A análise de dados contendo informações sequenciais é um problema de crescente interesse devido à grande quantidade de informação que é gerada, entre outros, em processos de monitoramento. As séries temporais são um dos tipos mais comuns de dados sequenciais e consistem em observações ao longo do tempo. O algoritmo k-Nearest Neighbor - Time Series Prediction kNN-TSP é um método de previsão de dados temporais. A principal vantagem do algoritmo é a sua simplicidade, e a sua aplicabilidade na análise de séries temporais não-lineares e na previsão de comportamentos sazonais. Entretanto, ainda que ele frequentemente encontre as melhores previsões para séries temporais parcialmente periódicas, várias questões relacionadas com a determinação de seus parâmetros continuam em aberto. Este trabalho, foca-se em dois desses parâmetros, relacionados com a seleção de vizinhos mais próximos e a função de previsão. Para isso, é proposta uma abordagem simples para selecionar vizinhos mais próximos que considera a similaridade e a distância temporal de modo a selecionar os padrões mais similares e mais recentes. Também é proposta uma função de previsão que tem a propriedade de manter bom desempenho na presença de padrões em níveis diferentes da série temporal. Esses parâmetros foram avaliados empiricamente utilizando várias séries temporais, inclusive caóticas, bem como séries temporais reais referentes a variáveis ambientais do reservatório de Itaipu, disponibilizadas pela Itaipu Binacional. Três variáveis limnológicas fortemente correlacionadas são consideradas nos experimentos de previsão: temperatura da água, temperatura do ar e oxigênio dissolvido. Uma análise de correlação é realizada para verificar se os dados previstos mantem a correlação das variáveis. Os resultados mostram que, o critério de seleção de vizinhos próximos e a função de previsão, propostos neste trabalho, são promissores / Treating data that contains sequential information is an important problem that arises during the data mining process. Time series constitute a popular class of sequential data, where records are indexed by time. The k-Nearest Neighbor - Time Series Prediction kNN-TSP method is an approximator for time series prediction problems. The main advantage of this approximator is its simplicity, and is often used in nonlinear time series analysis for prediction of seasonal time series. Although kNN-TSP often finds the best fit for nearly periodic time series forecasting, some problems related to how to determine its parameters still remain. In this work, we focus in two of these parameters: the determination of the nearest neighbours and the prediction function. To this end, we propose a simple approach to select the nearest neighbours, where time is indirectly taken into account by the similarity measure, and a prediction function which is not disturbed in the presence of patterns at different levels of the time series. Both parameters were empirically evaluated on several artificial time series, including chaotic time series, as well as on a real time series related to several environmental variables from the Itaipu reservoir, made available by Itaipu Binacional. Three of the most correlated limnological variables were considered in the experiments carried out on the real time series: water temperature, air temperature and dissolved oxygen. Analyses of correlation were also accomplished to verify if the predicted variables values maintain similar correlation as the original ones. Results show that both proposals, the one related to the determination of the nearest neighbours as well as the one related to the prediction function, are promising

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No. of bitstreams: 1 FERREIRA_Larissa_2015.pdf: 46221411 bytes, checksum: ae8a0358ebf5e33024f58e5c75dae037 (MD5) Previous issue date: 2015-11-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Some conservation actions require the knowledge of the geographical distribution of species, however, this knowledge is far from being achieved for most species. The species distribution models (SDMs) have proved a useful tool to predict the distribution of species and guide field research to find new records. The SDMs using field data occurrence and environmental variables to indicate potential sites for the occurrence of a species. The quality and quantity of the data used are important to a successful result prediction models and application to conservation. The choice of environmental data and the algorithm and their settings are important for the development of models, the choice of these variables have directly influences to the quality of the models. Another very important step in modeling is the quality assessment and validation of the model, is that it may decrease the risk of accepting as true models with gross errors. The objective of this study is to evaluate the applicability of models generated by MaxEnt to find new populations of plants considering different data configurations used. For this, considering that the field validation is the most appropriate in the literature, but the most costly, the first chapter proposes a validation methodology of the models as easy application field. The methodology was able to find new records in the field, therefore, indicated for the validation of models. In the second chapter, knowing of the existence of a wide variety of variables that influence the performance of the models, the aim was to test the influence of the sample size, the spatial bias, the set of climate data and settings available for the MaxEnt algorithm in the areas of prediction potential distribution. The results demonstrated that the use of sampling and climate data restricted to the limit of the study area and also the use of soil data generate more accurate models. / Algumas ações conservacionistas necessitam do conhecimento da distribuição geográfica das espécies, porém, esse conhecimento está longe de ser alcançado para a maioria das espécies. Os modelos de distribuição de espécies (MDEs) têm se mostrado uma ferramenta útil para prever a distribuição das espécies e guiar pesquisas de campo para encontrar novos registros. Os MDEs utilizam dados de ocorrência e variáveis ambientais para indicar locais potenciais para a ocorrência de uma espécie. A precisão e quantidade dos dados utilizados são importantes para um bom resultado de predição dos modelos e aplicação à conservação. A escolha dos dados ambientais e do algoritmo e suas configurações são essenciais para o desenvolvimento dos modelos, pois influenciam diretamente na qualidade dos mesmos. Outra etapa bastante importante na modelagem é a validação do modelo, pois é ela que diminui o risco de aceitar como verdadeiros modelos que possuem erros grosseiros. O objetivo principal deste estudo é avaliar a aplicabilidade de modelos gerados pelo MaxEnt para encontrar populações de plantas, considerando diferentes configurações dos dados utilizados. Para isso o primeiro capítulo propõe uma metodologia de validação dos modelos em campo de fácil aplicação, uma vez que a validação em campo é a mais indicada pela literatura. A metodologia proposta no capítulo um é uma adaptação ao método de “caminhamento” ou método expedito de levantamento e caracterização da vegetação. A metodologia proposta foi eficaz para a localização das espécies em campo e mostrou que a caracterização da vegetação é uma etapa importante para a interpretação dos resultados, uma vez que explicou a ausência de duas espécies em áreas onde o modelo havia previsto presença. Apresenta como principal desvantagem a necessidade de pessoas experientes para o reconhecimento das espécies de plantas para a sua aplicação de forma agilizada. No segundo capítulo, foi testada a influência da área de amostragem, do conjunto de dados climáticos e das configurações do algoritmo Maxent na predição de áreas potenciais de distribuição. Os resultados obtidos demonstraram que o uso de dados amostrais e climáticos restritos aos limites da área de interesse para a busca das espécies e a inclusão de dados de solo geram modelos mais acurados. Mostrou também que as diferentes configurações do Maxent geraram modelos muito similares.

