Spatial Interpolation of Meteorologic Variables in Vietnam using the Kriging Method

2015 ◽  
Keyword(s):  
Spatial Interpolation ◽  
Kriging Method

scholarly journals Spatial Interpolation on Rainfall Data over Peninsular Malaysia Using Ordinary Kriging

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Suhaila Jamaludin ◽  
Hanisah Suhaimi
Keyword(s):  
Spatial Analysis ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Rainfall Intensity ◽  
Peninsular Malaysia ◽  
Rainfall Data ◽  
Kriging Method ◽  
Intensity Index ◽  
Dry Conditions ◽  
Two Indices

This study presents the spatial analysis of the rainfall data over Peninsular Malaysia. 70 rainfall stations were utilized in this study. Due to the limited number of rainfall stations, the Ordinary Kriging method which is one of the techniques in Spatial Interpolation was used to estimate the values of the rainfall data and to map their spatial distribution. This spatial analysis was analysed according to the two indices that describe the wet events and another two indices that characterize dry conditions. Large areas at the east experienced high rainfall intensity compared to the areas in the west, northwest and southwest. The small value that has been obtained in Aridity Intensity Index (AII) reflects that the high amount of rainfall in the eastern areas is not contributed by low-intensity events (less than 25th percentile). In terms of number of consecutive dry days, Northwestern areas in Peninsular Malaysia recorded the highest value. This finding explains the occurrence of a large number of floods and soil erosions in the eastern areas.

scholarly journals Kriging Convolutional Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 3187-3194
Author(s):  
Gabriel Appleby ◽  
Linfeng Liu ◽  
Li-Ping Liu
Keyword(s):  
Neural Networks ◽  
Spatial Interpolation ◽  
Recent Progress ◽  
Kriging Method ◽  
Convolutional Networks ◽  
Spatial Locality ◽  
Estimation Problems ◽  
Specific Configuration ◽  
Graph Neural Networks ◽  
Direct Use

Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends. Traditional kriging methods have strong Gaussian assumptions, and as a result, often fail to capture complexities within the data. Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining advantages of Graph Neural Networks (GNN) and kriging. Compared to standard GNNs, KCNs make direct use of neighboring observations when generating predictions. KCNs also contain the kriging method as a specific configuration. Empirically, we show that this model outperforms GNNs and kriging in several applications.

scholarly journals PERBANDINGAN INTERPOLASI SPASIAL DENGAN METODE ORDINARY DAN ROBUST KRIGING PADA DATA SPASIAL BERPENCILAN (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

2012 ◽  
Author(s):  
NI MADE SUMA FRIDAYANI ◽  
I PUTU EKA NILA KENCANA ◽  
KOMANG GDE SUKARSA
Keyword(s):  
Spatial Data ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Control Point ◽  
Mean Absolute Deviation ◽  
Kriging Method ◽  
Absolute Deviation ◽  
Record Keeping ◽  
Kriging Estimation ◽  
Extreme Observation

Kriging as optimal spatial interpolation can produce less precise predictive value if there are outliers among the data. Outliers defined as extreme observation value of the other observation values that may be caused by faulty record keeping, improper calibration equipment or other posibbilities. Development of Ordinary Kriging method is Robust Kriging which transforms weight of clasic variogram thus become variogram that robust to outlier. The spatial data that used in this research is the spatial data that contains outliers and meet the assumptions of Ordinary Kriging. The analysis showed that the estimation value of Ordinary Kriging and Robust Kriging method is not much different in terms of Mean Absolute Deviation values which generated by both methods. An increase value of Mean Absolute Deviation on Robust Kriging estimation does not indicate that the Ordinary Kriging method is more precise than Robust Kriging method in the rainfall estimates of Amlapura control point remind that Robust Kriging does not eliminate the data of observation such as the Ordinary Kriging method. In general, Ordinary Kriging and Robust Kriging method can estimate the rainfall value of Amlapura control point quite well although it is not able to cover the changes in rainfall value that occurs due to the behavior geographic data.

