黄赵麟, 丁懿, 王君櫹, 贾振毅, 曾菁菁, 周生路. 基于多模型优选的区域土壤重金属含量空间预测方法研究[J]. 生态与农村环境学报, 2020, 36(3): 308-317. DOI: 10.19741/j.issn.1673-4831.2019.0326
    引用本文: 黄赵麟, 丁懿, 王君櫹, 贾振毅, 曾菁菁, 周生路. 基于多模型优选的区域土壤重金属含量空间预测方法研究[J]. 生态与农村环境学报, 2020, 36(3): 308-317. DOI: 10.19741/j.issn.1673-4831.2019.0326
    HUANG Zhao-lin, DING Yi, WANG Jun-xiao, JIA Zhen-yi, ZENG Jing-jing, ZHOU Sheng-lu. Spatial Prediction Method of Regional Soil Heavy Metals Content Based on Multiple Model Optimization[J]. Journal of Ecology and Rural Environment, 2020, 36(3): 308-317. DOI: 10.19741/j.issn.1673-4831.2019.0326
    Citation: HUANG Zhao-lin, DING Yi, WANG Jun-xiao, JIA Zhen-yi, ZENG Jing-jing, ZHOU Sheng-lu. Spatial Prediction Method of Regional Soil Heavy Metals Content Based on Multiple Model Optimization[J]. Journal of Ecology and Rural Environment, 2020, 36(3): 308-317. DOI: 10.19741/j.issn.1673-4831.2019.0326

    基于多模型优选的区域土壤重金属含量空间预测方法研究

    Spatial Prediction Method of Regional Soil Heavy Metals Content Based on Multiple Model Optimization

    • 摘要: 土壤重金属含量空间预测研究对实现区域土壤资源的优化利用以及土壤环境的保护和污染防治具有重要意义。以江苏省常州市金坛区为例,基于源、汇和空间分异因子,利用BP神经网络(back-propagation network)建模方法,分别构建了源汇模型(BP-S)、空间分异模型(BP-K)和改进的多因素综合模型(BP-SK),模拟预测了区域土壤重金属Cd、Pb、Cr、Cu和Zn含量的空间分布,并对各模型预测精度进行对比分析。针对不同模型在不同区域和元素间预测精度的差异,优选出预测精度最高的模型组合,以此探求区域重金属含量空间分布最优预测结果。结果表明:(1)BP-SK模型对Cd、Cr、Cu和Zn含量预测精度均高于BP-S和BP-K模型,仅在对Pb含量的预测中BP-S、BP-K模型精度高于BP-SK模型,BP-SK模型比其他模型更能突出局部特征,包含的信息更丰富。(2)优选后最优模型预测精度较原单一模型均有不同程度的提高,对Cd、Pb、Cr、Cu和Zn含量的预测精度分别提高15.15%、20.71%、19.19%、1.75%和9.24%。(3)各模型对Cd、Pb、Cr、Cu和Zn含量的空间预测高值区均位于区域中部和东北部,低值区位于西部丘陵山区,BP-SK模型在人为影响剧烈的地区预测效果更好,而BP-K模型在自然因素影响较大的丘陵山地区的适用性更好。

       

      Abstract: Spatial prediction of soil heavy metals is of great significance for utilization of regional soil resources, environment protection and pollution prevention. Jintan District in Changzhou City, Jiangsu Province was chosen as study area. Spatial variation model (BP-K), source-sink model (BP-S) and comprehensive model (BP-SK) were applied to simulate and predict the spatial distribution of heavy metals, including Cd, Pb, Cr, Cu and Zn. The results of the Models' performance were evaluated by coefficient of determination, root-mean-square deviation, mean absolute error and so on. Furthermore, the prediction results from the best performance model were selected to calculate heavy metal spatial distribution in each sub-region. The results show that:(1) The coefficient of determination of BP-SK model was higher than that of BP-S and BP-K for Cd, Cr, Cu and Zn. Only for Pb, the coefficients of determination of BP-S and BP-K were higher than that of the BP-SK model. BP-SK can strengthen local characteristics and uncover more information. (2) The comprehensive model performed better than original single model. The root-mean-square deviation of Cd, Pb, Cr, Cu and Zn were improved by 15.15%, 20.71%, 19.19%, 1.75% and 9.24%, respectively. (3) In prediction results, the high-value areas for Cd, Pb, Cr, Cu, and Zn were located in the central and northeastern parts, and the low-value areas were located in west hilly and mountainous areas. The BP-SK model has a better performance in areas with severe human influence, while the BP-K model has better applicability in hilly area where natural factors have a greater impact.

       

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