A análise de dados contendo informações sequenciais é um problema de crescente interesse devido à grande quantidade de informação que é gerada, entre outros, em processos de monitoramento. As séries temporais são um dos tipos mais comuns de dados sequenciais e consistem em observações ao longo do tempo. O algoritmo k-Nearest Neighbor - Time Series Prediction kNN-TSP é um método de previsão de dados temporais. A principal vantagem do algoritmo é a sua simplicidade, e a sua aplicabilidade na análise de séries temporais não-lineares e na previsão de comportamentos sazonais. Entretanto, ainda que ele frequentemente encontre as melhores previsões para séries temporais parcialmente periódicas, várias questões relacionadas com a determinação de seus parâmetros continuam em aberto. Este trabalho, foca-se em dois desses parâmetros, relacionados com a seleção de vizinhos mais próximos e a função de previsão. Para isso, é proposta uma abordagem simples para selecionar vizinhos mais próximos que considera a similaridade e a distância temporal de modo a selecionar os padrões mais similares e mais recentes. Também é proposta uma função de previsão que tem a propriedade de manter bom desempenho na presença de padrões em níveis diferentes da série temporal. Esses parâmetros foram avaliados empiricamente utilizando várias séries temporais, inclusive caóticas, bem como séries temporais reais referentes a variáveis ambientais do reservatório de Itaipu, disponibilizadas pela Itaipu Binacional. Três variáveis limnológicas fortemente correlacionadas são consideradas nos experimentos de previsão: temperatura da água, temperatura do ar e oxigênio dissolvido. Uma análise de correlação é realizada para verificar se os dados previstos mantem a correlação das variáveis. Os resultados mostram que, o critério de seleção de vizinhos próximos e a função de previsão, propostos neste trabalho, são promissores / Treating data that contains sequential information is an important problem that arises during the data mining process. Time series constitute a popular class of sequential data, where records are indexed by time. The k-Nearest Neighbor - Time Series Prediction kNN-TSP method is an approximator for time series prediction problems. The main advantage of this approximator is its simplicity, and is often used in nonlinear time series analysis for prediction of seasonal time series. Although kNN-TSP often finds the best fit for nearly periodic time series forecasting, some problems related to how to determine its parameters still remain. In this work, we focus in two of these parameters: the determination of the nearest neighbours and the prediction function. To this end, we propose a simple approach to select the nearest neighbours, where time is indirectly taken into account by the similarity measure, and a prediction function which is not disturbed in the presence of patterns at different levels of the time series. Both parameters were empirically evaluated on several artificial time series, including chaotic time series, as well as on a real time series related to several environmental variables from the Itaipu reservoir, made available by Itaipu Binacional. Three of the most correlated limnological variables were considered in the experiments carried out on the real time series: water temperature, air temperature and dissolved oxygen. Analyses of correlation were also accomplished to verify if the predicted variables values maintain similar correlation as the original ones. Results show that both proposals, the one related to the determination of the nearest neighbours as well as the one related to the prediction function, are promising

Les données acquises lors des surveillances environnementales peuvent être sujettes à différents types d'anomalies (i.e., données incomplètes, inconsistantes, inexactes ou aberrantes). Ces anomalies qui entachent la qualité des données environnementales peuvent avoir de graves conséquences lors de l'interprétation des résultats et l’évaluation des écosystèmes. Le choix des méthodes de prétraitement des données est alors crucial pour la validité des résultats d'analyses statistiques et il est assez mal défini. Pour étudier cette question, la thèse s'est concentrée sur l’acquisition des données et sur les protocoles de prétraitement des données afin de garantir la validité des résultats d'analyse des données, notamment dans le but de recommander la séquence de tâches de prétraitement la plus adaptée. Nous proposons de maîtriser l'intégralité du processus de production des données, de leur collecte sur le terrain et à leur analyse, et dans le cas de l'évaluation de la qualité de l'eau, il s’agit des étapes d'analyse chimique et hydrobiologique des échantillons produisant ainsi les données qui ont été par la suite analysées par un ensemble de méthodes statistiques et de fouille