scholarly journals Selection of an Appropriate Interpolation Method for Rainfall Data In Central Nigeria

2015 ◽  
Vol 8 (4) ◽  
pp. 423
Author(s):  
SO Bilewu ◽  
BF Sule
Keyword(s):  
Interpolation Method ◽  
Rainfall Data ◽  
Selection Of

scholarly journals Evaluation of discrepancies in spatial distribution of rainfall erosivity in the Czech Republic caused by different approaches using GIS and geostatistical tools

2017 ◽  
Vol 12 (No. 2) ◽  
pp. 117-127 ◽  
Author(s):  
J. Brychta ◽  
M. Janeček
Keyword(s):  
Spatial Distribution ◽  
Czech Republic ◽  
Temporal Resolution ◽  
Agricultural Land ◽  
Interpolation Method ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Yearly Variation ◽  
The Czech Republic ◽  
R Factor

The study presents all approaches of rainfall erosivity factor (R) computation and estimation used in the Czech Republic (CR). A lot of distortions stem from the difference in erosive rainfall criteria, time period, tipping rain gauges errors, low temporal resolution of rainfall data, the type of interpolation method, and inappropriate covariates. Differences in resulting R values and their spatial distribution caused by the described approaches were analyzed using the geostatistical method of Empirical Bayesian Kriging and the tools of the geographic information system (GIS). Similarity with the highest temporal resolution approach using 1-min rainfall data was analyzed. Different types of covariates were tested for incorporation to the cokriging method. Only longitude exhibits high correlation with R and can be recommended for the CR conditions. By incorporating covariates such as elevation, with no or weak correlation with R, the results can be distorted even by 81%. Because of significant yearly variation of R factor values and not clearly confirmed methodology of R values calculation and their estimation at unmeasured places we recommend the R factor for agricultural land in the Czech Republic R = 40 MJ/ha·cm/h +/– 10% depends on geographic location.

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.

EVALUATION OF SOLAR ENERGY AND SELECTION OF OPTIMAL TILT ANGLE OF SOLAR PANELS FOR CONDITIONS IN SYRIA

2018 ◽  
pp. 12-27
Author(s):  
A. Ramadan ◽  
V. V. Elistratov
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Tilt Angle ◽  
Interpolation Method ◽  
Solar Panels ◽  
Inclined Surfaces ◽  
Distance Weighted ◽  
Inclined Surface ◽  
Inverse Distance ◽  
Selection Of

The article suggests the method for calculating the solar radiation on a horizontal surface of the territory of Syria which has been developed using the weather database of NASA and ArcGIS software to create the atlases of Syria. In order to compute the solar radiation on an inclined surface for Syria, the following steps were taken. Firstly, the method proposed by Liu and Jordan (1962) and developed by Klein (1977) was used and applied at a point with a latitude of 33º and a longitude of 36º in Syria to calculate the total average daily monthly and yearly solar radiation on an inclined surface and its components (direct, diffuse and ground reflected). Secondly, the annual and monthly values of the optimal tilt angle of the solar panels were determined. Thirdly, verification of reliability and accuracy of calculations was carried out. Finally, using the interpolation method (inverse distance weighted IDW) in ArcGIS, the method proposed was applied to 63 points that covered the territory of Syria. Thus, we developed an Atlas of Syria of solar radiation on an inclined surface which characterized by the optimal tilt angles of solar panels and the maximum annual solar radiation on an inclined surface under these angles. Solar Radiation Atlas of Syria shows that the annual optimal tilt angle of the solar panels varies in the range from 23º to 28º and the maximum average annual solar radiation on an inclined surface under these angles varies in the range from 1859 to 2069 kWh/m2·year. In addition, on the basis of the NASA meteorological database, we determined the average total gross (natural) potential of solar energy on optimal inclined surfaces in Syria which is 362.1·103 TWh per year.

scholarly journals Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China

2018 ◽  
Vol 10 (7) ◽  
pp. 2290 ◽  
Author(s):  
Zhongqi Zhang ◽  
Dongsheng Yu ◽  
Xiyang Wang ◽  
Yue Pan ◽  
Guangxing Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Interpolation Method ◽  
Red Soil ◽  
Selection Of

