scholarly journals DISTRIBUIÇÃO ESPAÇO-TEMPORAL DA PLUVIOSIDADE NA ILHA DO MARANHÃO NO ANO DE 2016

2017 ◽  
Vol 3 (8) ◽  
pp. 126
Author(s):  
Juarez Mota Pinheiro
Keyword(s):  
Interpolation Method ◽  
Temporal Distribution ◽  
Kriging Interpolation ◽  
Annual Rainfall ◽  
Industrial District ◽  
The North ◽  
Kriging Interpolation Method ◽  
Annual Total ◽  
The One

SPACE-TEMPORAL DISTRIBUTION OF PLUVIOSITY IN ISLA DE MARANHÃO IN THE YEAR 2016DISTRIBUCIÓN ESPACIO-TEMPORAL DE LA PLUVIOSIDAD EN LA ISLA DEL MARANHÃO EN EL AÑO DE 2016Esta pesquisa identificou espacialmente e temporalmente a dinâmica de frequência acumulada das chuvas na Ilha do Maranhão durante o ano de 2016. Utilizando-se do banco de dados do Centro Nacional de Monitoramento e Alerta de Desastres Naturais - CEMADEN e do Instituto Nacional de Meteorologia - INMET, foram coletados dados pluviométricos de 13 (treze) estações automatizadas distribuídas por toda a ilha. Após processamento dos dados pelo software Golden Surfer 13, utilizando-se do método de interpolação krigagem, foram produzidos cartograficamente mapas representativos da distribuição mensal, sazonal e anual das chuvas. Com o resultado da pesquisa foi possível caracterizar que a maior quantidade de chuvas anuais ocorre no setor sul/sudoeste da Ilha, abrangendo toda a área do distrito industrial de São Luís, e a menor quantidade de chuvas ocorre a norte/nordeste da Ilha, por todo o município da Raposa, apresentando uma diferença de variação de 39,19% do total anual. Outra conclusão que a pesquisa conseguiu identificar é que na distribuição das chuvas no ano de 2016, em uma grande faixa urbana de São Luís, envolvendo os bairros da Cohama, Vinhais, Bequimão, Cohajap e parte do Turu, apresentou-se como os bairros de menor volume de pluviosidade dentro do município de São Luís e a área no entorno do Aeroporto em direção ao distrito industrial é a que apresentou os maiores volumes pluviométricos anuais. Também foi possível inferir que os ventos na ilha, no ano de 2016, possuem uma predominância de direção Leste-Nordeste (ENE) e que esta direção está influenciando na determinação de sua espacialidade pluviométrica.Palavras chave: Ilha do Maranhão; Pluviosidade; Distribuição Espaço-temporal.ABSTRACTThis research identified spatially and temporally how the cumulative frequency of rainfall on the island of Maranhão behaves throughout 2016. Using the database of National Center for Monitoring and Alert of Natural Disasters - CEMADEN and National Institute of Meteorology - INMET, rainfall data were collected from 13 (thirteen) automated stations distributed throughout the island. After data processing by the software Golden Surfer 13, Using the kriging interpolation method, representative maps of the monthly, seasonal and annual rainfall distribution were mapped. With the result of the research, it was possible to characterize that the largest amount of annual rainfall occurs in the south / southwest of the island, covering the whole area of the industrial district of São Luís, and the least rainfall occurs in the north / northeast of the island, the entire municipality of Raposa, presenting a difference of 39.19% of the annual total. Another conclusion that the research was able to identify is that in the distribution of rain in the year 2016, that in a large urban area of São Luís involving the neighborhoods of Cohama, Vinhais, Bequimão, Cohajap and part of Turu presented themselves as the neighborhoods with the lowest volume of rainfall in the municipality of São Luís and The area around the airport towards the industrial district is the one with the highest annual rainfall volumes. It was also possible to infer that the island's winds, in the year 2016, have a predominance of East-Northeast direction (ENE) and that this direction is influencing the determination of its pluviometric spatiality.Keywords: Maranhão Island; Rainfall; Spatial Distribution.RESUMENEsta investigación identificó espacialmente y temporalmente la dinámica de frecuencia acumulada de las lluvias en la Isla de Maranhão durante el año 2016. Utilizando la base de datos del Centro Nacional de Monitoreo y Alerta de Desastres Naturales - CEMADEN y del Instituto Nacional de Meteorología - INMET Se recogieron datos pluviométricos de 13 (trece) estaciones automatizadas distribuidas por toda la isla. Después del procesamiento de los datos por el software Golden Surfer 13, utilizando el método de interpolación de riego, se produjeron cartográficamente mapas representativos de la distribución mensual, estacional y anual de las lluvias. Con el resultado de la investigación fue posible caracterizar que la mayor cantidad de lluvias anuales ocurre en el sector sur / suroeste de la Isla, abarcando toda el área del distrito industrial de São Luís, y la menor cantidad de lluvias ocurre al norte / noreste de la Isla, por Todo el municipio de Raposa, presentando una diferencia de variación del 39,19% del total anual. Otra conclusión que la investigación logró identificar es que en la distribución de las lluvias en el año 2016, en una gran franja urbana de São Luís, envolviendo los barrios de Cohama, Viñedos, Bequimão, Cohajap y parte del Turu, se presentaron como los barrios de menor volumen de pluviosidad dentro del municipio de São Luís y el área en el entorno del Aeropuerto hacia el distrito industrial es la que presentó los mayores volúmenes pluviométricos anuales. También fue posible inferir que los vientos en la isla, en el año 2016, poseen una predominancia de dirección Este-Nordeste (ENE) y que esta dirección está influenciando en la determinación de su espacialidad pluviométrica.Palabras clave: Isla de Maranhão; Pluviosidad; Distribución Espacio-temporal.