de données. En particulier, les contributions multidisciplinaires de la thèse sont : (1) en chimie de l'eau: une procédure méthodologique permettant de déterminer les quantités de pesticides organochlorés dans des échantillons d'eau collectés sur le terrain en utilisant les techniques SPE–GC-ECD (Solid Phase Extraction - Gas Chromatography - Electron Capture Detector) ; (2) en hydrobiologie : une procédure méthodologique pour évaluer la qualité de l’eau dans quatre rivières Mexicaines en utilisant des indicateurs biologiques basés sur des macroinvertébrés ; (3) en science des données : une méthode pour évaluer et guider le choix des procédures de prétraitement des données produites lors des deux précédentes étapes ainsi que leur analyse ; et enfin, (4) le développement d’un environnement analytique intégré sous la forme d’une application développée en R pour l’analyse statistique des données environnementales en général et l’analyse de la qualité de l’eau en particulier. Enfin, nous avons appliqué nos propositions sur le cas spécifique de l’évaluation de la qualité de l’eau des rivières Mexicaines Tula, Tamazula, Humaya et Culiacan dans le cadre de cette thèse qui a été menée en partie au Mexique et en France. / Data obtained from environmental surveys may be prone to have different anomalies (i.e., incomplete, inconsistent, inaccurate or outlying data). These anomalies affect the quality of environmental data and can have considerable consequences when assessing environmental ecosystems. Selection of data preprocessing procedures is crucial to validate the results of statistical analysis however, such selection is badly defined. To address this question, the thesis focused on data acquisition and data preprocessing protocols in order to ensure the validity of the results of data analysis mainly, to recommend the most suitable sequence of preprocessing tasks. We propose to control every step in the data production process, from their collection on the field to their analysis. In the case of water quality assessment, it comes to the steps of chemical and hydrobiological analysis of samples producing data that were subsequently analyzed by a set of statistical and data mining methods. The multidisciplinary contributions of the thesis are: (1) in environmental chemistry: a methodological procedure to determine the content of organochlorine pesticides in water samples using the SPE-GC-ECD (Solid Phase Extraction – Gas Chromatography – Electron Capture Detector) techniques; (2) in hydrobiology: a methodological procedure to assess the quality of water on four Mexican rivers using macroinvertebrates-based biological indices; (3) in data sciences: a method to assess and guide on the selection of preprocessing procedures for data produced from the two previous steps as well as their analysis; and (4) the development of a fully integrated analytics environment in R for statistical analysis of environmental data in general, and for water quality data analytics, in particular. Finally, within the context of this thesis that was developed between Mexico and France, we have applied our methodological approaches on the specific case of water quality assessment of the Mexican rivers Tula, Tamazula, Humaya and Culiacan.

Purpose: This thesis aims to describe how to design and implement an IoT-Based digital twin framework for environmental monitoring in the indoor environment.  To fulfill the purpose of the study, the following research question is answered. How to create a digital twin solution utilizing AWS to establish interaction and convergence between the physical environment in a classroom and the virtual environment?  Method: As a research method, the research has conducted design science research (DSR). DSR is a new method, and it is an effective tool for enhancing engineering education research methods.  Results: The study describes in detail the steps required to create the framework. The framework enabled interaction and convergence between the physical and virtual environments in a particular location.  Implications: The research contributes to broadening the knowledge on using the Internet of things (IoT), digital twin (DT), and Amazon web services (AWS). The study provides future research with reference data and a framework to build upon.  Research Limitation: Due to time constraints, the study's scope and limitations are limited to the technologies that the participating company, Knowit, provides. Knowit AB is a Swedish IT consulting company that supports companies and organizations with services in digital transformation and system development. The study aims to create an AWS-based IoT framework, not improve the digital twin concept. The framework was implemented at Jönköping University. This work is also limited to temperature and light intensity as environmental parameters.

