摘要
【目的】利用自然成土作用变量,预测并制作栅格化的土壤有机质分布图,对发展热带数字化精细农业具有重要意义。【方法】使用2006年云南省景洪市测土配方样点数据,应用BP神经网络(BPNN)、基于强分类器算法的BP神经网络模型(BPNN-Ada)、基于粒子群算法优化的BP神经网络(PSO-BPNN)、基于遗传算法优化的BP神经网络(GA-BPNN)和多元线性回归(MLR)对土壤有机质的含量预测并进行空间化制图。【结果】①土壤样点X、Y坐标值能够有效提高算法精度且充分表现环境因子与土壤有机质空间分布上的协同关系。②4种神经网络算法预测结果土壤有机质空间分布基本类似,均呈现南高北低的趋势。③研究区域内4种神经网络模型的在建模集拟合程度从高至低依此次为:BPNN-Ada>GA-BPNN>PSO-BPNN>BPNN,在建模集中PSO-BPNN和GA-BPNN与BPNN拟合精度一致,BPNN-Ada的拟合精度R2最高为0.98。在验证集的预测能力由高至低依次为:BPNN-Ada>GA-BPNN>PSO-BPNN>BPNN。BPNN-Ada有着最高的预测精度和算法稳定性:RMSE=4.47、MAE=3.3、MRE=0.05、R^(2)=0.976。【结论】在景洪地区进行土壤有机质神经网络建模时加入地理坐标能够有效提高模型精度,且基于学习规则的神经网络优化算法效果要优于优化初始权重和阈值的神经网络算法及传统的BPNN算法。
[Objective]The aims were to predict and make grid soil organic matter(SOM)distribution map by using natural soil forming variables,in order to develop the tropical digital precision agriculture.[Method]Based on the data of soil testing formula samples in Jinghong City,Yunnan Province in 2006,BP neural network(BPNN),BP neural network model based on strong classification algorithm(BPNN-Ada),BP neural network based on particle swarm optimization(PSO-BPNN),BP neural network based on genetic algorithm(GA-BPNN)and multiple linear regression(MLR)were used to predict and spatially map the SOM content.[Result]①The X and Y coordinate values of soil samples could improve the accuracy of the algorithm effectively,and express the cooperative relationship between environmental factors and the spatial distribution of SOM.②The spatial distribution of SOM predicted by the four neural network algorithms was basically similar,showing a trend of high in the South and low in the north.③The fitting degree of the four neural network models in the modeling set in the study area from high to low followed:BPNN-Ada>GA-BPNN>PSO-BPNN>BPNN.In the modeling set,the fitting accuracy of pao-bpnn and GA-BPNN was consistent with that of BPNN,and the highest fitting accuracy R2 of BPNN ADA was 0.98.In the verification set,the prediction ability from high to low followed:BPNN-Ada>GA-BPNN>PSO-BPNN>BPNN.BPNN-Ada had the highest prediction accuracy and algorithm stability,and the order was:RMSE=4.47,MAE=3.3,MRE=0.05,R^(2)=0.98.[Conclusion]The addition of geographical coordinates to the neural network modeling of SOM in Yunnan can effectively improve the accuracy of the model,and the neural network optimization algorithm based on learning rules is better than the neural network algorithm that optimizes initial weights and thresholds and the traditional BPNN algorithm.
作者
杨晋帆
杨阳
刘婷婷
周汝良
王艳霞
叶江霞
YANG Jin-fan;YANG Yang;LIU Ting-ting;ZHOU Ri-liang;WANG Yan-xia;YE Jiang-xia(School of Geography and Ecotourism,Southwest Forestry University,Kunming 650224,China;College of Forestry,Southwest Forestry University,Kunming 650224,China)
出处
《土壤通报》
CAS
CSCD
北大核心
2023年第2期273-285,共13页
Chinese Journal of Soil Science
基金
国家自然科学基金(31860213、42061004)
云南省重大科技专项(202002AA100007)资助。
关键词
土壤有机质
热带土壤
土壤属性制图
机器学习
Soil organic matter
Tropical soil
Mapping of soil properties
Machine learning
