This work presents a novel point interpolation algorithm that is derived from a simple weighted linear regression model. The resulting expression is similar to Inverse Distance Weighting (IDW), which is a widely adopted interpolation algorithm. The novel approach is compared to other methods on synthetic data and also over study cases related to solar radiation, surface elevation, well elevation, and precipitation. Relevant aspects of IDW are preserved while the novel algorithm achieves better results with statistical significance. Artifacts are alleviated in interpolated surfaces generated by the novel approach when compared to the respective surfaces from IDW. The novel method was also revealed, for some cases, as the best alternative among all methods tested in terms of root mean square error. Computational efficiency was shown as competitive or even superior to most of the alternatives under certain conditions. This work is an extended version of our previous conference paper [LNCS 12138, 576 (2020)].

这项工作提出了一种新颖的点插值算法，该算法是从简单的加权线性回归模型得出的。结果表达式类似于逆距离加权（IDW），后者是一种广泛采用的插值算法。在合成数据上以及与太阳辐射，地表高程，井高和降水有关的研究案例中，将该新方法与其他方法进行了比较。IDW的相关方面得以保留，而新算法获得了具有统计意义的更好结果。与来自IDW的各个表面相比，在通过新颖方法生成的插值曲面中，伪像得到缓解。在某些情况下，就均方根误差而言，该新方法还被证明是所有测试方法中的最佳替代方法。在某些条件下，计算效率被证明具有竞争力，甚至优于大多数替代方案。这项工作是我们以前的会议论文[LNCS12138，576（2020）]。