Results 41 to 50 of about 584,789 (249)
Advancing Iron Ore Grade Estimation: A Comparative Study of Machine Learning and Ordinary Kriging
MineralsMineral grade estimation is a vital phase in mine planning and design, as well as in the mining project’s economic assessment. In mining, commonly accepted methods of ore grade estimation include geometrical approaches and geostatistical techniques such ...
Mujigela Maniteja +7 more
semanticscholar +1 more source
Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest–Ordinary Kriging Model
Applied SciencesThe accurate prediction of soil contamination in abandoned mining areas is necessary to address their environmental risks. This study employed a combined model of machine learning and geostatistics to predict the spatial distribution of soil ...
Hosang Han, J. Suh
semanticscholar +1 more source
Comparison of sensitivity of interpolation methods by rain gauge network density [PDF]
علوم و مهندسی آبیاریDifferent interpolation methods, each with their own strengths and weaknesses, are used to temporal and spatial estimation of precipitation. The accuracy of this estimate depends on the density of point data measured at rain gauge stations. In this study,
Amin Kanooni, Erfan Faraji Amogein
doaj +1 more source
Sparsity-aware field estimation via ordinary Kriging
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 In this paper, we consider the problem of estimating a spatially varying field in a wireless sensor network, where resource constraints limit the number of sensors selected in the network that provide their measurements for field estimation. Based on a one-to-one correspondence between the selected sensors and the nonzero elements of Kriging weights ...
Sijia Liu 0001 +3 more
openaire +2 more sources
Metamodel-Assisted Multidisciplinary Design Optimization of a Radial Compressor
International Journal of Turbomachinery, Propulsion and Power, 2019 Kriging is increasingly used in metamodel-assisted design optimization. For expensive simulations; however, one can afford only a few samples to build the Kriging model, which consequently lacks prediction accuracy.
Mohamed H. Aissa, Tom Verstraete
doaj +1 more source
Advanced Science, EarlyView.Furrow tillage resolves the conventional‐vs.‐no‐tillage trade‐off by simultaneously cutting CO2 efflux to 2.0–3.0 g C m−2 d−1 and unlocking high nutrient availability for the rice rhizosphere. This scalable agronomic solution strengthens soil health, enhances plant physiology, reshapes microbial metabolism, and shifts paddy systems toward a net ...
Arnab Majumdar +10 more
wiley +1 more source
Ege Bölgesi’nde yağışın mekânsal dağılımı
Coğrafi Bilimler Dergisi, 2013 Spatial modelling of climatological variables is one of the most crucial parts of environmental studies. Prediction of natural events such as hydrological phenomenon, drought, flood, ground-surface water amount, pollution of water sources, and issues ...
Olgu Aydın, İhsan Çiçek
doaj +1 more source
Human‐in‐the‐Loop Swarms: A Bionic Swarm Approach to Real‐World Soil Mapping
Advanced Intelligent Systems, EarlyView.This article introduces the “Bionic Swarm,” a novel system that lowers the barriers to real‐world swarm validation by abstracting difficult hardware tasks to app‐guided human agents. We demonstrate the system's utility through the experimental validation of a geotechnical soil‐mapping swarm algorithm and show superior performance to baseline approaches
Petras Swissler +5 more
wiley +1 more source
Spatial Prediction for Real Data using Kriging Technique [PDF]
المجلة العراقية للعلوم الاحصائية, 2018 This study investigates the prediction of unstable random spatial process using the Ordinary Kriging technique, based on the variogram function Y(X,Y) in finding out the predictors, and the universal multivariate technique, which has been applied by ...
Shaymaa Riyadh Thanoon Thanoon
doaj +1 more source
A partial envelope approach for modelling multivariate spatial‐temporal data
Canadian Journal of Statistics, EarlyView.Abstract In the new era of big data, modelling multivariate spatial‐temporal data is a challenging task due to both the high dimensionality of the features and complex associations among the responses across different locations and time points.
Reisa Widjaja +3 more
wiley +1 more source