3 research outputs found
Previsão do deslocamento de tempestades severas : abordagens por aprendizado de máquina
Orientador: Prof. Dr. Paulo Henrique SiqueiraCoorientador: Dr. Cesar Augustus Assis BenetiDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa : Curitiba, 03/08/2018Inclui referências: 97-102Área de concentração: Programação MatemáticaResumo: A previsao de tempestades severas pode auxiliar no processo de tomada de decisao e nas medidas operacionais, bem como ajudar a mitigar e ate mesmo antecipar os danos, permitindo que as acoes possiveis sejam tomadas. Portanto, existe a necessidade de tecnicas confiaveis e rapidas para o monitoramento de tempestades, que consiste em tres processos principais: identificacao de celulas de tempestades ativas, rastreamento, e tambem a previsao de seu deslocamento. O foco deste trabalho e o terceiro passo, com o objetivo de estudar metodos de aprendizado de maquina para previsao de tempestades de curto prazo em celulas identificadas e rastreadas pelo sistema TITAN (Identificacao, Rastreamento, Analise e Previsao de Tempestades) em diferentes estagios. A analise ocorre na regiao sul e sudeste do Brasil e usa dados de radares meteorologicos e descargas eletricas atmosfericas. Devido a natureza dos fenomenos representados neste trabalho, metodos de aprendizado de maquina foram escolhidos porque eles sao capazes de entender e aprender com os recursos e seus relacionamentos. Alem disso, uma vez que o modelo e aprendido pelo metodo escolhido, o processamento das novas entradas ocorre rapidamente. Dois tipos de tecnicas de regressao sao estudadas: Ensemble e Modelo Linear. Foram aplicados os seguintes metodos para a previsao: Bagging, Random Forest, Extra Trees, Theil Sen e Bayesian Ridge. A avaliacao dos resultados e feita comparando-os com a previsao fornecida pelo TITAN para cada celula, uma vez que e uma ferramenta bem estabelecida na area. O melhor desempenho foi obtido com o Algoritmo Random Forest. Seus resultados mostraram-se satisfatorios para a predicao de deslocamento, mostrando-se uma boa alternativa ao software padrao. Alem disso, uma contribuicao mais evidente dos metodos propostos e encontrada para a previsao do tamanho das tempestades. Palavras chaves: Aprendizado de Maquina. Regressao. Previsao de Tempestades. Aprendizado Agrupado. Modelo Linear.Abstract: Thunderstorm forecast can help in the decision-making process and operational measures, as well as help mitigate and even anticipate damage, allowing those decision to be taken. Therefore, there is a need for trustworthy and fast techniques for storms monitoring, consisting of three main processes: identification of active storm cells, tracking, and also their forecast their displacement. The focus of this work is the third step, aiming to study machine learning methods for short-term storm forecast on cells identified and tracked by TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) system in different stages. The analysis takes place in the discussed region and uses data from meteorological radars and atmospheric electrical discharges. Due to the nature of the phenomena represented in this work, machine learning methods are chosen because they are able to better understand and learn from the features and their relationships. Moreover, once the model is learned by the chosen method, the processing of the new entries occurs fastly. Two types of regression techniques are studied: Ensemble and Linear Model. In totally, it was applied the following methods for the forecast: Bagging, Random Forest, Extra Trees, Theil Sen and Bayesian Ridge. The evaluation of the results is done by comparing them with the forecast provided by TITAN for each cell, since that is a well-established tool in the area. The best performance was achieved with the Random Forest Algorithm, and its results proved to be satisfactory for the prediction of displacement, shown to good alternative to the standard software. In addition, a more evident contribution of the proposed methods was found to the prediction of the storms' shape. Keywords: Machine Learning. Regression. Thunderstorm Forecasting. Ensembles. Linear Models
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Formalization and modeling of human values for recipient sentiment prediction
Sentiment analysis is viewed generally as a text classification problem involving the prediction of the semantic orientation of a text. Much of the analysis has focused on the sentiment expressed in the sentence or by the writer but not the sentiment of the recipient. For example, the sentence “Housing costs have dropped significantly” might be assigned a negative classification by a sentiment analysis model, however humans from different works of life might express different sentiments. A landlord will likely express a negative sentiment while a renter might express a positive sentiment. Therefore, traditional sentiment analysis methods fail to capture the human centric aspects that motivate diverse sentiments. Additionally, attempts at predicting recipient sentiment have involved considerable human effort in the form of content analysis and empirical surveys, making the process expensive and time-consuming. Thus, the aim of this research is to develop a method of recipient sentiment analysis that is devoid of human input in the form of annotations or
empirical surveys. The approach taken in this research involves applying a model of human values towards recipient sentiment prediction. The justification for this approach is based on the well-established principle that values influence human behaviour of which sentiment is a form. Therefore, if a persons’ values can be modelled quantitatively, when presented with some text, in theory the sentiment of the recipient can be predicted. This research proposes that the application of values in developing sentences is a generative process, that can be represented as a language model. A mechanism called Feature Switching (FS) that enables the determination of recipient’s sentiment from the value language model is also discussed. The resulting sentiment prediction model has an accuracy in the range of 72.2%-72.5% which is in and about the range of performance of existing systems which make use of content analysis and human annotated data