scholarly journals INTERPOLASI SPASIAL DENGAN METODE ORDINARY KRIGING MENGGUNAKAN SEMIVARIOGRAM ISOTROPIK PADA DATA SPASIAL (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

2015 ◽  
Vol 4 (1) ◽  
pp. 26
Author(s):  
PUTU MIRAH PURNAMA D. ◽  
KOMANG GDE SUKARSA ◽  
KOMANG DHARMAWAN
Keyword(s):  
Spatial Data ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Control Points ◽  
Kriging Method ◽  
3D Data ◽  
Kriging Interpolation Method ◽  
Relationship Of

Spatial data is data that is presented in the geographic of an object, related to the location, shape and relationship of the earth in space. One of example of spatial data is rainfall. To determine the value of rainfall in an area, built to predict rain post information regarding rainfall. Spatial interpolation is used to estimate rainfall by collecting rainfall values held rain heading around. Assessment methods used in the estimate the rainfall in the Karangasem district is ordinary kriging using isotropic semivariogram that takes into account height on spatial data. Isotropic semivariogram which only takes into account the distance alone. Ordinary kriging method using isotropic semivariogram that takes into account height  value estimated rainfall is much different to the values at the control points Amlapura and Besakih. Interpolation on 3D data are not suitable for use on ordinary kriging method, grouping should be done at the data into a few weeks to application of ordinary kriging interpolation method using anisotropic semivariogram on 3D data.

A spatial interpolation method of granulometric properties of superficial sediments

2006 ◽  
Vol 177 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Alain Lepretre ◽  
Georges Chapalain ◽  
Patrice Carpentier
Keyword(s):  
Grain Size ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Statistical Parameter ◽  
Size Distributions ◽  
Kriging Method ◽  
Grain Size Distributions ◽  
Superficial Sediments ◽  
Spatial Interpolation Method ◽  
Granulometric Characteristics

Abstract The present paper deals with the estimate and the mapping of granulometric characteristics (i.e. grain-size distributions and statistical parameters) of superficial sediments. A spatial interpolation method based on use of a spherical factor analysis (SFA) and a kriging procedure is proposed. Applied to a set of data collected in the bay of Wissant (straits of Dover), this mixed SFA and kriging method proves to be much more efficient than a direct kriging method for reproducing the complex typology of a statistical parameter such as the median. Beyond cartographical uses, this new interpolation method should be helpful (1) to investigate the time-evolution of a sedimentary cover subject to a time-varying sampling plan and (2) to estimate granulometric characteristics, especially the grain-size distributions, at the grid points of a numerical model for hydrodynamics and multicomponents sediment transport.

Review of Matheron’s Kriging Method and its Application at the Estimation of Mineral Deposits

Veritas ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 59
Author(s):  
Edgar M Marín Ballón ◽  
Hugo Jiménez-Pacheco ◽  
Máximo O. M. Rondón Rondón ◽  
Antonio E. Linares Flores Castro ◽  
Ferly E. Urday Luna
Keyword(s):  
Geological Structure ◽  
Mineral Deposits ◽  
Kriging Method ◽  
Geostatistical Methods ◽  
Recoverable Reserves ◽  
Complex Geological Structure ◽  
The Mean ◽  
Industrial Software ◽  
Strict Stationarity ◽  
Mining Exploration

The Geostatistics provides effective tools for the solution of many problems of engineering in which the location in the space of the variable under study is considered, based on definitions of mathematics that provide the necessary foundation for its application. In particular, the Geostatistics are applied in the spatial estimation of the recoverable reserves of mineral deposits. The geostatistical methods that are used in the estimation of mineral deposits are implemented in industrial software and consider the evaluation of the complex geological structure, but these softwares only display the obtained results with an input data and do not exhibit the concepts thatthey use during the process or the methodology of its application. This happens particularly with the Kriging method, which is based on the assumption of strict stationarity, taking into account changes in the mean and local variations, therefore unreliable. In this study is established to review the Kriging method, its application in the estimation of the recoverable reserves of mining deposits and the relevance of the developed model established particularly in mines ofPeru, which use this method as part of the mining exploration for the evaluation of the feasibility of exploitation.

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