Agricultural intensification on the Canterbury Plains in New Zealand has lead to the degradation of natural streams and rivers through lowering of water quality and significant reduction of surface flows from the use of ground and surface water resources. However, this same agricultural expansion has led to the development of a network of permanently flowing open water races to supply stock water to farms across the Canterbury Plains. Stock water races form an extensive network, with approximately 6,500 km of races. Initially I surveyed 62 water races and compared habitat characteristics, water quality, benthic invertebrate and fish communities with nearby natural streams. Races are characterised physically by straight, narrow and shallow channels, and small, uniform substrate. Water races are more turbid than natural streams, and can have high summer temperatures. The benthic macroinvertebrate communities of water races contained a range of taxa, including some not found in natural streams, but communities were less diverse than communities in natural streams, and tended to be dominated by a limited set of generalist taxa. A longitudinal study of three water races showed gradients in physical characteristics of races, including a downstream decrease in channel width, water depth, current velocity and substrate size. However, few strong longitudinal changes to community structure were found, as the generalist taxa commonly occurring in water races were able to tolerate conditions throughout the race network. To test if macroinvertebrate communities were limited by the homogeneous habitat of water races, I conducted a substrate manipulation experiment, where large cobbles and small boulders were added to reaches in five water races. Despite an increase in substrate and current heterogeneity, there were few significant changes to the macroinvertebrate communities over the four months of the manipulation. This outcome does not eliminate low habitat heterogeneity as a limiting factor for water race communities. Rather, the benthic invertebrate community throughout the water race network is a product of the homogeneous habitat, which limits the availability of colonists of taxa that would benefit from increased habitat diversity. A survey of the fish assemblages of water races found races had a depauperate fish community. Only two species were commonly found in water races, and the average species richness of races was 1.5. By contrast natural streams had a higher diversity of fish species (mean 4 three species), and contained representatives of a greater number of species that are typical of streams and rivers on the Canterbury Plains. My research has shown that stock water races provide an important source of aquatic biodiversity on the plains, both in addition to natural streams and in their own right. However, the biodiversity value of stock water races could be improved with enhancement of in-stream habitat.

Urbanization has been associated with the degradation of streams, and a consequence of forest to urban land transition is a change in streamflow. Therefore, the purpose of this thesis is to examine the impacts of land-cover change in ten different watersheds in the rapidly urbanizing Atlanta, GA USA metropolitan area. Streamflow and precipitation data for a 30-year period (1986-2016) were analyzed in conjunction with land cover data from 1992, 2001, and 2011. Big Creek and Suwanee Creek experienced the most urbanization and increases (20%) in streamflow and runoff, and high flow (>95th percentile of flow) days doubled and increased 85%, respectively. Precipitation-adjusted streamflow for Peachtree Creek and Flint River decreased about 17%. Runoff ratios for South River were the highest among all watersheds, even the Etowah River, which remained moderately forested and had the most precipitation and slope.

No description available.

