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首页> 外文期刊>Paddy and Water Environment >Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan
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Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan

机译：利用台湾地区中部距离反演加权法估计空间降雨分布

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摘要

In this article, we used the inverse distance weighting (IDW) method to estimate the rainfall distribution in the middle of Taiwan. We evaluated the relationship between interpolation accuracy and two critical parameters of IDW: power (α value), and a radius of influence (search radius). A total of 46 rainfall stations and rainfall data between 1981 and 2010 were used in this study, of which the 12 rainfall stations belonging to the Taichung Irrigation Association (TIA) were used for cross-validation. To obtain optimal interpolation data of rainfall, the value of the radius of influence, and the control parameter-α were determined by root mean squared error. The results show that the optimal parameters for IDW in interpolating rainfall data have a radius of influence up to 10–30 km in most cases. However, the optimal α values varied between zero and five. Rainfall data of interpolation using IDW can obtain more accurate results during the dry season than in the flood season. High correlation coefficient values of over 0.95 confirmed IDW as a suitable method of spatial interpolation to predict the probable rainfall data in the middle of Taiwan.
机译：在本文中，我们使用了逆距离加权（IDW）方法来估算台湾中部的降雨分布。我们评估了插值精度与IDW的两个关键参数之间的关系：功率（α值）和影响半径（搜索半径）。本研究使用了1981年至2010年之间的46个降雨站和降雨数据，其中使用了台中灌溉协会（TIA）的12个降雨站进行交叉验证。为了获得最佳的降雨插值数据，通过均方根误差确定影响半径的值和控制参数-α。结果表明，在大多数情况下，内插降雨数据中IDW的最佳参数的影响半径最大为10-30 km。但是，最佳α值在零到五之间变化。与干旱季节相比，在干旱季节使用IDW进行插值的降雨数据可以获得更准确的结果。高相关系数值超过0.95证实了IDW是一种适用于空间插值的预测台湾中部可能降雨数据的方法。

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