摘要
以云南省天星站和坡脚站10、20、40 cm 3个土层的土壤含水量观测数据为基础,通过改进时变滤波经验模态分解(TVFEMD)和快速学习网(FLN)方法构建基于多种优化算法的预测模型(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN),提升土壤含水量时间序列预测精度。通过比较各优化算法的模型性能,为土壤水分预测提供更优的建模方法。结果表明,TVFEMD分解效果主要受带宽阈值和B样条阶数2个关键参数影响。采用IVYA算法优化这2个参数可提升时间序列分解质量,进而改善模型预测性能。TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在训练集上表现出卓越的预测性能,其平均绝对百分比误差(MAPE)为0.002%~0.077%,决定系数(R^(2))为0.9997~1.0000;预测集中的MAPE为0.006%~0.459%,R^(2)为0.9966~1.0000。与TVFEMD-PSO-FLN模型相比,TVFEMD-BLSO/AO/IVYA/EGO-FLN模型在拟合性能和预测精度方面均有明显提升。采用BLSO、AO、IVYA和EGO算法优化FLN超参数可有效提升模型性能,其中IVYA算法的优化效果较突出。
Based on the observed soil moisture content data from 10,20,and 40 cm soil layers at Tianxing and Pojiao stations in Yunnan Province,a prediction model(TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN)was constructed by improving the time-varying filter empirical mode decomposition(TVFEMD)and fast learning network(FLN)methods to enhance the time-series prediction accuracy of soil moisture content.By comparing the performance of different optimization algorithms,a superior modeling approach was provided for soil moisture prediction.The results showed that the TVFEMD decomposition performance was primarily influenced by two key parameters:Bandwidth threshold and B-spline order.Optimizing these two parameters using the IVYA algorithm improved the time-series decomposition quality and further enhanced the model’s prediction performance.The TVFEMD-BLSO/AO/IVYA/EGO-FLN model demonstrated outstanding prediction performance on the training set,with a mean absolute percentage error(MAPE)of 0.002%~0.077%and a coefficient of determination(R^(2))of 0.9997~1.0000.The MAPE in the prediction set was 0.006%~0.459%,and R^(2)was 0.9966~1.0000.Compared with the TVFEMD-PSO-FLN model,the TVFEMD-BLSO/AO/IVYA/EGO-FLN model showed significant improvements in both fitting performance and prediction accuracy.Optimizing FLN hyperparameters using BLSO,AO,IVYA,and EGO algorithms effectively improved model performance,with the IVYA algorithm exhibiting the most notable optimization effect.
作者
田宇
崔东文
TIAN Yu;CUI Dong-wen(Yunnan Institute of Water&Hydropower Engineering Investigation,Design and Research,Kunming 650021,China;Wenshan Zhuang and Miao Autonomous Prefecture Water Bureau,Wenshan 663000,Yunnan,China)
出处
《湖北农业科学》
2025年第5期147-154,共8页
Hubei Agricultural Sciences
基金
国家自然科学基金项目(41702278)
中国地质调查局地质调查项目(DD20221758
DD20190326)。