Mineração de fluxos contínuos de dados é uma área de pesquisa emergente que visa extrair conhecimento a partir de grandes quantidades de dados, gerados continuamente. Detecção de novidade é uma tarefa de classificação que consiste em reconhecer que um exemplo ou conjunto de exemplos em um fluxo de dados diferem significativamente dos exemplos vistos anteriormente. Essa é uma importante tarefa para fluxos contínuos de dados, principalmente porque novos conceitos podem aparecer, desaparecer ou evoluir ao longo do tempo. A maioria dos trabalhos da literatura apresentam a detecção de novidade como uma tarefa de classificação binária. Poucos trabalhos tratam essa tarefa como multiclasse, mas usam medidas de avaliação binária. Em vários problemas, o correto seria tratar a detecção de novidade em fluxos contínuos de dados como uma tarefa multiclasse, no qual o conceito conhecido do problema é formado por uma ou mais classes, e diferentes novas classes podem aparecer ao longo do tempo. Esta tese propõe um novo algoritmo MINAS para detecção de novidade em fluxos contínuos de dados. MINAS considera que a detecção de novidade é uma tarefa multiclasse. Na fase de treinamento, MINAS constrói um modelo de decisão com base em um conjunto de exemplos rotulados. Na fase de aplicação, novos exemplos são classificados usando o modelo de decisão atual, ou marcados como desconhecidos. Grupos de exemplos desconhecidos podem formar padrões-novidade válidos, que são então adicionados ao modelo de decisão. O modelo de decisão é atualizado ao longo do fluxo a fim de refletir mudanças nas classes conhecidas e permitir inserção de padrões-novidade. Esta tese também propõe uma nova metodologia para avaliação de algoritmos para detecção de novidade em fluxos contínuos de dados. Essa metodologia associa os padrões-novidade não rotulados às classes reais do problema, permitindo assim avaliar a matriz de confusão que é incremental e retangular. Além disso, a metodologia de avaliação propõe avaliar os exemplos desconhecidos separadamente e utilizar medidas de avaliação multiclasse. Por último, esta tese apresenta uma série de experimentos executados usando o MINAS e os principais algoritmos da literatura em bases de dados artificiais e reais. Além disso, o MINAS foi aplicado a um problema real, que consiste no reconhecimento de atividades humanas usando dados de acelerômetro. Os resultados experimentais mostram o potencial do algoritmo e da metodologia propostos / Data stream mining is an emergent research area that aims to extract knowledge from large amounts of continuously generated data. Novelty detection is a classification task that assesses if an example or a set of examples differ significantly from the previously seen examples. This is an important task for data streams, mainly because new concepts may appear, disappear or evolve over time. Most of the work found in the novelty detection literature presents novelty detection as a binary classification task. A few authors treat this task as multiclass, but even they use binary evaluation measures. In several real problems, novelty detection in data streams must be treated as a multiclass task, in which, the known concept about the problem is composed by one or more classes and different new classes may appear over time. This thesis proposes a new algorithm MINAS for novelty detection in data streams. MINAS deals with novelty detection as a multiclass task. In the training phase, MINAS builds a decision model based on a labeled data set. In the application phase, new examples are classified using the decision model, or marked with an unknown profile. Groups of unknown examples can be later used to create valid novelty patterns, which are added to the current decision model. The decision model is updated as new data arrives in the stream in order to reflect changes in the known classes and to allow the addition of novelty patterns. This thesis also proposes a new methodology to evaluate classifiers for novelty detection in data streams. This methodology associates the unlabeled novelty patterns to the true problem classes, allowing the evaluation of a confusion matrix that is incremental and rectangular. In addition, the proposed methodology allows the evaluation of unknown examples separately and the use multiclass evaluation measures. Additionally, this thesis presents a set of experiments carried out comparing the MINAS algorithm and the main novelty detection algorithms found in the literature, using artificial and real data sets. Finally, MINAS was applied to a human activity recognition problem using accelerometer data. The experimental results show the potential of the proposed algorithm and methodologies

In this thesis, learning refers to the intelligent computational extraction of knowledge from data. Supervised learning tasks require data to be annotated with labels, whereas for unsupervised learning, data is not labelled. Semi-supervised learning deals with data sets that are partially labelled. A major issue with supervised and semi-supervised learning of data streams is late-arriving or missing class labels. Assuming that correctly labelled data will always be available and timely is often unfeasible, and, as such, supervised methods are not directly applicable in the real world. Therefore, real-world problems usually require the use of semi-supervised or unsupervised learning techniques. For instance, when considering a spam detection task, it is not reasonable to assume that all spam will be identified (correctly labelled) prior to learning. Additionally, in semi-supervised learning, "the instances having the highest [predictive] confidence are not necessarily the most useful ones" [41]. We investigate how self-training performs without its selective heuristic in a streaming setting. This leads us to our contributions. We extend an existing concept drift detector to operate without any labelled data, by using a sliding window of our ensemble's prediction confidence, instead of a boolean indicating whether the ensemble's predictions are correct. We also extend selective self-training, a semi-supervised learning method, by using all predictions, and not only those with high predictive confidence. Finally, we introduce a novel windowing type for ensembles, as sliding windows are very time consuming and regular tumbling windows are not a suitable replacement. Our windowing technique can be considered a hybrid of the two: we train each sub-classifier in the ensemble with tumbling windows, but delay training in such a way that only one sub-classifier can update its model per iteration. We found, through statistical significance tests, that our framework is (roughly 160 times) faster than current state of the art techniques, and achieves comparable predictive accuracy. That being said, more research is needed to further reduce the quantity of labelled data used for training, while also increasing its predictive accuracy.

