http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4307 2025-09-27T12:04:27Z 2025-09-27T12:04:27Z GOMES, Vanessa Karoline Inacio http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/9731 2024-11-12T13:18:35Z 2023-02-27T00:00:00Z

Título: Análise de tendências de índices de mudanças climáticas na precipitação do estado de Pernambuco Autor: GOMES, Vanessa Karoline Inacio Primeiro orientador: SILVA, Antonio Samuel Alves da Abstract: Many studies shown that the increase in the planet’s average temperature causes the hydrological cycle to intensify. This could cause changes in rainfall patterns, such as an increase in the frequency and intensity of extreme events resulting in severe and prolonged droughts in some locations and excessive rainfall in others, which would significantly impact the hydrological availability of a region and the quality of life of its inhabitants. Thus, it is necessary to study the variability and impacts of climate change, enabling a better understanding of the area’s climate in order to adapt and mitigate these climatic conditions. In this context, the present work aimed to analyze the trends and magnitudes of 11 extreme weather indices recommended by the Expert Team on Climate Change Detection Monitoring and Indices for the state of Pernambuco. For this, 809 grid points were used, which contain information regarding daily rainfall from 1961 to 2020 and three non-parametric methods were employed: the Mann-Kendall test for trend detection, sen’s slope for estimating the magnitude of the trend, and the Mann-Whitney-Wilcoxon test to assess whether there are significant differences in the index values for each region of Pernambuco: Zona da Mata, Agreste, and Sertão. In addition, the Inverse Distance Weighting interpolator was used to perform the spatial analysis of precipitation and extreme weather indices. The results indicated an intensification of drought over much of the state, with significant reductions in total annual precipitation, consecutive wet days, and an increase in consecutive dry days. The trends show an acceleration in the desertification process in the Sertão region, which is part of the semi-arid Northeast and already suffers from scarce and poorly distributed rainfall. In relation to the Zona da Mata, the extreme rainfall indices showed significant increases, alerting us to the natural disasters that affect this region. The Agreste region showed similar results to the Zona da Mata, but with less intensity. Based on the results obtained it is possible to infer that the study area tends to become drier, with rainfall increasingly concentrated in shorter periods of time, and the dry periods interspersed between these rainfall events are becoming longer. Instituição: Universidade Federal Rural de Pernambuco Tipo do documento: Dissertação

2023-02-27T00:00:00Z BARROS, Vaniele da Silva http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8768 2022-12-14T20:33:46Z 2021-02-15T00:00:00Z

Título: Análise de séries temporais de focos de calor nos biomas brasileiros utilizando gráfico de recorrência Autor: BARROS, Vaniele da Silva Primeiro orientador: STOSIC, Tatijana Abstract: The scientific interest in studies that use recurrence analysis to approach the transitions between regular and chaotic behaviors, as well as, in the identification of structures of dynamic systems, has spread over the years. Among the main tools of this analysis, we highlight the Recurrence Graph method and the Recurrence Quantification Analysis, which are widely used in the analysis of time series supposedly coming from non-linear and even non-stationary dynamic systems. In particular, this work analyzed or evaluated the large and small scale patterns in the Recurrence Graphs of the series of hot pixels in the Amazon, Cerrado, Caatinga and Atlantic Forest biomes and to obtain the quantitative measures by the method of Recurrence Quantification Analysis. Daily series of hot pixels derived from data provided by National Institute of Space Research – INPE, of the biomes were analyzed for the period from July 4, 2002 to December 31, 2019. In Brazil, the annual average of number of hot pixels, between 2002 and 2019, is approximately 241,866 detections, being these most frequent events between the months of July to October. Considering the absolute values referring to the number of hot pixels in each biome, the highest concentration occurs in the Amazon biome, as it has the largest territorial extension, however, considering the number of hot pixels and the area of each biome, the Cerrado has the highest record per 𝑘𝑚2. The structures present in the Recurrence Graphs of the daily series of hot pixels of the biomes indicate low predictability, while for the series of anomalies, they indicate high predictability, in addition to presenting abrupt changes in the dynamics of the systems in both cases. The values of the various indices that serve as measures of process quantification confirm these results, were obtained through the application of the Recurrence Quantification Analysis method. Instituição: Universidade Federal Rural de Pernambuco Tipo do documento: Dissertação

