Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network
Abstract A quantitative research on the effect of coal mining on the soil organic carbon (SOC) pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining. Moreover, the spatial prediction model of SOC content suit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | International Journal of Coal Science & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40789-023-00588-3 |
| _version_ | 1797818230946922496 |
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| author | Qiangqiang Qi Xin Yue Xin Duo Zhanjun Xu Zhe Li |
| author_facet | Qiangqiang Qi Xin Yue Xin Duo Zhanjun Xu Zhe Li |
| author_sort | Qiangqiang Qi |
| collection | DOAJ |
| description | Abstract A quantitative research on the effect of coal mining on the soil organic carbon (SOC) pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining. Moreover, the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved. Taking the Changhe River Basin of Jincheng City, Shanxi Province, China, as the study area, this paper proposed a radial basis function neural network model combined with the ordinary kriging method. The model includes topography and vegetation factors, which have large influence on soil properties in mining areas, as input parameters to predict the spatial distribution of SOC in the 0–20 and 2040 cm soil layers of the study area. And comparing the prediction effect with the direct kriging method, the results show that the mean error, the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method. Based on the fitting effect of the predicted and measured values, the R 2 obtained by the radial basis artificial neural network are 0.81, 0.70, respectively, higher than the value of 0.44 and 0.36 obtained by the direct kriging method. Therefore, the model combining the artificial neural network and kriging, and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas. |
| first_indexed | 2024-03-13T09:05:05Z |
| format | Article |
| id | doaj.art-b233c50d89a04da4a7caa95695d8a5d3 |
| institution | Directory Open Access Journal |
| issn | 2095-8293 2198-7823 |
| language | English |
| last_indexed | 2024-03-13T09:05:05Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Coal Science & Technology |
| spelling | doaj.art-b233c50d89a04da4a7caa95695d8a5d32023-05-28T11:06:30ZengSpringerOpenInternational Journal of Coal Science & Technology2095-82932198-78232023-05-0110111310.1007/s40789-023-00588-3Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural networkQiangqiang Qi0Xin Yue1Xin Duo2Zhanjun Xu3Zhe Li4Institute of Land Science, College of Resources and Environment, Shanxi Agricultural UniversityInstitute of Land Science, College of Resources and Environment, Shanxi Agricultural UniversityInstitute of Land Science, College of Resources and Environment, Shanxi Agricultural UniversityInstitute of Land Science, College of Resources and Environment, Shanxi Agricultural UniversityInstitute of Land Science, College of Resources and Environment, Shanxi Agricultural UniversityAbstract A quantitative research on the effect of coal mining on the soil organic carbon (SOC) pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining. Moreover, the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved. Taking the Changhe River Basin of Jincheng City, Shanxi Province, China, as the study area, this paper proposed a radial basis function neural network model combined with the ordinary kriging method. The model includes topography and vegetation factors, which have large influence on soil properties in mining areas, as input parameters to predict the spatial distribution of SOC in the 0–20 and 2040 cm soil layers of the study area. And comparing the prediction effect with the direct kriging method, the results show that the mean error, the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method. Based on the fitting effect of the predicted and measured values, the R 2 obtained by the radial basis artificial neural network are 0.81, 0.70, respectively, higher than the value of 0.44 and 0.36 obtained by the direct kriging method. Therefore, the model combining the artificial neural network and kriging, and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas.https://doi.org/10.1007/s40789-023-00588-3Mining areaSoil organic carbonRadial basis function neural networkEnvironmental factorSpatial prediction |
| spellingShingle | Qiangqiang Qi Xin Yue Xin Duo Zhanjun Xu Zhe Li Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network International Journal of Coal Science & Technology Mining area Soil organic carbon Radial basis function neural network Environmental factor Spatial prediction |
| title | Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network |
| title_full | Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network |
| title_fullStr | Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network |
| title_full_unstemmed | Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network |
| title_short | Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network |
| title_sort | spatial prediction of soil organic carbon in coal mining subsidence areas based on rbf neural network |
| topic | Mining area Soil organic carbon Radial basis function neural network Environmental factor Spatial prediction |
| url | https://doi.org/10.1007/s40789-023-00588-3 |
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