Open AccessCross-Validation of Elevation Data Interpolation
Author(s) -
Hussain Zaydan Ali
Publication year - 2021
Publication title -
magallaẗ kulliyyaẗ al-rāfidayn al-ǧāmi'aẗ al-'ulūm/maǧallaẗ kulliyyaẗ al-rāfidayn al-ǧāmiʻaẗ li-l-ʻulūm
Language(s) - English
Resource type - Journals
eISSN - 2790-2293
pISSN - 1681-6870
DOI - 10.55562/jrucs.v32i2.326
Subject(s) - interpolation (computer graphics) , elevation (ballistics) , digital elevation model , multivariate interpolation , computer science , inverse distance weighting , data mining , mean squared error , set (abstract data type) , spatial analysis , sample (material) , cross validation , algorithm , remote sensing , statistics , geography , mathematics , bilinear interpolation , artificial intelligence , computer vision , motion (physics) , chemistry , geometry , chromatography , programming language
Most geographical spatial analysis requires a continuous data set and this study is designed to create such a surface. Digital model of landscape is an important part within creation of geo-information systems. It is an important tool in applications, which model an Earth’s surface like geomorphology, hydrology, geology, cartography, ecology, etc. Many software products offer different interpolation methods for creation of digital model of landscape. Its accuracy and quality is impacted by selection of an interpolation method and precision input data. Several studies have demonstrated that various spatial interpolation techniques perform differently depending on the type of attribute, geometrical configuration of the samples, spatial resolution, world region, etc. Hence, selecting the best interpolation technique for each particular situation is a key factor. The major objective of this paper is to assess the spatial variability of elevation data in Iraq by comparing different interpolation procedures. The elevation data were interpolated using a deterministic method (Inverse square distance) and geostatistical methods in ArcGIS. Cross-validation is a sample reuse algorithm for quantitative comparison of experimental performance of alternative interpolation methods. Cross validation can help make an informed decision as to which method provides the best results. Two diagnostic statistics are mainly considered in this paper from the results mean error, and root mean square error.