Hardware Implementation Tasks to Configure Regulators

2014 ◽  
Vol 945-949 ◽  
pp. 2611-2616 ◽  
Author(s):  
Valery Goncharov ◽  
Ilya O. Ilyin ◽  
Andrey Kudryavtsev
Keyword(s):  
Mobile Device ◽  
Hardware Implementation ◽  
Interpolation Method ◽  
Real Interpolation ◽  
Mathematical Software ◽  
The Real ◽  
Advantages And Disadvantages ◽  
Software Packages ◽  
Real Interpolation Method ◽  
Selection Of

The authors consider the problem of configuring regulators. This paper describes the selection of instrumental tools to create a mobile device, based on the real interpolation method, enabling to configure regulators without the use of mathematical software packages like Matlab or Mathcad. Also described are advantages and disadvantages of selecting hardware and software part for the mobile devise:

f-x adaptive seismic-trace interpolation

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. V9-V16 ◽  
Author(s):  
Mostafa Naghizadeh ◽  
Mauricio D. Sacchi
Keyword(s):  
Interpolation Method ◽  
Real Data ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Filter Length ◽  
Frequency Space ◽  
Adaptive Prediction ◽  
Two Parameters ◽  
Spatially Variant ◽  
Selection Of

We use exponentially weighted recursive least squares to estimate adaptive prediction filters for frequency-space [Formula: see text] seismic interpolation. Adaptive prediction filters can model signals where the dominant wavenumbers vary in space. This concept leads to an [Formula: see text] interpolation method that does not require windowing strategies for optimal results. In other words, adaptive prediction filters can be used to interpolate waveforms that have spatially variant dips. The interpolation method’s performance depends on two parameters: filter length and forgetting factor. We pay particular attention to selection of the forgetting factor because it controls the algorithm’s adaptability to changes in local dip. Finally, we use synthetic- and real-data examples to illustrate the performance of the proposed adaptive [Formula: see text] interpolation method.

scholarly journals Principles of the tugboats’ surface systematization in the development of theoretical drawing by the interpolation method

2020 ◽  
pp. 55-61
Author(s):  
Svetlana V. Davydova ◽  
Ivan V. Andriyanov
Keyword(s):  
Interpolation Method ◽  
Single System ◽  
Early Design ◽  
Ship Hulls ◽  
Surface Development ◽  
Early Design Stages ◽  
Selection Of

The work is devoted to the issues of the tugboat’s surface development by the interpolation method at the early design stages. The developed theoretical drawing of the tugboat’s hull should correspond as much as possible to the specified parameters, namely, the calculated values ​​of the overall completeness ratio and the relative abscissa of the magnitude center. The principles of the approach to existing ship hulls’ systematization are given and the possibility of their application for developing a theoretical surface with the specified parameters is evaluated. As a result of the analysis, systematization and selection of the most suitable hulls for processing were carried out. The variety of hull shapes has been successfully brought to a single system, which is necessary for interpolation, and consequently for creation of a new hull with the specified parameters. The adopted approach to the ship surface development can be used to automate the process of obtaining it.

scholarly journals Aplikasi ESDA untuk Studi Variabilitas Spasial Hujan Bulanan di Jawa Timur

2011 ◽  
Vol 25 (2) ◽  
pp. 178
Author(s):  
I Indarto
Keyword(s):  
Spatial Variability ◽  
Spatial Data ◽  
Interpolation Method ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Spatial Data Analysis ◽  
Statistical Tool ◽  
Voronoi Map ◽  
Result Show ◽  
Inverse Distance

This article expose the spatial variability of monthly-rainfall (MR) in East Java region. Monthly rainfall data were collected from 943 pluviometres spread around the regions. Spatial statistics analysed by means of ESDA (Exploratory Spatial Data Analysis) techniques available on Geostatistical Analyst extention of ArcGIS (9.3). Statistical tools exploited to analise the data include: (1) Histogram, (2) Voronoi Map, and (3) QQ-Plot. The result show that histogram and QQ-Plot of Monthly Rainfall data are leptocurtosis. Statistical value obtained from the analysis are: minimum = 54 mm/month, average = 155,5 mm/month, maximum = 386 mm/month, and median = 150 mm/month. Other statistical value summarised are: standard deviation = 44,2 ; skewness = 0,95; and curtosis = 5,09. Finally, monthly rainfall-maps are produced by interpolating the data using Inverse Distance Weighed (IDW) interpolation method. The research demonstrate the capability and benefit of those statistical tool to describe detailed spatial variability of rainfall.

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