Effects of Sampling and Interpolation Methods on Accuracy of Extracted Watershed Features

Understanding the magnitude and distribution of the mixes of the near-ground carbon dioxide (CO2) components spatially (related to the surface characteristics) and temporally (over seasonal timescales) is critical to evaluating present and future climate impacts. Thus, the application of in situ measurement approaches, combined with the spatial interpolation methods, will help to explore variations in source contribution to the total CO2 mixing ratios in the urban atmosphere. This study presents the spatial characteristic and temporal trend of atmospheric CO2 levels observed within the city of Wroclaw, Poland for the July 2017–August 2018 period. The seasonal variability of atmospheric CO2 around the city was directly measured at the selected sites using flask sampling with a Picarro G2201-I Cavity Ring-Down Spectroscopy (CRDS) technique. The current work aimed at determining the accuracy of the interpolation techniques and adjusting the interpolation parameters for estimating the magnitude of CO2 time series/seasonal variability in terms of limited observations during the vegetation and non-vegetation periods. The objective was to evaluate how different interpolation methods will affect the assessment of air pollutant levels in the urban environment and identify the optimal sampling strategy. The study discusses the schemes for optimization of the interpolation results that may be adopted in areas where no observations are available, which is based on the kriging error predictions for an appropriate spatial density of measurement locations. Finally, the interpolation results were extended regarding the average prediction bias by exploring additional experimental configurations and introducing the limitation of the future sampling strategy on the seasonal representation of the CO2 levels in the urban area.

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On Hermite-Fejér type interpolation on the Chebyshev nodes

Bulletin of the Australian Mathematical Society ◽

10.1017/s0004972700012223 ◽

1993 ◽

Vol 47 (1) ◽

pp. 13-24 ◽

Cited By ~ 2

Author(s):

Graeme J. Byrne ◽

T.M. Mills ◽

Simon J. Smith

Keyword(s):

Approximation Error ◽

Interpolation Polynomial ◽

Precise Estimate ◽

Type Interpolation ◽

Interpolation Methods ◽

Chebyshev Nodes ◽

Interpolatory Process

Given f ∈ C [−1, 1], let Hn, 3(f, x) denote the (0,1,2) Hermite-Fejér interpolation polynomial of f based on the Chebyshev nodes. In this paper we develop a precise estimate for the magnitude of the approximation error |Hn, 3(f, x) − f(x)|. Further, we demonstrate a method of combining the divergent Lagrange and (0,1,2) interpolation methods on the Chebyshev nodes to obtain a convergent rational interpolatory process.

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Comparison of Data Interpolation Methods in Time Course Pupil Diameter Data

2020 Medical Technologies Congress (TIPTEKNO) ◽

10.1109/tiptekno50054.2020.9299242 ◽

2020 ◽

Author(s):

Mahdieh Farzin Asanjan ◽

Vilda Purutcuoglu ◽

Fikret Ari ◽

Didem Gokcay

Keyword(s):

Time Course ◽

Pupil Diameter ◽

Data Interpolation ◽

Interpolation Methods

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Comparison of boundary interpolation methods on the geometrical modeling of viscosity for CaO-Al2O3-SiO2 melts

Journal of Non-Crystalline Solids ◽

10.1016/j.jnoncrysol.2021.120782 ◽

2021 ◽

Vol 562 ◽

pp. 120782

Author(s):

Xiaomin Zha ◽

Dexiang Hou ◽

Zhigang Yu ◽

Jieyu Zhang ◽

Kuochih Chou

Keyword(s):

Boundary Interpolation ◽

Geometrical Modeling ◽

Interpolation Methods

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Comparison of Different Interpolation Methods for Prediction of Soil Salinity in Arid Irrigation Region in Northern China

Agronomy ◽

10.3390/agronomy11081535 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1535

Author(s):

Tonggang Fu ◽

Hui Gao ◽

Jintong Liu

Keyword(s):

Soil Salinity ◽

Subsurface Layer ◽

Salt Content ◽

Spatial Prediction ◽

Northern China ◽

Sodium Adsorption Ratio ◽

Interpolation Methods ◽

Distance Weighting ◽

Total Salt Content ◽

Inverse Distance

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity.

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