scholarly journals Prediction of cadmium pollutant with ordinary point kriging method using Gstat-R

2017 ◽  
Author(s):  
Annisa Nur Falah ◽  
Atje Setiawan Abdullah ◽  
Kankan Parmikanti ◽  
Budi Nurani Ruchjana
Keyword(s):  
Kriging Method ◽  
Ordinary Point

Prediction of Spread Shear Strength of Rock with Ordinary Point Kriging Method using GStat-R

2019 ◽  
Vol 13 (3) ◽  
pp. 393-399
Author(s):  
Atje Setiawan, Nur, Annisa Nur, Budi Nurani Abdullah, Hamid, Falah, Ruchjana
Keyword(s):  
Shear Strength ◽  
Kriging Method ◽  
Ordinary Point

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.

scholarly journals DEVELOPMENT OF THE MONTHLY AVERAGE DAILY SOLAR RADIATION MAP USING A-CBR, FEM, AND KRIGING METHOD

2017 ◽  
Vol 24 (2) ◽  
pp. 489-512 ◽  
Author(s):  
Choongwan KOO ◽  
Taehoon HONG ◽  
Kwangbok JEONG ◽  
Jimin KIM
Keyword(s):  
Solar Radiation ◽  
Decision Makers ◽  
Estimation Accuracy ◽  
Case Based Reasoning ◽  
Final Decision ◽  
Kriging Method ◽  
Pv System ◽  
Fem Model ◽  
Remote Sensors ◽  
Monthly Average

Photovoltaic (PV) system could be implemented to mitigate global warming and lack of energy. To maximize its effectiveness, the monthly average daily solar radiation (MADSR) should be accurately estimated, and then an accurate MADSR map could be developed for final decision-makers. However, there is a limitation in improving the accuracy of the MADSR map due to the lack of weather stations. This is because it is too expensive to measure the actual MADSR data using the remote sensors in all the sites where the PV system would be installed. Thus, this study aimed to develop the MADSR map with improved estimation accuracy using the advanced case-based reasoning (A-CBR), finite element method (FEM), and kriging method. This study was conducted in four steps: (i) data collection; (ii) estimation of the MADSR data in the 54 unmeasured locations using the A-CBR model; (iii) estimation of the MADSR data in the 89 unmeasured locations using the FEM model; and (iv) development of the MADSR map using the kriging method. Compared to the previous MADSR map, the proposed MADSR map was determined to be improved in terms of its estimation accuracy and classification level.

Prediction concentration of PM2.5 in Surabaya using ordinary Kriging method

2021 ◽  
Author(s):  
Derbi W. Fitri ◽  
Nurul Afifah ◽  
Siti M. D. Anggarani ◽  
Nur Chamidah
Keyword(s):  
Ordinary Kriging ◽  
Kriging Method

Generation of surrogate-based aerodynamic model of an UCAV configuration using an adaptive co-Kriging method

2019 ◽  
Vol 95 ◽  
pp. 105511 ◽  
Author(s):  
Halil Kaya ◽  
Hakan Tiftikçi ◽  
Ümit Kutluay ◽  
Evren Sakarya
Keyword(s):  
Kriging Method ◽  
Aerodynamic Model

Mapping properties of an asynchronous crop: the example of time interval between flowering and maturity of banana

2017 ◽  
Vol 8 (2) ◽  
pp. 481-486 ◽  
Author(s):  
J. Lamour ◽  
O. Naud ◽  
M. Lechaudel ◽  
B. Tisseyre
Keyword(s):  
Precision Agriculture ◽  
Development Stage ◽  
Production Data ◽  
Time Interval ◽  
Plant Responses ◽  
Spatial Structures ◽  
Kriging Method ◽  
New Methods ◽  
Operational Constraints ◽  
Banana Crops

Precision agriculture for banana crops has been little investigated so far. The main difficulty to implement precision agriculture methods lies in the asynchronicity of this crop: after a few cycles, each plant has its own development stage in the field. Indeed, maps of agronomical interest are difficult to produce from plant responses without implementing new methods. The present study explores the feasibility to derive a spatially relevant indicator from the date of flowering and the date of maturity (time to harvest). The time between these dates (TFM) may give insight in spatial distribution of vigor. The study was carried out using production data from 2015 acquired in a farm from Cameroon. Data from individual plants that flowered at different weeks were gathered so as to increase the density of TFM sampling. The temporal variability of TFM, which is induced by weather and operational constraints, was compensated by centering TFM data on their medians (TFMc). The mapping of TFMc was obtained using a classical kriging method. Spatial structures highlighted by TFMc either at the farm level or at the plot level, suggest that such maps could be used to support agronomic decisions.

scholarly journals An adaptive kriging method for solving nonlinear inverse statistical problems

2017 ◽  
Vol 28 (4) ◽  
pp. e2439 ◽  
Author(s):  
Shuai Fu ◽  
Mathieu Couplet ◽  
Nicolas Bousquet
Keyword(s):  
Kriging Method ◽  
Statistical Problems ◽  
Adaptive Kriging
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