Para identificar a influência de uma área de várzea do ribeirão do Feijão (São Carlos-SP) sobre a ciclagem de nitrogênio e sobre a qualidade da água superficial e subsuperficial, foram analisadas as características físicas e químicas da água e determinadas as taxas de nitrificação e desnitrificação dos sedimentos da várzea. A maior concentração dos compostos nitrogenados foi observada na água de interface subsuperficial da várzea, região mais ativa em termos de fluxos de água e materiais. As taxas de nitrificação variaram de 0,145 a 0,068 &#956mol N-NO3-.g-1.dia-1 e a rota metabólica predominante foi a autotrófica, na qual as bactérias utilizaram amônio como substrato. As taxas de desnitrificação tiveram um valor médio de 0,0081 nmol N2O.g-1.dia-1. Mediante um modelo de estimativa foi calculado que 70% da água que circula no Ribeirão do Feijão provém do lençol que flui sob terras secas e o restante das áreas de várzea da bacia. Foi observado que existe uma considerável redução das concentrações dos compostos nitrogenados, principalmente do amônio, desde as zonas ripárias mais distantes do curso do rio até o canal, passando pela área de várzea. O funcionamento da várzea como sistema de filtro e depuração das águas subsuperficiais que alimentam o rio foi evidenciada pelas características físicas e químicas da água do rio em relação ao uso do solo na bacia. / In order to identify the influence of a floodplain area of the Feijão stream (São Carlos-SP) on surface and subsurface water quality, the physical and chemical characteristics of the water were analyzed and the floodplain sediment\'s nitrification and denitrification rates were determined. The highest concentration of nitrogen compounds was observed at the floodplain\'s subsurface water interface it being the most active region with respect to water and solute flow. Nitrification rates varied between 0.145 and 0.068 &#956mol N-NO3-.g-1.day-1 and the autotrophic metabolic route dominated, in which bacteria use ammonia as a substrate. Denitrification rate average was 0.0081 nmol N2O.g-1.day-1. Through a model it was estimated that 70% of the water flowing in the Feijão stream came from the water table flowing under dry land, the remainder coming from the floodplain of the area. A significant reduction of nitrogen compound concentration, mainly ammonium, was observed between the more distant riparian zones and the river\'s channel going through the floodplain. The floodplain\'s action as a filtering system for the water reaching the river was brought out through the physical and chemical characteristics of the river water relative to land use in the catchment area.

O presente trabalho teve como objetivo fazer um estudo taxonômico da fauna de Plecoptera (Perlidae) da região Sul do Brasil. Nós estudamos material depositado em diferentes coleções, e também novos espécimes coletados ao longo do desenvolvimento deste trabalho. Os adultos foram capturados com puçás na vegetação das margens dos riachos e através de atração luminosa. As ninfas foram coletadas dentro de riachos com o auxílio de redes D. A genitália dos adultos foi diafanizada usando KOH. Como resultado, três novas espécies foram descritas, duas possuem novos registros de ocorrência e quatro tiveram o registro de suas distribuições ampliadas para esta região. / This work aimed to make a taxonomic study of the Plecoptera (Perlidae) fauna from southern Brazil. We studied material deposited in different collections, and also new specimens collected along the development of this work. Adults were captured with D-nets in vegetation from the banks of streams and through light attraction. Nymphs were collected in streams with a D-net. The genitalia of adults were diaphanized by KOH. The adults genitalia was cleared using KOH. As a result, three new species were described, two have new records of occurrence and four had record their distributions expanded to this region.

