基于海鸥算法优化随机森林的土壤硒含量高光谱反演

Hyperspectral Inversion of Soil Selenium Content Based on Seagull Algorithm Optimized Random Forest

针对土壤硒含量光谱数据冗余、模型复杂度较高等问题,本研究系统采集含硒土壤若干份,并获取样本硒含量和光谱信息,对原始光谱进行平滑多元散射校正一阶微分(SG-MSC-FD)光谱增强处理,利用稳定性竞争自适应重加权采样(sCARS)等特征提取算法筛选特征波长,建立土壤硒含量的偏最小二乘(PLSR)、支持向量机(SVM)、随机森林(RF)、海鸥优化随机森林(SOA-RF)预测模型,通过对比不同特征筛选下模型的决定系数(R2)、均方根误差(RMSE)和相对分析误差(RPD),寻找最佳的组合模型。结果表明:不同特征筛选下的模型精度均有较大提升,其中变量组合集群分析法结合遗传算法(VCPA-GA)精度最高,sCARS算法提取的变量数最少,仅占全波段的0.49%;RF较SVM和PLSR模型有更好的鲁棒性,SOA-RF模型的参数最佳,极大地提升了模型的反演精度。综上,经VCPA-GA特征提取下的SOA-RF模型是最佳的预测模型(R2=0.92、RMSE为0.08、RPD为2.911),该模型能够实现对土壤硒含量快速、高效反演。

The aim of this study is to investigate the problem of redundant soil selenium content spectral data and high model complexity.Several selenium-containing soil samples were collected,and the selenium content and spectral information of the samples were obtained,The raw spectra were preprocessed using Savizkg-Golag multivariate scatter correction first-order differential(SG-MSC-FD),and the feature wavelengths were screened using stability competitive adaptive reweighted sampling(sCARS)and other algorithms to establish the partial least squares regression(PLSR),support vector machine(SVM),random forest(RF),soil selenium-content seagull optimization algorithm(SOA)-RF prediction models.The coefficient of regression(R2),root mean square error(RMSE)and relative predictive deviation(RPD)values of the models under different feature screenings were compared to determine the best combination model.The results show that the accuracy of the models under different feature filtering is improved.The sCARS algorithm extracts the least number of variables,accounting for only 0.49%of the full band,and the algorithm combined variable combination cluster analysis and genetic algorithm has the highest accuracy.The RF model exhibits better robustness than the SVM and PLSR models,and the inversion accuracy of the models significantly improves with parameter optimization of SOA-RF.In summary,the SOA-RF model with VCPA-GA feature extraction is the best prediction model(R2=0.92,RMSE is 0.08,RPD is 2.911),and it can achieve rapid and efficient inversion of soil selenium content.