scholarly journals Air Quality Measurement Using an IoT Network: a Case Study

Ingeniería ◽  
2022 ◽  
Vol 26 (3) ◽  
pp. 401-418
Author(s):  
Hernán Paz Penagos ◽  
Andrés Alejandro Moreno Sánchez ◽  
José Noé Poveda Zafra
Keyword(s):  
Air Quality ◽  
Interpolation Method ◽  
Mobile Station ◽  
Quality Measurement ◽  
Monitoring Network ◽  
Kriging Interpolation ◽  
University Campus ◽  
The North ◽  
Kriging Interpolation Method ◽  
The University

Context: The evaluation of air quality in Colombia is localized; it does not go beyond determining whether the level of the polluting gas at a specific point of the monitoring network has exceeded a threshold, according to a norm or standard, in order to trigger an alarm. It is not committed to objectives as important as the real-time identification of the dispersion dynamics of polluting gases in an area, or the prediction of the newly affected population. From this perspective, the presence of polluting gases was evaluated on the university campus of Escuela Colombiana de Ingeniería Julio Garavito, located north of the city of Bogotá, and the affected population was estimated for the month of October, 2019, using the Kriging geostatistical technique. Method: This study is part of the design and construction of an auxiliary mobile station that monitors and reports complementary information (CO and SO2 gases) to that provided by the Guaymaral meteorological station, located in the north of Bogotá. This information is transmitted through an IoT network to a server, where a database is created which stores the information on polluting gases reported by the 14 stations of the Bogotá air quality monitoring network, the information sent by the auxiliary station, and the statistical information of the population present on the university campus. Pollutant gas data and population information recorded from October 1st to 31st, 2019, are the input for data analysis using the Kriging interpolation method and predicting the affected population on said campus. Results: There is a particulate matter concentration of 29 µg/m3 of PM10 in the coliseum and 12,6 µg/m3 of PM2,5 in building G, in addition to 9,8 ppb of O3 in building I, 14,9 ppb of NO2 in that same building, 0,79 ppb of CO in building C, and 0,65 ppb of SO2 also in building C, thus allowing to infer, according to the Bogotá air quality index, a favorable air quality for a population of 2.131 people who visited the campus university during the aforementioned period. Conclusions: The correct integration of the data in the web server and their analysis, carried out in the R language, allowed determining the approximate indicators of the polluting factors around Escuela Colombiana de Ingeniería Julio Garavito. Additionally, to determine the affected population, these indicators were correlated with the information on the registered population that entered the campus during the period under study. Based on the results obtained, it was concluded that the air quality on the campus of Escuela Colombiana de Ingeniería Julio Garavito is favorable, and that 2.131 people benefited daily from these conditions.