O zooplâncton possui grande sensibilidade ambiental e responde rapidamente a alterações ambientais com alterações na abundância e riqueza de organismos. Ainda se conhece muito pouco sobre os organismos aquáticos do Cerrado, assim como há poucos estudos sobre o zooplâncton de ambientes lóticos no Brasil. Esse trabalho teve como objetivo caracterizar dois córregos da bacia do Rio Preto, médio São Francisco, pela avaliação de comunidades zooplanctônicas e suas relações com características físicas, químicas e biológicas da água, comparando córregos em áreas de vegetação nativa conservada e sob influência de áreas agrícolas. Foram amostradas duas sub-bacias do Rio Preto, localizadas em área de Cerrado (DF e GO) durante quatro semanas nos períodos seco e chuvoso. Em dois pontos de amostragem por córrego, foram coletadas amostras de zooplâncton e de água. Variáveis físicas e químicas da água e a concentração de a clorofila a foram determinadas. Os dois córregos apresentaram tendência de aumento de íons no período chuvoso. O córrego Estanislau (em área agrícola) apresentou maiores concentrações de nitrato, sódio, cloreto e clorofila a em relação ao córrego Pindaíba (em área de vegetação nativa). O zooplâncton se caracterizou por densidades muito baixas e alta riqueza de espécies, com 63 táxons registrados ao todo. A riqueza de espécies e a densidade de organismos do zooplâncton foram maiores no córrego Pindaíba. O córrego Estanislau possui indícios de estar sofrendo impactos negativos do uso rural de sua bacia. / The zooplankton community has high environmental sensitivity and quickly responds to changes in the environment with changes in its abundance and diversity. Yet, little is known about the aquatic organisms of the Cerrado, as well there had been few investigations on the zooplankton of Brazilian lotic environments. This work had the aims to characterize two streams of Rio Preto watershed, part of the Rio São Francisco watershed; to assess their zooplankton communities and the relationships between chlorophyll a and physical and chemical characteristics of water; and to contrast forested and agricultural streams. Two streams, located in the Cerrado Biome (Federal District and the state of Goiás), were sampled for four weeks in dry and rainy seasons. Zooplankton and water samples were taken in two sampling points per stream. Physical and chemical characteristics of water and chlorophyll a concentration were determined. Ionic concentration was higher on rainy season in both streams. The agricultural stream (Estanislau) had higher concentrations of nitrate, sodium, chloride, and chlorophyll a than the forested stream (Pindaiba). Zooplankton was characterized by very low densities and high species richness, with a total of 63 taxa recorded on both streams. Zooplankton density and species richness were higher in Pindaiba stream. Estanislau stream has signs to be suffering negative impacts from the agricultural land use of its watershed.

Current real-world applications are generating a large volume of datasets that are often continuously updated over time. Detecting outliers on such evolving datasets requires us to continuously update the result. Furthermore, the response time is very important for these time critical applications. This is challenging. First, the algorithm is complex; even mining outliers from a static dataset once is already very expensive. Second, users need to specify input parameters to approach the true outliers. While the number of parameters is large, using a trial and error approach online would be not only impractical and expensive but also tedious for the analysts. Worst yet, since the dataset is changing, the best parameter will need to be updated to respond to user exploration requests. Overall, the large number of parameter settings and evolving datasets make the problem of efficiently mining outliers from dynamic datasets very challenging. Thus, in this thesis, we design an exploration framework for detecting outliers in data streams, called EFO, which enables analysts to continuously explore anomalies in dynamic datasets. EFO is a continuous lightweight preprocessing framework. EFO embraces two optimization principles namely "best life expectancy" and "minimal trial," to compress evolving datasets into a knowledge-rich abstraction of important interrelationships among data. An incremental sorting technique is also used to leverage the almost ordered lists in this framework. Thereafter, the knowledge abstraction generated by EFO not only supports traditional outlier detection requests but also novel outlier exploration operations on evolving datasets. Our experimental study conducted on two real datasets demonstrates that EFO outperforms state-of-the-art technique in terms of CPU processing costs when varying stream volume, velocity and outlier rate.

