Spatial interpolation method based on integrated RBF neural networks for estimating heavy metals in soil of a mountain region  被引量:1

一种用于山区土壤重金属评估的集成RBF空间插值算法(英文)

在线阅读下载全文

作  者:李宝磊[1] 张榆锋[1] 施心陵[1] 章克信[2] 张俊华[1] 

机构地区:[1]云南大学信息学院,昆明650091 [2]昆明医学院第二附属医院心血管科,昆明650031

出  处:《Journal of Southeast University(English Edition)》2015年第1期38-45,共8页东南大学学报(英文版)

基  金:The National Natural Science Foundation of China(No.61261007,61062005);the Key Program of Yunnan Natural Science Foundation(No.2013FA008)

摘  要:A novel spatial interpolation method based on integrated radial basis function artificial neural networks (IRBFANNs) is proposed to provide accurate and stable predictions of heavy metals concentrations in soil at un- sampled sites in a mountain region. The IRBFANNs hybridize the advantages of the artificial neural networks and the neural networks integration approach. Three experimental projects under different sampling densities are carried out to study the performance of the proposed IRBFANNs-based interpolation method. This novel method is compared with six peer spatial interpolation methods based on the root mean square error and visual evaluation of the distribution maps of Mn elements. The experimental results show that the proposed method performs better in accuracy and stability. Moreover, the proposed method can provide more details in the spatial distribution maps than the compared interpolation methods in the cases of sparse sampling density.提出了一种在山区能够准确、稳定地预测未采样点土壤重金属浓度的集成径向基函数神经网络空间插值方法(IRBFANNs).该方法集成径向基函数神经网络和神经网络集成技术的优点.为了研究所提IRBFANNs方法的性能,进行了3组不同采样密度条件下的实验.通过M n元素插值的均方根误差和分布估计图对IRBFANNs和其他6个插值方法进行了比较.实验结果表明:IRBFANNs方法在精确性和稳定性方面优于其他参评方法,且在采样密度稀疏条件下该方法能够提供细节较丰富的分布估计图.

关 键 词:integrated radial basis function artificial neuralnetworks spatial interpolation soil heavy metals mountainregion 

分 类 号:TP183[自动化与计算机技术—控制理论与控制工程]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象