基于Boruta算法和GA优化混合地统计模型的土壤有机质空间分布预测

doi:10.11720/wtyht.2024.1114

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Abstract

Establishing a spatial prediction model for soil organic matter (SOM) can accurately predict the spatial distribution of SOM content, playing a significant role in scientific soil management and ecosystem service enhancement. Focusing on the soils in Yonghe County, Linfen City, Shanxi Province, this study extracted topographic factors and vegetation indices from the digital elevation model (DEM) and vegetation remote sensing data. With soil attributes as variable factors, this study, using the Boruta algorithm, selected the characteristic variablescorrelating strongly with SOM from variable factors as auxiliary variables. These auxiliary variables were used as model inputand the measured SOM values as model output.The SOM content in samples in the training set was predicted usingthe ordinary Kriging (OK)method, the back propagation neural network (BPNN), the genetic algorithm-optimized BPNN (GA-BPNN), and the improved BPNN combined with the geostatistical method (the GA-BPNN-OK method) separately. The prediction accuracy was comparatively analyzed based on samples in the validation set. The results show that: (1)The Boruta algorithm ranked the selected characteristic variables in order of importance, obtaining the sequence of total nitrogen >topographic wetness index (TWI) > elevation > slope > normalized difference vegetation index (NDVI) > enhanced vegetation index (EVI); (2)Despite local differences,the SOM prediction results obtained using the four methods exhibited roughly the same overall spatial distribution: low in the western and southwestern portions of the study areabut high in the eastern and southeastern portions;(3)Compared to the other three models, the GA-BPNN-OK model demonstrated more distinct low- and high-value areas in the predicted SOM distribution. (4) As revealed by the comparison of prediction accuracy indices, the GA-BPNN-OK method yielded a minimum root mean square error (RMSE) of 0.059, a minimum mean absolute error (MAE) of 0.240,a minimum mean relative error (MRE) of 0.165, and a maximum fitting coefficient (R²) of 0.78. To verify the effects of the Boruta algorithm in improving model accuracy, global variables, as well as the variables determined through characteristic selection, were used as the model inputof the GA-BPNN method. The comparison of the prediction results indicates that the Boruta algorithm reduced the model error. Therefore, the Boruta algorithm and the GA-BPNN-OK method constitute the optimal prediction model for the spatial distribution of SOM content.

Key words： soil organic matter (SOM) BPNN GA-BPNN feature selection GA-BPNN-OK

Received: 14 March 2023 Published: 27 June 2024

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	Articles by authors
	Peng-Li GAO
	Da-Lu REN
	Chao-Hui LI
	Zhi-Qiang FENG
	Hong-Yun MIAO
	Lin QIAO
	Jian-Wu WANG
	Yong-Liang YANG
	Li-Ming ZHANG
	Guang-Hui LI

Cite this article:

Peng-Li GAO,Da-Lu REN,Chao-Hui LI, et al. Predicting the spatial distribution of soil organic matter using the model consisting of the Boruta algorithm and the optimized GA combined with the geostatistical method[J]. Geophysical and Geochemical Exploration, 2024, 48(3): 747-758.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2024.1114 OR https://www.wutanyuhuatan.com/EN/Y2024/V48/I3/747

General situation of the study area and distribution of soil samples

GA-BPNN (a) and BPNN (b) flow chart

Technology roadmap

Descriptive statistics of SOM content

Feature selection results of Boruta algorithm

SOM,GA-BPNN and residual semi-variogram parameters

Statistical characteristics of prediction results of soil organic matter content

Spatial distribution of soil organic matter content in Yonghe County, Shanxi Province

Statistical analysis of accuracy of each prediction model

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