Yin Guangcai, Chen Xingling, Zhu Hanghai, Chen Zhiliang, Su Chuanghong, He Zechen, Qiu Jinrong, Wang Tieyu
Guangdong Industrial Contaminated Site Remediation Technology and Equipment, Engineering Research Center, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Research center for eco-environment restoration technology, South China Institute of Environmental Science, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510006, China.
Sci Total Environ. 2022 Jun 15;825:153948. doi: 10.1016/j.scitotenv.2022.153948. Epub 2022 Feb 24.
To improve the prediction accuracy of soil heavy metals (HMs) by spatial interpolation, a novel interpolation method based on genetic algorithm and neural network model (GANN model), which integrates soil properties and environmental factors, was proposed to predict the soil HM content. Eleven soil HMs (Cu, Pb, Zn, Cd, Ni, Cr, Hg, As, Co, V and Mn) were predicted using the GANN model. The results showed that the model had a good prediction performance with correlation coefficients (R) varying from 0.7901 to 0.9776. Compared with other traditional interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), and spline with barriers interpolation (SBI) methods, the GANN model had a relatively lower root mean square error value, ranging from 0.0497 to 77.43, suggesting that the GANN model might be a more accurate spatial interpolation method and the soil properties together with the environmental geographical factors played key roles in prediction of soil HMs.
为提高土壤重金属（HMs）空间插值预测精度，提出一种基于遗传算法和神经网络模型的新型插值方法（GANN模型），该方法整合土壤性质和环境因素来预测土壤重金属含量。利用GANN模型对11种土壤重金属（铜、铅、锌、镉、镍、铬、汞、砷、钴、钒和锰）进行预测。结果表明，该模型具有良好的预测性能，相关系数（R）在0.7901至0.9776之间变化。与其他传统插值方法，包括反距离加权（IDW）、普通克里金（OK）、泛克里金（UK）和带障碍样条插值（SBI）方法相比，GANN模型的均方根误差值相对较低，范围为0.0497至77.43，这表明GANN模型可能是一种更精确的空间插值方法，且土壤性质与环境地理因素在土壤重金属预测中起关键作用。
