不同插值方法在土地整理工程中测定农业土壤指数的比较

IF 3.1 Q2 ENGINEERING, GEOLOGICAL International Journal of Engineering and Geosciences Pub Date : 2019-02-01 DOI:10.26833/IJEG.422570
Mevlut Uyan
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引用次数: 7

摘要

土地整理(LC)是提高农业区域加工效率和促进农村发展的工具,同时也是促进可持续农业不可或缺的应用。为了实现LC后的重新分配过程,确定每个农业地块的土壤指数(SI)的正确性对LC项目的成功至关重要。如今,插值方法被广泛应用于映射过程中,以估计未采样点的SI。本研究的目的是评估和比较三种插值方法在LC项目中的农业SI值图和GIS技术的性能。SI数据是从132个观测点确定的。利用三种空间插值方法——普通克里格(OK)、反距离加权(IDW)和径向基函数(RBF)对农业SI值进行建模。结果表明,所有方法都对SI的平均浓度提供了很高的预测精度。在本研究中,尽管表现最好的插值方法是OK,但结果表明,三种方法的性能略有不同。结果表明,所有方法都具有良好的估计性能,RMSE(均方根误差)和ME(平均误差)接近0%。
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COMPARISON OF DIFFERENT INTERPOLATION TECHNIQUES IN DETERMINING OF AGRICULTURAL SOIL INDEX ON LAND CONSOLIDATION PROJECTS
Land consolidation (LC) is a tool to improve the processing efficiency of agricultural area and the promotion of rural development same time an indispensable application for the promotion of sustainable agriculture. In order to achieve the reallocation process after LC, determining the correct of soil index (SI) for each of the agricultural parcels is very important for the success of LC projects. Nowadays, interpolation methods are extensively applied in the mapping processes to estimate the SI at unsampled sites. The objective of this study was to evaluate and compare the performance of three interpolation methods for the agricultural SI values maps with GIS technology for LC projects. The SI data were determined from 132 observation points. Three spatial interpolation methods Ordinary Kriging (OK), Inverse Distance Weighted (IDW), and Radial Basis Functions (RBFs) were utilized for modeling the agricultural SI values. The results indicated that all methods provided a high prediction accuracy of the mean concentration of SI.In this study, although the best performed interpolation method was the OK, the results showed that the performance differed slightly among three methods. Results show that all the methods present a good performance in the estimation with RMSE (root mean square error) and ME (mean error) close to 0%.
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来源期刊
CiteScore
4.00
自引率
0.00%
发文量
12
审稿时长
30 weeks
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