Journal ArticleDOI
An adaptive inverse-distance weighting spatial interpolation technique
George Y. Lu,David Wong +1 more
TLDR
Adaptive IDW performs better than the constant parameter method in most cases, and better than ordinary kriging in one of the authors' empirical studies when the spatial structure in the data could not be modeled effectively by typical variogram functions.About:
This article is published in Computers & Geosciences.The article was published on 2008-09-01. It has received 1002 citations till now. The article focuses on the topics: Inverse distance weighting & Weighting.read more
Citations
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Journal ArticleDOI
Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan
Feng-Wen Chen,Chen-Wuing Liu +1 more
TL;DR: In this paper, the authors used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan, and evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power (α value), and a radius of influence (search radius).
Journal ArticleDOI
Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.
TL;DR: It is argued that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized and it is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources.
Journal ArticleDOI
Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium
TL;DR: In this article, the authors developed different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and compared the results of geostatistical and deterministic approaches.
Proceedings ArticleDOI
Deep Distributed Fusion Network for Air Quality Prediction
TL;DR: A deep neural network (DNN)-based approach, which consists of a spatial transformation component and a deep distributed fusion network, to predict the air quality of next 48 hours for each monitoring station, considering air quality data, meteorology data, and weather forecast data.
Journal ArticleDOI
Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application
TL;DR: This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM2.5 data in the contiguous U.S. based on the error statistics results of k-fold cross validation, and finds the SF-based method performed better overall than the IDW-based methods.
References
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TL;DR: In this paper, Krigeage and continuite spatiale were used for interpolation of a variogramme with anisotropic interpolation reference record created on 2005-06-20, modified on 2011-09-01.
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TL;DR: In this article, an advanced-level introduction to geostatistics and Geostatistical methodology is provided, including tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation.