IDW:逆距离权重法
逆距离权重法(Inverse Distance Weighting,简称IDW)是一种广泛应用于空间插值和数据分析领域的重要方法。为了便于书写和使用,该方法通常以其英文缩写“IDW”来表示。作为一种经典的地理统计技术,它基于“距离越近,相似性越高”的原理,通过计算待估点与邻近已知点之间的加权平均值来预测未知位置的数值。该方法操作简便、原理直观,在环境科学、地质勘探、气象预测及GIS分析等多个未明确分类的相关领域中都发挥着重要作用。
Inverse Distance Weighting具体释义
Inverse Distance Weighting的英文发音
例句
- 【 Method 】 Data were analyzed with the traditional statistical analysis method, the geo-statistical analysis with semivariogram structure and model fitting, the ordinary Kriging and the inverse distance weighting method.
- 【方法】样品测定结果采用传统统计分析,半方差结构和模型拟合的地统计分析以及结合普通Kriging和反比距离插值方法进行分析。
- The study selects some representative methods of estimation in spatial variables as objects, including Inverse Distance Weighting(IDW) in the traditional space estimation;
- 本研究选取比较有代表性的几种空间估计方法:传统空间插值方法中的反距离加权法和趋势面法;
- Moreover, three estimation methods ( bi-linear interpolation, inverse distance weighting method and the correlation coefficient method ) were compared and the results showed that there was no absolutely optimal way.
- 另外,通过分析对比雨量站点预报降雨的三种估算方法(双线性插值法,反距离加权法和相关系数法),提出了集成双线性插值法和反距离加权法的耦合估算方法。
- Because the root mean square error is more important, ordinary kriging interpolation was better than inverse distance weighting, which was better than simple Kriging method.
- 因为均方根预测误差值更为重要,所以从总体上看普通克里格方法优于反距离加权插值法,而反距离加权法又优于简单克里格方法。
- Improvement Research on Inverse Distance Weighting(IDW) Method Based on Dynamic Voronoi Diagram
- 基于动态Voronoi图的距离倒数加权法的改进研究
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