Results 31 to 40 of about 12,800 (234)

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, Wenbo Wu, Victor De Oliveira, Keying Ye   +3 more
wiley   +1 more source

Optimale Methoden zur Interpolation von Umweltvariablen in Geographischen Informationssystemen [PDF]

Geographica Helvetica
In Geographie Information Systems there are many applications where the objeets of study can only be described by sampling information at selected point locations and by subsequent interpolation.
P. A. Burrough
doaj   +1 more source

Kriging Convolutional Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
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.
Gabriel Appleby, Linfeng Liu 0004, Liping Liu 0001   +2 more
openaire   +3 more sources

Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Deep Underground Science and Engineering, EarlyView.
B1 is bord width 1, B2 is bord width 2, L is the pillar length, W is the pillar width, red color and letter A represent the pillars, and white color and number 1 represent excavated areas. Pstress is the average pillar stress; σv is the vertical component of the virgin stress, MPa; and e is the areal extraction ratio. e = B o B o + B P ${\rm{e}}=\frac{{
Tawanda Zvarivadza, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Stochastic Kriging for Simulation Metamodeling [PDF]

Operations Research, 2008
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables, or uncontrollable ...
Ankenman, Bruce E., Nelson, Barry L., Staum, Jeremy   +2 more
openaire   +1 more source

Exponentially‐constrained Gaussian mixture model for quantifying vertical development of water‐conducting fracture zones in lithologically stratified mining roofs

Deep Underground Science and Engineering, EarlyView.
This study proposes an exponentially‐constrained Gaussian mixture model (EcGMM) to quantify vertical fracture heterogeneity in stratified roof strata. The model integrates a piecewise exponential decay term capturing global stress dissipation with Gaussian components representing localized fracture intensification at lithological interfaces.
Huiqing Yuan, Zhimin Xu, Weixiao Chen, Chenghang Zhang, Shijie Xiong, Yajun Sun   +5 more
wiley   +1 more source

Convex and monotonic bootstrapped Kriging [PDF]

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC), 2012
Abstract: Distribution-free bootstrapping of the replicated responses of a given discreteevent simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing simulation with as output either the mean or the 90% quantile of
Kleijnen, Jack P.C., Mehdad, E., van Beers, W.C.M.   +2 more
openaire   +6 more sources

Bias Correction of ERA5 Temperature Across Five Topoclimatic Clusters: A Comparative Evaluation of Four Methods

International Journal of Climatology, EarlyView.
ERA5 near‐surface temperature over South America exhibits systematic cold biases in daily maximum temperature and warm biases in daily minimum temperature. To address the continent's strong climatic and topographic heterogeneity, South America was partitioned into five topoclimatic clusters, and four bias‐correction approaches were evaluated. The Multi‐
José Roberto Rozante, Gabriela Rozante
wiley   +1 more source

Climatic Drivers of the Area Burned by Winter Wildfires in Northern Italy

International Journal of Climatology, EarlyView.
This study investigates winter wildfires occurring between November and April in northern Italy over the period 2008–2022. By integrating high‐resolution burned‐area data with gridded climatic indices derived from 150 weather stations, it identifies spatial and temporal wildfire patterns, highlights the most fire‐prone mountain areas through pixel ...
Alice Baronetti, Paolo Fiorucci, Antonello Provenzale   +2 more
wiley   +1 more source