Results 101 to 110 of about 2,567 (206)

Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information

ISPRS International Journal of Geo-Information, 2017
In this study, different interpolation techniques are presented, assessed, and compared for the estimation of soil iron (Fe) contents in locations where observations were not available.
Panagiotis Tziachris, Eirini Metaxa, Frantzis Papadopoulos, Maria Papadopoulou   +3 more
doaj   +1 more source

Spatial interpolation methods for monthly rainfalls and temperatures in Basilicata

Forest@, 2008
Spatial interpolated climatic data on grids are important as input in forest modeling because climate spatial variability has a direct effect on productivity and forest growth. Maps of climatic variables can be obtained by different interpolation methods
Ferrara A, Borghetti M, Mancino G, Fiorenzo F   +3 more
doaj   +1 more source

Analisis Spasial Prediksi Sebaran Jumlah Mobil Pribadi di Kota Bandung dengan Metode Universal Kriging [PDF]

, 2016
Kota Bandung memiliki permasalahan yang hampir sama dengan kota lainnya, yaitu salah satunya merupakan kemacetan. Kemacetan di Kota Bandung terjadi disebabkan oleh berbagai aspek. Salah satu penyebabnya adalah kendaraan mobil pribadi.
ARI NUGROHO
core  

Mapping Rainfall Variability in the São Francisco River Basin: Insights for Water Resource Management

Revista Brasileira de Meteorologia
The São Francisco River Basin (SFRB) is extremely important for both the local rural and urban population, running through several states and municipalities in Brazil.
Elias Silva de Medeiros, Willian dos Santos Oliveira, Paulo Vinicius da Silva, Amaury de Souza, Carolina Cristina Bicalho, Munir Mauad, Sidnei Azevedo de Souza   +6 more
doaj   +1 more source

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Sustainable Environment Research, 2019
Digital soil mapping for soil texture is mostly an understanding of how soil texture fractions vary in space as influenced by environmental variables mainly derived from the digital elevation model (DEM).
Jeremy P. Mondejar, Alejandro F. Tongco
doaj   +1 more source

Supplemental Material, UniversalKriging_SMR_Supplemental_Content - Using Universal Kriging to Improve Neighborhood Physical Disorder Measurement [PDF]

, 2018
Supplemental Material, UniversalKriging_SMR_Supplemental_Content for Using Universal Kriging to Improve Neighborhood Physical Disorder Measurement by Stephen J. Mooney, Michael D. M. Bader, Gina S. Lovasi, Kathryn M. Neckerman, Andrew G.
Michael D. M. Bader (660736), Andrew G. Rundle (453802), Julien O. Teitler (660732), Gina S. Lovasi (660735), Kathryn M. Neckerman (660733), Stephen J. Mooney (660730)   +5 more
core   +1 more source

Kernel Approximations for Universal Kriging Predictors [PDF]

This work derives explicit kernel approximations for universal kriging predictors for a class of intrinsic random function models. This class of predictors is of particular interest because they are equivalent to the standard two-dimensional thin plate ...
Stein, M., Zhang, B.
core  

Combined Universal Kriging and Kalman Filter Techniques to Improve Wind Speed Prediction for Northeastern U.S. [PDF]

, 2017
The scope of this study is to identify and improve wind speed prediction errors for storms that have impacted the Northeastern United States during 2003-2014.
Samalot, Alexander
core  

Grade control in a quartz deposit using universal fuzzy kriging [PDF]

[EN] We adapt the universal kriging method of prediction to contexts with uncertainty and imprecise data. Using fuzzy set theory, we develop a fuzzy universal kriging that we use to estimate quality classes for a quartz deposit.
Saavedra González, Angeles, Giráldez Pérez, Eduardo, Ordóñez Galán, Celestino, Taboada Castro, Javier, Sierra Fernández, Carlos   +4 more
core  

Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction

Environmental Research Communications
This study advances environmental monitoring by predicting the spatial and temporal distribution of Harmful Algal Blooms (HABs) in the Republic of Korea through a hybrid approach that combines geostatistical and deep learning methods.
Sung Jae Kim, Yongbok Cho
doaj   +1 more source