PMO OpenIR
Black Soil Organic Matter Content Estimation Using Hybrid Selection Method Based on RF and GABPSO
Ma Yue1; Jiang Qi-gang1; Meng Zhi-guo1,2; Liu Hua-xin1
2018
Source PublicationSPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN1000-0593
Volume38Issue:1Pages:181-187
Corresponding AuthorJiang Qi-gang(jiangcg417@163.com)
AbstractTo solve the problem of high-dimensional variables and characteristic wavelengths selection on soil organic matter content estimation using hyperspectral data, a hybrid feature selection method that combined random forest and self-adaptive searching method was proposed. In this hybrid method, random forest was employed to select spectral variables as the preliminary optimal dataset, which had great importance in the modeling process. The wrapper approach which combined genetic algorithm and binary particle swarm optimization was used as the self-adaptive searching algorithm to further search variables in the preliminary dataset. As for the prediction model, random forest was picked on because of the strong robustness and the excellent performance of dealing with high-dimensional variables. In this paper, the soil samples collected in the typical black soil region were used as the research object, and the Vis-NIR spectral data of the soil obtained from ASD spectrometer and the organic matter content through chemical analysis were used as the data sources. Following reflectance transformation and spectral resampling, the proposed hybrid selection method was employed to extract the characteristic spectral regions that were used as the input data for random forest. The prediction accuracy was compared with the results from random forest algorithm with the spectral datasets which were respectively extracted by no-selected method, only random forest method and only self-adaptive searching method. The results showed that using random forest model with the characteristic wavelengths extracted by proposed method obtained the highest predicted accuracy, in which the R-2, RMSE and the RPD were 0. 838, 0. 54% and 2. 534, respectively. Moreover, the proposed method was more efficient to selected features than other approaches. It can be concluded that the hybrid feature selection method and random forest algorithm can be effectively applied to black soil organic matter content estimation using hyperspectral data and it also provides a reference for solving the problem of variables selection and modeling on other types of soil organic matter content estimation.
KeywordHyperspectral Black soil organic matter content Genetic algorithm Binary particle swarm optimization Random forest
DOI10.3964/j.issn.1000-0593(2018)01-0181-07
WOS KeywordMODEL ; SPECTROSCOPY ; SPECTRA
Indexed BySCI
Language英语
WOS Research AreaSpectroscopy
WOS SubjectSpectroscopy
WOS IDWOS:000423649200034
PublisherOFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
Citation statistics
Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/22239
Collection中国科学院紫金山天文台
Corresponding AuthorJiang Qi-gang
Affiliation1.Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun 130026, Jilin, Peoples R China
2.Chinese Acad Sci, Key Lab Planetary Sci, Shanghai 200030, Peoples R China
Recommended Citation
GB/T 7714
Ma Yue,Jiang Qi-gang,Meng Zhi-guo,et al. Black Soil Organic Matter Content Estimation Using Hybrid Selection Method Based on RF and GABPSO[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2018,38(1):181-187.
APA Ma Yue,Jiang Qi-gang,Meng Zhi-guo,&Liu Hua-xin.(2018).Black Soil Organic Matter Content Estimation Using Hybrid Selection Method Based on RF and GABPSO.SPECTROSCOPY AND SPECTRAL ANALYSIS,38(1),181-187.
MLA Ma Yue,et al."Black Soil Organic Matter Content Estimation Using Hybrid Selection Method Based on RF and GABPSO".SPECTROSCOPY AND SPECTRAL ANALYSIS 38.1(2018):181-187.
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