2021-02-15T00:00:00Z SILVA, Taciana Araújo da http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8767 2022-12-14T19:29:29Z 2020-02-28T00:00:00Z

Título: Estudo do desempenho da combinação de preditores baseados em cópulas e máquinas de vetor de suporte para séries temporais úteis ao desenvolvimento sustentável Autor: SILVA, Taciana Araújo da Primeiro orientador: SILVA, Frank Sinatra Gomes da Abstract: The 21th century has emphasized discussions regarding the sustainable development. In this way, it has been paramount the use of control indicators aimed to monitor, understand, diagnostic, and forecast variables of interest. Thus, time series modelling and forecasting exercises are important tools for decision making with respect to plans and actions regarding the triple bottom line of the sustainable development: environment, economy, and society. The statistical-computing modelling has become attractive by means of the development of technics for analyzing and predicting the behavior of phenomena changing through the time, considering the respective past observations. Among the alternatives, one must highlight the combination of predictors. Usually, they have been more accurate and efficient in statistical terms in comparison with single models. Therefore, approaches like copulas and support vector regression (SVR) seem useful, though classical formalisms, such as simple average, median, and minimal variance combinators, have presented good results. This work aims to compare the performance of the aforementioned combination strategies when forecasting time series useful for the sustainable development. Thus, twelve time series are taken into account, as well as four single modeling and four combination modeling formalisms. For all cases, optimized models are provided via optimization methods. The methodology is based on: i) to build single models for each time series, considering artificial neural nets, autoregressive and moving average, exponential smoothing, and SVR; ii) to build the combinators; and iii) to evaluate the performance of the models, according to a number of stablished quality metrics as well as measures proposed in the work. The results show that copula combinators are more promising for series useful for sustainable development when compared to SVR combinators. Still, even in the face of combined models, the simplest models showed good results. Parsimony and overfitting training may have compromised the performance of individual machine learning models, such as SVR and ANN, thus affecting the performance of the combiners. Instituição: Universidade Federal Rural de Pernambuco Tipo do documento: Dissertação

2020-02-28T00:00:00Z PESSOA, Ruben Vivaldi Silva http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8766 2022-12-14T16:56:54Z 2021-02-19T00:00:00Z

Título: Correlações em séries temporais de preços de frango, soja e milho Autor: PESSOA, Ruben Vivaldi Silva Primeiro orientador: STOSIC, Borko Abstract: With the evolution of the agricultural market, the process of production, export and consumption of food commodities has changed. Given this scenario, food prices can be affected by several factors, such as the energy market through strategies that even divert food cultures for the production of biofuels, causing interest in a better understanding of these relationships. In recent years, many studies were developed on the relationship between the food market and other markets, seeking to explain the link between the prices of different commodities with the prices of agricultural commodities (raw materials). However, Brazil still needs more attention in its food market. Here, the objective was to investigate intrinsic long-term correlations between Brazilian food markets, using Econophysics techniques. The daily series of price returns for chicken, soybeans and corn were analysed for the period from 02/02/2004 to 06/16/2017, obtained by the Center for Advanced Studies in Applied Economics / Escola Superior de Agricultura Luiz de Queiroz / Universidade of São Paulo - CEPEA / ESALQ / USP. Chicken prices depend mainly on the cost of the feed, which includes corn and soy as a source of energy and protein, respectively. The correlations were analysed using methods the Detrended Cross Correlation Analysis (DCCA) and the correlation coefficient associated with it and the recently proposed Detrended Partial Cross Correlation Analysis (DPCCA) useful to quantify the intrinsic cross correlations between two non-stationary time series. The results point to the absence of cross correlations for temporal scales up to 30 days. The intrinsic correlations presented by the DPCCA between chicken and corn price returns are stronger than the correlations between chicken and soybeans, especially from 250-day scales, signalling that the interactions between the markets for these commodities are greater in the long run. Furthermore, it was observed that after the 2008 crisis, the correlations decreased for temporal scales up to 200 days. Instituição: Universidade Federal Rural de Pernambuco Tipo do documento: Dissertação

2021-02-19T00:00:00Z