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Digital mapping of soil organic matter using a particle swarm optimization algorithm and machine learning algorithms

文献类型： 外文期刊

作者： Zhao, Yue 1 ; Zhang, Zhi 2 ; Chen, Yunfeng 2 ; Hu, Cheng 2 ; Luo, Zhijun 1 ;

作者机构： 1.Jiangxi Agr Univ, Coll Land Resources & Environm, Nanchang 330045, Peoples R China

2.Hubei Acad Agr Sci, Inst Plant Protect & Soil Fertilizer, Key Lab Fertilizat Agr Wastes, Minist Agr & Rural Affairs, Wuhan 430064, Peoples R China

关键词： Particle swarm optimization algorithm; Machine learning algorithms; Digital soil mapping; Soil organic matter

期刊名称：JOURNAL OF SOILS AND SEDIMENTS （ 影响因子：3.0； 五年影响因子：3.4 ）

ISSN： 1439-0108

年卷期： 2025 年 25 卷 7 期

页码：

收录情况： SCI

摘要： Purpose Soil organic matter (SOM) is an important soil quality parameter and plays a central role in soil nutrient cycling, aggregate stability and water retention. This study aims to investigate the effectiveness of different machine learning algorithms in the simulation of SOM in agricultural soils and to explore the role of environmental factors in SOM simulation. Materials and methods In this study, 223 soil sampling points were obtained, and the topography, the Normalized Difference Vegetation Index (NDVI) and SOM information at adjacent points were selected as environmental factors. The spatial simulation and prediction of SOM were carried out using the particle swarm optimization-back propagation neural network (PSO-BPNN), particle swarm optimization-random forest (PSO-RF), particle swarm optimization-support vector machine (PSO-SVM), backpropagation neural network (BPNN), random forest (RF), and support vector machine (SVM) methods. Results and discussion The validity of the models was verified by MAE, MRE, RMSE, MAPE and R-2, and the prediction accuracies of the six models were PSO-SVM > PSO-BPNN > PSO-RF > RF > SVM > BPNN in descending order. The particle swarm algorithm significantly improved the computational accuracies of the SVM, BPNN and RF models, in which the R-2 values increased by 16.36%, 18.87% and 3.28%, respectively. The PSO algorithm was found to play an optimization role in the BPNN, RF and SVM methods, speeding up the process of obtaining the optimal solution and improving the speed of iterative convergence and the prediction efficiency. Conclusions The combination of the particle swarm algorithm and SVM, BPNN and RF modeling achieved good results in the spatial prediction of SOM in the cultivated land of the plain area. As a result, our findings aid in optimizing soil sample density and serve as a foundation for future SOM digital soil mapping approaches (DSM) in comparable regions.

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