A Comparison between Interpolation Methods for More Accurate Elevation Surface Using Gnss And Gis

Abdulrahman, Farsat Heeto; Foroughi, Soleiman; Adam, Sarhat Mustafa

doi:10.26682/csjuod.2020.23.2.27

Cited by 1 publication

(1 citation statement)

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“…At the end of their study, they found that the interpolation technique gave significant results against the prediction of the original ballistic projectile. Interpolation techniques have also been shown to be often used in real-world data problems where Abdulrahman et al [15] has made an assessment of surface area with height using global navigation satellite systems (GNSS) and geographical information systems (GIS) techniques. To test the best technique to be used, the researcher has tested the data obtained using different interpolation techniques namely inverse distance weighting (IDW), ordinary kriging, and local polynomial interpolation (LPI).…”

Section: Introductionmentioning

confidence: 99%

Spatial interpolation method comparison for physico-chemical parameters of river water in Klang River using MATLAB

et al. 2022

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Water quality is one of the most highly debated issues worldwide at the moment. Inadequate water supplies affect human health, hinder food production, and degrade the environment. Using contemporary technology to analyze pollution statistics can help solve pollution issues. One option is to take advantage of advancements in intelligent data processing to conduct hydrological parameter analysis. To perform conclusive water quality studies, a lot of data is necessary. Unfilled data (information gaps) in the long-term hydrological data set may be due to equipment faults, collection schedule delays, or the data collection officer’s absence. The lack of hydrological data skews its interpretation. Therefore, interpolation is used to recreate and fill missing hydrological data. From 2012 to 2017, the Klang River’s biochemical oxygen demand (BOD) in Selangor, Malaysia, was sampled. This study examined three methods of interpolation for their effectiveness using the MATLAB software: piecewise cubic hermite interpolating polynomial (PCHIP), cubic Spline data interpolation (Spline), and modified Akima partitioned cubic hermite interpolation (Makima). The accuracy is assessed using root mean square error (RMSE). All interpolation algorithms offer excellent results with low RMSE. However, PCHIP delivers the best match between interpolated and original data.

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Section: Introductionmentioning

confidence: 99%