Numerical Investigation of the Identifiability of Elastomer Mechanical Properties by Nano-Indentation and Shape-Manifold Approach

2021 ◽  
Author(s):  
Oumaima Ezzaamari ◽  
Guénhaël Le Quilliec ◽  
Florian Lacroix ◽  
Stéphane Méo
Keyword(s):  
Numerical Study ◽  
Interpolation Method ◽  
Structural Characteristics ◽  
Numerical Approach ◽  
Kriging Interpolation ◽  
Nano Indentation ◽  
Kriging Interpolation Method ◽  
Elastomeric Materials ◽  
Hyper Elastic ◽  
Characterization Test

ABSTRACT Various research is covering instrumented nano-indentation in the literature. However, studies on this characterization test remain limited when it comes to the local mechanical behavior of elastomeric materials. The application of nano-indentation on these materials is a difficult task given their complex mechanical and structural characteristics. We try to overcome these experimental limitations and find an effective numerical approach for local mechanical characterization of hyper-elastic materials. For such needs, we carried out a numerical study based on model reduction and shape manifold approach to investigate the parameters identification of different hyper-elastic constitutive laws by using instrumented indentation. Similarly, we studied the influence of the indenter geometry, the friction coefficient variation, and finally the indented material height effect. To this end, we constructed a reduced order model through a design of experiments by proper orthogonal decomposition combined with the kriging interpolation method.

scholarly journals Estimation of Ice Thickness and the Features of Subglacial Media Detected by Ground Penetrating Radar at the Baishui River Glacier No. 1 in Mt. Yulong, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4105
Author(s):  
Jing Liu ◽  
Shijin Wang ◽  
Yuanqing He ◽  
Yuqiang Li ◽  
Yuzhe Wang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Ice Thickness ◽  
Grid Data ◽  
Differential Gps ◽  
Landsat Images ◽  
Kriging Interpolation Method ◽  
Bedrock Surface ◽  
Ground Penetrating

Using ground-penetrating radar (GPR), we measured and estimated the ice thickness of the Baishui River Glacier No. 1 of Yulong Snow Mountain. According to the position of the reflected media from the GPR image, combined with the radar waveform amplitude and polarity change information, the ice thickness and the changing medium position at the bottom of this temperate glacier were identified. Water paths were found in the measured ice, including ice caves and crevasses. A debris-rich ice layer was found at the bottom of the glacier, which produces strong abrasion and ploughing action at the bedrock surface. This results in the formation of different detrital layers stagnated at the ice-bedrock interface and numerous crevasses on the bedrock surface. Based on the obtained ice thickness and differential GPS data, combined with Landsat images, the kriging interpolation method was used to obtain grid data. The average ice thickness was 52.48 m and between 4740 and 4890 m above sea level, with a maximum depth of 92.83 m. The bedrock topography map of this area was drawn using digital elevation model from the Shuttle Radar Topography Mission. The central part of the glacier was characterized by small ice basins with distributed ice steps and ice ridges at the upper and lower parts.

