Using Diversity Ensembles with Time Limits to Handle Concept Drift

While traditional supervised learning focuses on static datasets, an increasing amount of data comes in the form of streams, where data is continuous and typically processed only once. A common problem with data streams is that the underlying concept we are trying to learn can be constantly evolving. This concept drift has been of interest to researchers the last few years and there is a need for improved machine learning algorithms that are capable of dealing with concept drifts. A promising approach involves using an ensemble of a diverse set of classifiers. The constituent classifiers are re-trained when a concept drift is detected. Decisions regarding the number of classifiers to maintain and the frequency of re-training classifiers are critical factors that determine classification accuracy in the presence of concept drift. This dissertation systematically investigated these issues in order to develop an improved classifier for online ensemble learning. The impact of reducing the time requiring additional ensembles was studied using artificial and real world datasets. Findings from these studies revealed that in many cases the number of time steps additional ensembles are in memory can be reduced without sacrificing prequential accuracy. It was also found that this new ensemble approach performed well in the presence of false concept drift

Advances in understanding natural groundwater quality controls in coastal aquifers

Groundwater quality in coastal aquifers is largely influenced by the interaction between the sea and the bordering aquifer systems. This interaction can result in freshening of saline aquifers or salinization of fresh water bodies. In complex cases even both situations can be found in the same aquifer system. While the main mechanism for salinization or freshening is hydrodynamically driven (groundwater flow), also physical and chemical processes within the aquifer will alter groundwater composition. Cation exchange is in many cases an important process to consider; it results in a hydrochemical spectrum of groundwater types reflecting both the hydrodynamical and hydrochemical characteristics of the aquifer.In order to understand the natural groundwater quality and the controlling processes in a coastal aquifer, it is crucial to known both the hydrodynamical and hydrochemical behaviour and the way these are linked together, because usually only the interaction between them can explain the observed quality istributions.Where mixing of fresh and salt water occurs, density-driven flow may become important and change quality distribution, while the groundwater composition itself influences hydrodynamics. An integrated approach of both aspects is indispensable.Modelling is an important tool in understanding how aquifer systems work. Until recently, hydrodynamical and hydrochemical aspects were tackled separately, with different models. Hydrodynamical aspects have been studied with flow models (such as MODFLOW) or a combination of a flow and a solute transport model (such as MT3D) for simulating salt transport, coupled together for incorporating density-driven flow (such as in SEAWAT). On the other hand, hydrochemical aspects have been investigated with hydrogeochemical models based on speciation and a reaction scheme. In hydrogeochemical models, groundwater flow was either not taken into account, or, at best, it was considered in a very simplified approach (along a path-line). Recently, codes have been developed combining three-dimensional flow models fully with the flexibility of an extensible hydrochemical model based on thermodynamic databases, such as PHAST, which combines the well-known PHREEQC model with the HST3D model for flow and transport. Simulations with the PHAST model have shown that it is capable of simulating the whole hydrochemical spectrum of groundwater types in coastal aquifers under freshening and salinizing conditions incorporating cation exchange processes and mineral equilibria such as calcite dissolution. The capabilities of this new generation of models will also allow for including redox components (iron content of the water) or processes such as surface complexation (e.g. sorption onto hydroxides). The future for such models looks bright, because for the first time they will provide the complete groundwater composition as their output.However, models need to be based on good and sufficient field data! Without them the reliability of the models is unknown and their value for predictive purposes is hypothetical. Therefore the advances in modelling should go together with new techniques of sampling, measuring and monitoring and with improved analytical methods. Advances have been made also in these fields. Yet, the most important advance in understanding natural groundwater quality is not related to a specific technical innovation but rests in the minds of hydrogeologists. Indeed, only a profound insight in the combined hydrodynamical and hydrochemical aspects by a refined system analysis can provide the key to really understand what controls groundwater quality, also in coastal aquifers

Ranking of water-table depths for purposes of ecosystem management in the coastal dunes of Belgium

Integrated monitoring was conducted to evaluate the result of nature development actions to increase the biodiversity. During the first year, the natural fluctuation of the piezometric level has been determined based on existing data and newly gathered data. From these data, the groundwater table classes following a classification, developed in The Netherlands, have been deduced. However, the result does not correspond with the observed vegetation. To predict the natural habitats and ecosystems in the coastal dunes of Belgium, a new classification adapted to natural conditions in the area is required. Advantages of existing approaches are integrated in the classification, combined with new elements, to represent the relationship between groundwater regimes and ecosystems. This new classification, consisting of 4 codes, provides information about the mean high water table depth, the mean low water table depth, mean spring water table depth and whether inundation can take place or not. It also gives an indication of the variability of the seasonal fluctuations of the water table and the importance of the fluctuation of the water table between years. With this new classification, a fine breakdown by ecotypes is possible. The new classification has been applied to the existing data in the study area