首页> 外文期刊>Journal of Science and Technology of Agriculture and Natural Resources >Estimating Soil Cation Exchange Capacity (in View of Pedotransfer Functions) Using Regression and Artificial Neural Networks and the Effect of Data Partitioning on Accuracy and Precision of Functions
【24h】

Estimating Soil Cation Exchange Capacity (in View of Pedotransfer Functions) Using Regression and Artificial Neural Networks and the Effect of Data Partitioning on Accuracy and Precision of Functions

机译:使用回归和人工神经网络估算土壤阳离子交换容量(从Pedotransfer函数来看)以及数据划分对函数准确性和精确度的影响

获取原文

摘要

Soil fertility measures such as cation exchange capacity (CEC) may be used in upgrading soil maps and improving their quality. Direct measurement of CEC is costly and laborious. Indirect estimation of CEC via pedotransfer functions, therefore, may be appropriate and effective. Several delineations of two consociation map units consisting of two soil families, Shahrak series and Chaharmahal series, located in Shahrekord plain were identified. Soil samples were taken from two depths of 0-20 and 30-50 cm and were analyzed for several physico-chemical properties. Clay and organic matter percentages as well as moisture content at -1500 kPa correlated best with CEC. Pedotransfer functions were successfully developed using regression and artificial neural networks. In this research, it seemed that one hidden layer with one node was sufficient for all neural networks models. The best regression model consisting of organic matter and clay variables showed R2=0.81 and RMSE=7.2 while best corresponding neural network with a learning coefficient of 0.3 and an epoch of 40 had R2=0.88 and RMSE=0.34. Data partitioning according to soil series and soil depths increased the accuracy and precision of the functions. Compared to regression, artificial neural network technique gave pedotransfer functions with greater R2 and smaller RMSE. Keywords: Cation exchange capacity (CEC), Pedotransfer, Regression, Artificial neural network, Soil partitioning. Full-Text Type of Study: Research | Subject: Ggeneral Received: 2010/02/23 Related Websites Scientific Publications Commission - Health Ministry Scientific Publications Commission - Science Ministry Yektaweb Company Site Keywords ?????, Academic Journal, Scientific Article, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ?? Vote ? 2015 All Rights Reserved | JWSS - Isfahan University of Technology
机译:土壤肥力措施,例如阳离子交换能力(CEC),可用于升级土壤图并改善其质量。直接测量CEC既费钱又费力。因此,通过pedotransfer函数间接估计CEC可能是适当且有效的。确定了位于Shahrekord平原的由两个土壤家族Shahrak系列和Chaharmahal系列组成的两个关联图单元的几个轮廓。从两个深度为0-20和30-50 cm的土壤中取样,并分析了几种理化性质。粘土和有机物的百分比以及-1500 kPa的水分含量与CEC的相关性最好。使用回归和人工神经网络成功开发了Pedotransfer函数。在这项研究中,似乎一个具有一个节点的隐藏层足以满足所有神经网络模型的需求。由有机质和黏土变量组成的最佳回归模型显示R2​​ = 0.81,RMSE = 7.2,而学习系数为0.3,历元为40的最佳对应神经网络的R2 = 0.88,RMSE = 0.34。根据土壤系列和土壤深度进行数据划分提高了功能的准确性和精确性。与回归相比,人工神经网络技术提供了具有更大的R2和更小的RMSE的pedotransfer函数。关键字:阳离子交换容量(CEC),Pedotransfer,回归,人工神经网络,土壤分配。全文研究类型:研究|主题:一般收稿日期:2010/02/23相关网站科学出版物委员会-卫生部科学出版物委员会-科学部Yektaweb公司网站关键字??????,Academic Journal,Scientific Article,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ??投票吗? 2015版权所有| JWSS-伊斯法罕工业大学

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号