Η μελέτη και παρακολούθηση του Περιβάλλοντος με σκοπό την κατανόηση και προστασία του, προϋποθέτουν τη δυνατότητα καταγραφής, επεξεργασίας και αποθήκευσης πλήθους μετρήσεων καθώς και τη χρήση μαθηματικών μοντέλων. Για τον σκοπό αυτό έχουν αναπτυχθεί από διεθνείς οργανισμούς (Κυβερνητικούς και Μη) ειδικές Περιβαλλοντικές Βάσεις Δεδομένων που ανάλογα με τα ιδιαίτερα χαρακτηριστικά τους καταγράφουν τις τιμές διαφόρων περιβαλλοντικών μεταβλητών. Για την αποτελεσματικότερη χρήση των ΠΒΔ και την συγκριτική αξιολόγησή τους αναπτύχθηκε ένας Τυπολογικός Πίνακας βασισμένος σε δώδεκα (12) παραμέτρους με σκοπό την κατάταξη των κυριότερων ΠΒΔ σε αυτόν. Ως εκ τούτου, επιλέχθηκαν δεκαεπτά (17) ΠΒΔ, που υποστηρίζονται είτε από διεθνείς ή από ελληνικούς φορείς, και περιλαμβάνουν την περιοχή της ανατολικής Μεσογείου. Η ανάπτυξη του Τυπολογικού Πίνακα επιτρέπει την κατάταξη οποιασδήποτε άλλης ΠΒΔ σε αυτόν ενώ αποτελεί και ένα χρήσιμο εργαλείο στο σχεδιασμό μελλοντικών τέτοιων Βάσεων. Κάνοντας χρήση των φίλτρων του Πίνακα επιλέχθηκαν τρείς (3) μελέτες περιπτώσεων για την συγκριτική αξιολόγηση των αποτελεσμάτων δύο (2) ΠΒΔ που πληρούν όμοια κριτήρια. Στα συμπεράσματα της εργασίας περιλαμβάνεται η διαπίστωση ότι παρά την ετερογένεια της πληροφορίας που παρατηρείται μεταξύ των ΠΒΔ, υπάρχουν οι προϋποθέσεις να εξαχθούν ασφαλείς και χρήσιμες παρατηρήσεις. / Over the past decades, an effort has been made by several Governments and Non-Governmental Organizations to develop and support Environmental Data Bases (EDBs) containing specific environmental parameters and characteristics. The aim was to study and monitor environmental variables in order to better understand and predict their structures and trends and thus protect the global ecosystem. In order to achieve an effective way of using the various Environmental DBs a typological Table was developed in accordance to specific parameters. Priority was given to those EDBs that focus on the eastern part of Mediterranean. This Typology allows every EDB to be classified according to specific spatio-temporal scales and parameters and simultaneously can offer a better approach for designing other EDBs in the future. Three (3) case studies were selected based on the Typological Table for comparative assessment of the EDBs. In general the EDB are heterogeneous and do not follow the same data structure. However under some circumstances, interesting information can be extracted that expands and completes our knowledge about the Environment.

The rise of crowdsourcing practices has revolutionized the way in which organizational tasks can be performed. One application of crowdsourcing that has been receiving growing attention over the past decade is using the crowd to collect environmental data that is needed to monitor, manage and predict environmental events. Most projects are still in their initial stages and since these projects often require high levels of citizen participation, there is a need to better understand the motivations for individuals to(dis)engage in these crowdsourcing projects, especially when long-term participation is required. Therefore, the aim of this thesis is to explore the motivations of individuals that participated in a crowdsourcing project for environmental data collection. Building on Self-Determination Theory, Social Exchange Theory and Social Identity theory, a case was studied of an organization that successfully motivated citizens from all over the world to use their drones to collect geographic climate data. In total, ten interviews were conducted with the drone-operators. The findings suggest that participants felt intrinsically motivated to engage because they wanted to do good, cared about climate change and were passionate about flying drones. Additionally, they experienced extrinsic motivations such as the possibility of gaining future job offers, recognition, and experience. Moreover, since participation was deemed relatively easy and straightforward, costs of participation were low and outweighed by the benefits. Finally, feeling part of a wider community made the participants more motivated to engage. Using crowdsourcing to collect environmental data on a wider spatiotemporal scale can help guide policy-making that mitigates or manages the impacts of climate change. Future projects would benefit from more and diversified research that support the design of long-term participation schemes, as the success of the projects depends on the level of citizen engagement.

