Results 91 to 100 of about 13,495 (192)

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

A disposition of interpolation techniques [PDF]

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2)
Heuvelink, G.B.M., Knotters, M.
core   +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

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Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines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

Monotonicity-Preserving Bootstrapped Kriging Metamodels for Expensive Simulations [PDF]

Kriging (Gaussian process, spatial correlation) metamodels approximate the Input/Output (I/O) functions implied by the underlying simulation models; such metamodels serve sensitivity analysis and optimization, especially for computationally expensive ...
Beers, W.C.M. van, Kleijnen, Jack P.C.
core   +1 more source

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

Universal Kriging for Accurate Shadow Fading Modeling in LOS/NLOS Conditions

IEEE Open Journal of Antennas and Propagation
Traditional path loss (PL) models often fail to capture the spatial characteristics of wireless channels in environments combining line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, where shadow fading exhibits clear spatial non-stationarity ...
Melissa E. Diago-Mosquera, Francisco Cuevas-Pacheco, Leyre Azpilicueta   +2 more
doaj   +1 more source

Interpolation and Synthesis of Sparse Samples in Exoplanet Atmospheric Modeling

The Planetary Science Journal
This paper highlights methods from geostatistics that are relevant to the interpretation, intercomparison, and synthesis of atmospheric model data, with a specific application to exoplanet atmospheric modeling.
Jacob Haqq-Misra, Eric T. Wolf, Thomas J. Fauchez, Ravi K. Kopparapu   +3 more
doaj   +1 more source

Satellite-Based NO2 and Model Validation in a National Prediction Model Based on Universal Kriging and Land-Use Regression. [PDF]

Environ Sci Technol, 2016
Young MT, Bechle MJ, Sampson PD, Szpiro AA, Marshall JD, Sheppard L, Kaufman JD.   +6 more
europepmc   +1 more source