文献类型： 外文期刊
作者： Zhao, Li 1 ; Hu, Yue-Ming 1 ; Zhou, Wu 1 ; Liu, Zhen-Hua v 1 ; Pan, Yu-Chun 6 ; Shi, Zhou 7 ; Wang, Lu 1 ; Wang, Guang-Xi 1 ;
作者机构： 1.South China Agr Univ, Coll Nat Resources & Environm, Guangzhou 510642, Guangdong, Peoples R China
2.South China Agr Univ, Key Lab Construct Land Transformat, Minist Land & Resources, Guangzhou 510642, Guangdong, Peoples R China
3.South China Agr Univ, Guangdong Prov Key Lab Land Use & Consolidat, Guangzhou 510642, Guangdong, Peoples R China
4.South China Agr Univ, Guangdong Prov Engn Res Ctr Land Informat Technol, Guangzhou 510642, Guangdong, Peoples R China
5.Univ Elect Sci China, Sch Resources & Environm, Chengdu 610054, Sichuan, Peoples R China
6.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
7.Zhejiang Univ, Coll Environm & Resource Sci, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310029, Zhejiang, Peoples R China
8.Southern Illinois Univ Carbondale, Coll Liberal Arts, Dept Geog & Environm Resources, Carbondale, IL 62901 USA
关键词： soil heavy metal mercury content; hyperspectral remote sensing; MLR; BPNN; GA-BPNN
期刊名称：SUSTAINABILITY （ 影响因子：3.251； 五年影响因子：3.473 ）
ISSN： 2071-1050
年卷期： 2018 年 10 卷 7 期
页码：
收录情况： SCI
摘要： Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R-2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R-2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.
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