Species distribution maps can provide important information to focus conservation efforts and enable spatial management of human activities. Two sympatric marine predators, grey seals (Halichoerus grypus) and harbour seals (Phoca vitulina), have overlapping ranges but contrasting population dynamics around the UK; whilst grey seals have generally increased, harbour seals have shown significant regional declines. A robust analytical methodology was developed to produce maps of grey and harbour seal usage estimates with corresponding uncertainty, and scales of spatial partitioning between the species were found. Throughout their range, both grey and harbour seals spend the majority of their time within 50 km of the coast. The scalability of the analytical approach was enhanced and environmental information to enable spatial predictions was included. The resultant maps have been applied to inform consent and licensing of marine renewable developments of wind farms and tidal turbines. For harbour seals around Orkney, northern Scotland, distance from haul out, proportion of sand in seabed sediment, and annual mean power were important predictors of space-use. Utilising seal usage maps, a framework was produced to allow shipping noise, an important marine anthropogenic stressor, to be explicitly incorporated into spatial planning. Potentially sensitive areas were identified through quantifying risk of exposure of shipping traffic to marine species. Individual noise exposure was predicted with associated uncertainty in an area with varying rates of co-occurrence. Across the UK, spatial overlap was highest within 50 km of the coast, close to seal haul outs. Areas identified with high risk of exposure included 11 Special Areas of Conservation (from a possible 25). Risk to harbour seal populations was highest, affecting half of all SACs associated with the species. For 20 of 28 animals in the acoustic exposure study, 95% CI for M-weighted cumulative Sound Exposure Levels had upper bounds above levels known to induce Temporary Threshold Shift. Predictions of broadband received sound pressure levels were underestimated on average by 0.7 dB re 1μPa (± 3.3). An analytical methodology was derived to allow ecological maps to be quantitatively compared. The Structural Similarity (SSIM) index was enhanced to incorporate uncertainty from underlying spatial models, and a software algorithm was developed to correct for internal edge effects so that loss of spatial information from the map comparison was limited. The application of the approach was demonstrated using a case study of sperm whales (Physeter macrocephalus, Linneaus 1758) in the Mediterranean Sea to identify areas where local-scale differences in space-use between groups and singleton whales occurred. SSIM is applicable to a broad range of spatial ecological data, providing a novel tool for map comparison.

Although the physical characteristics of surfing breaks are well described in the literature, there is little specific research on surfing and coastal management. Such research is required because coastal engineering has had significant impacts to surfing breaks, both positive and negative. Strategic planning and environmental impact assessment methods, a central tenet of integrated coastal zone management (ICZM), are recommended by this thesis to maximise surfing amenities. The research reported here identifies key oceanographic considerations required for ICZM around surfing breaks including: surfing wave parameters; surfing break components; relationship between surfer skill, surfing manoeuvre type and wave parameters; wind effects on waves; currents; geomorphic surfing break categorisation; beach-state and morphology; and offshore wave transformations. Key coastal activities that can have impacts to surfing breaks are identified. Environmental data types to consider during coastal studies around surfing breaks are presented and geographic information systems (GIS) are used to manage and interpret such information. To monitor surfing breaks, a shallow water multibeam echo sounding system was utilised and a RTK GPS water level correction and hydrographic GIS methodology developed. Including surfing in coastal management requires coastal engineering solutions that incorporate surfing. As an example, the efficacy of the artificial surfing reef (ASR) at Mount Maunganui, New Zealand, was evaluated. GIS, multibeam echo soundings, oceanographic measurements, photography, and wave modelling were all applied to monitor sea floor morphology around the reef. Results showed that the beach-state has more cellular circulation since the reef was installed, and a groin effect on the offshore bar was caused by the structure within the monitoring period, trapping sediment updrift and eroding sediment downdrift. No identifiable shoreline salient was observed. Landward of the reef, a scour hole ~3 times the surface area of the reef has formed. The current literature on ASRs has primarily focused on reef shape and its role in creating surfing waves. However, this study suggests that impacts to the offshore bar, beach-state, scour hole and surf zone hydrodynamics should all be included in future surfing reef designs. More real world reef studies, including ongoing monitoring of existing surfing reefs are required to validate theoretical concepts in the published literature.