Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature

Souza, Amaury de; Abreu, Marcel Carvalho; Júnior, José Francisco de Oliveira; Aristone, Flávio; Fernandes, Widinei Alves; Aviv‐Sharon, Elinor; Graf, Renata

doi:10.1590/1678-4324-2022210331

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…The variation in amplitude observed for the wind speed (1.6 m s -1 ) can be attributed to the winter solstice. At this time, penetration of frontal systems (SF) occurred in the studied region (Souza et al, 2022). Shaban, Resen, and Bassil (2020) have collected experimental data on the city of Al-Najaf, Iraq and determined the best theoretical ensemble to be used as Weibull's parameters.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Weibull parameters by different methods for farms

Aristone,

Souza,

Pobocikova

et al. 2023

Acta Sci Technol

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One of the most prevalent clean and sustainable forms of energy produced worldwide is the power created from wind runoff. Wind turbines ought to be installed in areas with favorable circumstances to transform mechanical wind energy into electricity. Finding appropriate ways to predict the energy produced by a wind farm using the Weibull distribution is the main goal of this work. Theoretical techniques have been applied to calculate Weibull selected characteristics using experimental data gathered at the campus of Universidade Federal de Mato Grosso do Sul (UFMS) in Brazil. These data were gathered 10 meters above the surface. The effectiveness of four statistical techniques that are frequently used in the energy industry are compared: the standard energy factor method; the least squares regression method; the moment method; and the mean standard deviation method in estimating Weibull parameters. The root mean square error, Chi-square error, Kolmogorov-Smirnov test, and coefficient of determination are used to contrast the statistical methodologies. The results demonstrated that the least squares regression approach performs less well than other methods. The standard energy factor approach, the moment method, and the mean standard deviation method are the most effective techniques when modifying Weibull distribution curves for the assessment of wind speed data. The data analysis confirms that these three strategies are fully applicable if the wind speed distribution closely matches the Weibull distribution.

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Section: Resultsmentioning

confidence: 99%

Evaluation of Weibull parameters by different methods for farms

Aristone,

Souza,

Pobocikova

et al. 2023

Acta Sci Technol

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“…The Euclidean distance was the measure of dissimilarity and Ward's (Ward, 1963) hierarchical method for clustering, according to Lyra et al (2014). The Elbow method was used to determine the number of groups (Souza et al, 2022). The CA technique was applied in the R environment (R Core Team, 2022), using the following packages: cluster (Maechler et al, 2022), factoextra (Kassambara & Mundt, 2020) and NbClust (Charrad et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Long‐term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability

Abreu,

de Souza Fraga,

Lyra

et al. 2023

Intl Journal of Climatology

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The study of rainfall trends is crucial for food security and water availability in Alagoas state, Northeast of Brazil. In this work, monthly, seasonal and annual rainfall trends have been studied (1960–2016) for homogeneous rainfall regions over the eastern part of the Northeast Brazil (ENEB) and later related to climate variability. Cluster analysis was applied to identify homogeneous rainfall regions while the Mann–Kendall (MK), modified Mann–Kendall (MMK) and Pettitt tests were used in the analysis and identification of trends on a spatial and temporal scale. To relate rainfall and climate variability modes, Spearman's correlation was used in each homogeneous region. The rainfall series provided evidence of a general decrease in rainfall in the rainy period and an increase in the dry period, mainly over the driest region. The break points of time series occurred mostly in periods of great variations in values of modes of climate variability, especially the Monthly Niño3.4 Index and the Southern Oscillation Index (SOI), both having a robust influence across the region. Moreover, the probable rainfall in the time series with trends was different in most months before and after the breakpoint. After the breakpoint, probable rainfall was lower, influenced by the breakpoint year (size of the series before and after the breakpoint), which mainly occurred in the 1980s and 1990s and presented a warm phase and a greater number of El Niño events. The MK and MMK trend tests showed the ability to detect trends, although there is no established standard on which test or version to use due to self‐correlated, nonhomogeneous series with nonrandom or nonindependent data. Rainfall is an important variable for water and food security and in the monitoring of natural disasters. The changes detected in this study can be used as reference information for public policies on water resources and future studies for Alagoas and similar regions.

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“…• Hierarchical Methods [19]: Hierarchical clustering methods iteratively merge individual data points or clusters based on their proximity, generating a hierarchical tree-like structure that reveals the underlying organization of the data [15]. Single Linkage clustering, for instance, forms clusters by pairing the two closest data points, while Average Linkage considers the average distance between all pairs of points in two clusters.…”

Section: Theorical Basicmentioning

confidence: 99%

“…• Non-Hierarchical Methods [19]: Unlike hierarchical methods, non-hierarchical clustering techniques, such as K-Means, require a predefined number of clusters before analysis and partitioning data points accordingly [20]. While non-hierarchical methods offer simplicity and efficiency, hierarchical approaches provide more nuanced insights into the hierarchical structure of the data.…”

Section: Theorical Basicmentioning

confidence: 99%

Mapping dengue vulnerability: spatial cluster analysis reveals patterns in Central Java, Indonesia

Fithriyyah,

Purwaningsih,

Konate

et al. 2023

SITech

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In Indonesia, where the interplay between climate variability and infectious diseases is pronounced, Dengue Fever poses a significant threat, particularly in Central Java, ranking as the province with the third-highest incidence of Dengue cases nationwide. This study adopts a proactive approach, employing cluster analysis techniques—single linkage, average linkage, and Ward’s method—to categorize cities and regencies in Central Java based on their susceptibility to Dengue outbreaks. The comparative analysis, facilitated by standard deviation values, reveals nuanced vulnerability patterns, with the single linkage method presenting the most refined categorization, yielding four distinct vulnerability clusters: very low (0.097), low (0.150), medium (0.205), and high (0.303). Furthermore, spatial analysis utilizing Moran’s Index indicates a positive spatial autocorrelation among Dengue cases (Moran’s I = 0.62, p 0.05), underscoring the spatial homogeneity in case distribution across regions. These findings emphasize the critical need for targeted interventions and evidence-based policymaking to effectively combat Dengue transmission in Central Java and mitigate its public health impact.

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Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature

Cited by 7 publications

References 29 publications

Evaluation of Weibull parameters by different methods for farms

Evaluation of Weibull parameters by different methods for farms

Long‐term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability

Mapping dengue vulnerability: spatial cluster analysis reveals patterns in Central Java, Indonesia

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