Realizar o agrupamento de fluxos de dados contínuos e multidimensionais (multidimensional data streams) é uma tarefa dispendiosa, visto que esses tipos de dados podem possuir características peculiares e que precisam ser consideradas, dentre as quais destacam-se: podem ser infinitos, tornando inviável, em muitas aplicações realizar mais de uma leitura dos dados; ponto de dados podem possuir diversas dimensões e a correlação entre as dimensões pode impactar no resultado final da análise e; são capazes de evoluir com o passar do tempo. Portanto, faz-se necessário o desenvolvimento de métodos computacionais adequados a essas características, principalmente nas aplicações em que realizar manualmente tal tarefa seja algo impraticável em razão do volume de dados, por exemplo, na análise e predição do comportamento climático. Nesse contexto, o objetivo desse trabalho de pesquisa foi propor técnicas computacionais, eficientes e eficazes, que contribuíssem para a extração de conhecimento de fluxos de dados com foco na tarefa de agrupamento de fluxos de dados similares. Assim, no escopo deste trabalho, foram desenvolvidos dois métodos para agrupamento de fluxos de dados evolutivos, multidimensionais e potencialmente infinitos, ambos baseados no conceito de dimensão fractal, até então não utilizada nesse contexto na literatura: o eFCDS, acrônimo para evolving Fractal Clustering of Data Streams, e o eFCC, acrônimo para evolving Fractal Clusters Construction. O eFCDS utiliza a dimensão fractal para mensurar a correlação, linear ou não, existente entre as dimensões dos dados de um fluxo de dados multidimensional num período de tempo. Esta medida, calculada para cada fluxo de dados, é utilizada como critério de agrupamento de fluxos de dados com comportamentos similares ao longo do tempo. O eFCC, por outro lado, realiza o agrupamento de fluxos de dados multidimensionais de acordo com dois critérios principais: comportamento ao longo do tempo, considerando a medida de correlação entre as dimensões dos dados de cada fluxo de dados, e a distribuição de dados em cada grupo criado, analisada por meio da dimensão fractal do mesmo. Ambos os métodos possibilitam ainda a identificação de outliers e constroem incrementalmente os grupos ao longo do tempo. Além disso, as soluções propostas para tratamento de correlações em fluxos de dados multidimensionais diferem dos métodos apresentados na literatura da área, que em geral utilizam técnicas de sumarização e identificação de correlações lineares aplicadas apenas à fluxos de dados unidimensionais. O eFCDS e o eFCC foram testados e confrontados com métodos da literatura que também se propõem a agrupar fluxos de dados. Nos experimentos realizados com dados sintéticos e reais, tanto o eFCDS quanto o eFCC obtiveram maior eficiência na construção dos agrupamentos, identificando os fluxos de dados com comportamento semelhante e cujas dimensões se correlacionam de maneira similar. Além disso, o eFCC conseguiu agrupar os fluxos de dados que mantiveram distribuição dos dados semelhante em um período de tempo. Os métodos possuem como uma das aplicações imediatas a extração de padrões de interesse de fluxos de dados proveniente de sensores climáticos, com o objetivo de apoiar pesquisas em Agrometeorologia. / To cluster multidimensional data streams is an expensive task since this kind of data could have some peculiarities characteristics that must be considered, among which: they are potencially infinite, making many reads impossible to perform; data can have many dimensions and the correlation among them could have an affect on the analysis; as the time pass through they are capable of evolving. Therefore, it is necessary the development of appropriate computational methods to these characteristics, especially in the areas where performing such task manually is impractical due to the volume of data, for example, in the analysis and prediction of climate behavior. In that context, the research goal was to propose efficient and effective techniques that clusters multidimensional evolving data streams. Among the applications that handles with that task, we highlight the evolving Fractal Clustering of Data Streams, and the eFCC acronym for evolving Fractal Clusters Construction. The eFCDS calculates the data streams fractal dimension to correlate the dimensions in a non-linear way and to cluster those with the biggest similarity over a period of time, evolving the clusters as new data is read. Through calculating the fractal dimension and then cluster the data streams the eFCDS applies an innovative strategy, distinguishing itself from the state-of-art methods that perform clustering using summaries techniques and linear correlation to build their clusters over unidimensional data streams. The eFCDS also identifies those data streams who showed anomalous behavior in the analyzed time period treating them as outliers. The other method devoleped is called eFCC. It also builds data streams clusters, however, they are built on a two premises basis: the data distribution should be likely the same and second the behavior should be similar in the same time period. To perform that kind of clustering the eFCC calculates the clusters fractal dimension itself and the data streams fractal dimension, following the evolution in the data, relocating the data streams from one group to another when necessary and identifying those that become outlier. Both eFCDS and eFCC were evaluated and confronted with their competitor, that also propose to cluster data streams and not only data points. Through a detailed experimental evaluation using synthetic and real data, both methods have achieved better efficiency on building the groups, better identifying data streams with similar behavior during a period of time and whose dimensions correlated in a similar way, as can be observed in the result chapter 6. Besides that, the eFCC also cluster the data streams which maintained similar data distribution over a period of time. As immediate application the methods developed in this thesis can be used to extract patterns of interest from climate sensors aiming to support researches in agrometeorology.