implementation of the model at the study area, sensitivity analysis of the formulations used to compute the sediment transport to the variation of input parameters (e.g. depth, sediment size, tidal currents) and analysis of the sediment uxes and bathymetric changes predicted. The simulations considered as sediment transport forcing the tidal currents only and the coupled forcing of tides and waves. Considering the wave e ect as sediment transport forcing, both monochromatic waves and a wave regime were simulated. The results revealed that the observed residual sediment transport patterns are generated due to the channel con guration. Inside the lagoon the uxes are mainly induced by the tidal currents action, restricting the action of waves to the inlet and adjacent coast. In the navigation channel the residual sediment uxes predicted are directed o - shore with values between 7 and 40 m3=day generating accretions of approximately 10 m3=day for the shallower region and 35 m3=day for the region between the tidal gauge and the tri^angulo das mar es. At the inlet, the residual uxes are approximately 30 m3=day inducing trends of erosion of approximately 20 m3=day. At the North side of the nearshore accretion is predicted, while at the South side is predicted erosion, at the rates of 250 and 1500 m3=day, respectively. It was also concluded that the waves with higher contribution to the residual sediment uxes are those with heights between 4 and 5 m. However, the storm waves with heights bigger than 5 m, despite their 10% of frequency of occurrence are responsible for 25% of the observed sediment transport.

Convex Quadratic Programming Approach to the Maximum Matching Problem

The problem of determining a maximum matching or whether there exists a perfect matching, is very common in a large variety of applications and as been extensively studied in graph theory. In this paper we start to introduce a characterisation of a family of graphs for which its stability number is determined by convex quadratic programming. The main results connected with the recognition of this family of graphs are also introduced. It follows a necessary and sufficient condition which characterise a graph with a perfect matching and an algorithmic strategy, based on the determination of the stability number of line graphs, by convex quadratic programming, applied to the determination of a perfect matching. A numerical example for the recognition of graphs with a perfect matching is described. Finally, the above algorithmic strategy is extended to the determination of a maximum matching of an arbitrary graph and some related results are presented.