Numerical Modeling of Fracture Process Using Element-Free Method

2013 ◽  
Vol 427-429 ◽  
pp. 146-149
Author(s):  
Cheng Fan
Keyword(s):  
Interpolation Method ◽  
Kriging Interpolation ◽  
Computational Accuracy ◽  
Simple Method ◽  
Solution Domain ◽  
Kriging Interpolation Method ◽  
Finite Covers ◽  
Crack Problems ◽  
Cover Systems ◽  
Element Free

A new element-free formulation of Kriging interpolation procedure based on finite covers technique and Kriging interpolation method which integrates the flexibilities of the manifold method in dealing with discontinuity and the element-free features of the moving Kriging interpolation. Two cover systems are employed in this method. Mathematical cover of the solution domain under consideration are used to construct shape function and physical cover is used to reproduce the geometry of the solution domain. The mathematical covers can take any types of shape and is much easily formed compared with those in the conventional MM. The presented method can overcome some difficulties in conventional element-free Galerkin methods in treating discontinuous crack problems. The fundamental theory of this procedure is illustrated and numerical analyses of examples show that the proposed procedure is an effective and simple method with higher computational accuracy.

Integration of statistical and spatial methods for distributing precipitation in tropical areas

2012 ◽  
Vol 44 (6) ◽  
pp. 982-994 ◽  
Author(s):  
Mandana Abedini ◽  
Md Azlin Md Said ◽  
Fauziah Ahmad
Keyword(s):  
Multivariate Regression ◽  
Interpolation Method ◽  
Rainfall Data ◽  
Environmental Research ◽  
Kriging Interpolation ◽  
Slope Aspect ◽  
Annual Average ◽  
Rain Gauges ◽  
Kriging Interpolation Method ◽  
Topographical Factors

The high spatial resolution of precipitation distribution is a major concern for experts in environmental research and planning. This paper establishes a combination of multivariate regression algorithm and spatial analysis to predict distribution of precipitation, considering the four topographical factors of altitude, slope, aspect and location. Annual average and seasonal rainfall data were collected in nine rain gauges in Ulu Kinta Catchment in East Malaysia from 1974 to 2010. To examine records and fill gaps from long-term rain gauges, homogeneity analysis was performed using the double-mass curve method. Estimated missing rainfall data were also tested using index gauges from network rainfall stations. Multivariate regression analysis was conducted to propose an empirical equation for the study area. Topographical factors were considered from a 90 m resolution digital elevation model. The multivariate regression model was found to clarify 74% of spatial variability of precipitation on annual average and 78% during wet season. However, the correlation coefficient for the dry season decreased sharply to 63%. By using the kriging interpolation method, the estimated annual average improved to 78.4%; the average improved to 65.2 and 80.3% in the dry and wet seasons, respectively. This confirms the efficiency and significance of the model and its potential for use in other tropical catchments.

The Flow Units Study Based on Reservoir Architecture

2011 ◽  
Vol 361-363 ◽  
pp. 66-69
Author(s):  
Cong Jun Feng ◽  
Zhi Dong Bao ◽  
Ying Wang
Keyword(s):  
Oil Recovery ◽  
Interpolation Method ◽  
Flow Unit ◽  
Kriging Interpolation ◽  
Empirical Formulas ◽  
Reservoir Architecture ◽  
Flow Units ◽  
Kriging Interpolation Method ◽  
The Relationship

In the case of Fourth Member of Quantou Formation (K1q4) in Well X5-16 of Fuyu Oilfield, it integrates the theory of reservoir architecture and methodology for flow-unit analysis to characterize the architectural units and their permeable features in reservoirs. As the research found, point bars are very developed in low-sinuosity meandering distributary channels. Therefore, parameter modeling for reservoirs, confined by reservoir architecture is firstly constructed from empirical formulas and integrating the data from closely-spaced wells in dense pattern area. At this basis, clustering analysis with optimized reservoir parameters help demarcate the classification of flow units and further the Kriging interpolation method is introduced for interwell flow unit prediction. Besides, the study also illustrates the relationship between the lateral accretion and the flow unit. Finally, the research achievements were confirmed by successfully matching the production data, so as to predict how the remaining oil distributes, or to adjust the development plan, as well as enhance the oil recovery.

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