基于多通道卷积方式的土壤重金属镍含量定量预测

付承彪,庄清源,田安红

Quantitative prediction of soil heavy metal nickel content based on multi-channel convolution method

Chengbiao FU,Qingyuan ZHUANG,Anhong TIAN

表 2 不同策略下的深度学习模型预测性能对比

Tab.2 Comparative analysis of deep learning model prediction performance under different strategies

模型 $ {{R}}_{\mathrm{c}}^{2} $ $ {\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{c}} $ $ {\mathrm{R}\mathrm{P}\mathrm{D}}_{\mathrm{c}} $ $ {{R}}_{\mathrm{v}}^{2} $ $ {\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{v}} $ $ {\mathrm{R}\mathrm{P}\mathrm{D}}_{\mathrm{v}} $ MTF-ResNet 0.967 38.077 9.525 0.853 114.387 2.604 MTF-VGG 0.930 72.066 4.453 0.796 134.718 2.211 MTF-Inception 0.992 23.284 13.708 0.814 128.470 2.319 MTF-MobileNet 0.921 76.000 4.123 0.770 143.022 2.083 MTF-GASF-ResNet 0.877 95.593 4.054 0.872 106.379 2.800 MTF-GASF-VGG 0.866 87.313 3.803 0.839 119.718 2.488 MTF-GASF-Inception 0.919 53.504 7.521 0.833 121.777 2.446 MTF-GASF-MobileNet 0.952 54.571 6.354 0.799 133.530 2.231 MC-ResNet 0.920 77.850 4.681 0.901 93.573 3.184 MC-VGG 0.909 73.914 4.888 0.850 115.264 2.585 MC-Inception 0.919 74.952 4.221 0.854 113.773 2.618 MC-MobileNet 0.942 61.496 5.096 0.844 117.841 2.528