基于ArcGIS不同空间插值方法的降雨量预测效果对比

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英文篇名：A Comparison of Precipitation Prediction Effect Based on Different Spatial Interpolation Methods in ArcGIS 作者：黄利燕 英文作者：HUANG Liyan;Surveying and Mapping Institute Lands and Resource Department of Guangdong Province; 关键词：ArcGIS ; 降雨量 ; 空间插值 ; 效果对比 ; 交叉验证 英文关键词：precipitation;;spatial interpolation;;effect comparison;;cross validation 中文刊名：北京测绘 英文刊名：Beijing Surveying and Mapping 机构：广东省国土资源测绘院; 出版日期：2019-07-15 出版单位：北京测绘 年：2019 期：07 语种：中文; 页：23-26 页数：4 CN：11-3537/P ISSN：1007-3000 分类号：P457.6;P208

摘要

利用美国Idaho州的实测年均降雨量数据,基于ArcGIS平台分别采用趋势面拟合、IDW、Splines和普通Kriging四种插值方法进行了降雨量预测建模,使用留出验证法对所建的四种模型实施了预测效果评估。基于20个预留站点的验证结果显示:趋势面拟合、IDW、Splines和普通Kriging四种插值方法的拟合R2分别为0.74,0.71,0.83,0.81;RMSE分别为5.17,5.30,4.11,4.37in。即在该例中Splines插值模型的预测效果最优,而后基于Splines插值模型生成了美国Idaho州年均降雨量分布图。
        Trend surface fitting,inverse distance weight,thin-plate splines and ordinary kriging interpolation methods were carried out in ArcGIS to model the distribution of precipitation in Idaho,US based on annual averaged precipitation data.Hold-out validation was used to evaluate prediction effect of these four methods.The validation result based on twenty hold-out stations showed that:the fitting R2 and RMSE of these four models were 0.74,0.71,0.83,0.81 and 5.17,5.30,4.11,4.37 in respectively.The model of Splines interpolation was the best method in this study.Then,a distribution map of Idaho's annual precipitation was generated using Splines interpolation method in ArcGIS.

引文

[1]任志勇.辽宁省某市降雨量的空间插值算法比较研究[J].北京测绘,2017(5):164-166.
    [2]孔令娜,向南平.基于ArcGIS的降水量空间插值方法研究[J].测绘与空间地理信息,2012,35(3):123-126.
    [3]周锁铨,薛根元,周丽峰,等.基于GIS降水空间分析的逐步插值方法[J].气象学报,2006,64(1):102-113.
    [4]秦俊桃,冯绍元,霍再林,等.几种地下水位空间插值方法在干旱内陆区的应用比较[J].中国农业大学学报,2010,15(5):124-129.
    [5]李广春,戴吾蛟,曾凡河,等.三维动态趋势面模型在地面沉降分析中的应用[J].大地测量与地球动力学,2016,36(6):508-512.
    [6]彭思岭.气象要素空间插值方法优化研究[J].地理空间信息,2017,15(7):86-89.
    [7]王琦,季民,曹品廉,等.矿山开采沉陷预计空间插值方法分析研究[J].北京测绘,2014(5):1-8.
    [8]刘正佳,于兴修,王丝丝,等.薄盘光滑样条插值中三种协变量方法的降水量插值精度比较[J].地理科学进展,2012,31(1):56-62.
    [9]陈欢,谢健.基于OpenMP的Kriging插值算法研究[J].计算机科学,2012(b06):392-395.
    [10]Yadav S,Shukla S.Analysis of k-Fold Cross-Validation over Hold-out Validation on Colossal Datasets for Quality Classification IEEE,International Conference on Advanced Computing[C]∥IEEE,2016.