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So interpolation is not only a pure mathematical problem, but important task in computer vision and artificial intelligence.
The water table depth may not be a stationary variable, so, interpolation through OK obviously added some error to the prediction.
The field test data had some blank areas due to the tunnels, so interpolation was applied to specify the speed in the intermediate points.
Reliable data are essential for assessing and monitoring groundwater quality, but in many cases data collection all over the area is time-consuming and not possible, so interpolation methods can save time and money significantly (Sahebjalal 2012).
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More important is that the value of the interpolating function at any point in the interpolating region can be modified under the condition that the interpolating data are not changed by selecting suitable parameters, so the interpolation surface may be modified for the given interpolation data when needed in practical design.
If so, the interpolation is again performed around a different point.
Point data are usually inadequate to identify vulnerable areas, so appropriate interpolation methods are needed to map the spatial distribution pattern of groundwater quality parameters.
Note that in this case, y has the same resolution as z so an interpolation method has to be used to obtain y from the observed MS image.
An attractive feature of RBF networks is a result by Micchelli [62] that (again under mild conditions on (G)) the matrix ([G(mathbf{x}_j-mathbf{x}_k)]_{j,k=1}^M) is always invertible for an arbitrary choice of (M) and (mathbf{x}_k)'s, so that interpolation by RBF networks is always possible.
Exploratory data analysis indicates that the relationship between service volume and time is generally linear, so linear interpolation method was used to impute the missing values.
For the prospective analysis, we compared methods i, j, and k. (Not every still-living person had a "final" value, and so linear interpolation was not possible).
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