TVFEMD尋優(yōu)分解與智能算法優(yōu)化的FLN土壤 含水量預(yù)測(cè)

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關(guān)鍵詞：時(shí)變?yōu)V波經(jīng)驗(yàn)?zāi)B(tài)分解（TVFEMD）；算法優(yōu)化;快速學(xué)習(xí)網(wǎng)（FLN）；土壤含水量；預(yù)測(cè)中圖分類號(hào)：S271；TV93 文獻(xiàn)標(biāo)識(shí)碼：A文章編號(hào)：0439-8114（2025）05-0147-08DOI：10.14088/j.cnki.issn0439-8114.2025.05.023

TVFEMD optimization decomposition and FLN-based soil moisture content prediction using intelligent algorithm optimizations

TIANYu1，CUIDong-wen2

1.YunnanInstituteofWater&HydropowerEngineeingInvestigation，DesignandResearch，Kunming 65O1，China; 2.Wenshan Zhuang and Miao Autonomous Prefecture Water Bureau，Wenshan 663Ooo，Yunnan，China）

Abstract：BasedotheobservedsoilmosturecontentdatafromlO，2，and40cmsoillyersatTanxingadPojiaostationsinYunnanProvince，a prediction model（TVFEMD-BSLO/AO/IVYA/EGO/PSO-FLN）wasconstructedbyimprovingthetime-varying filter empirical modedecomposition（TVFEMD）andfastlearning network（FLN）methods toenhancethetime-series predictionaccuracy of soil moisturecontent.Bycomparingtheperformanceofdiferentoptimzatioalgoritms，asuperiormodelingapproachasprovidedforsoilmoistureprediction.Theresultsshowed thattheTVFEMDdecompositionperformancewasprimarilyinfluencedbytwo key parameters：Bandwidth thresholdand B-splineorder.Optimizing these twparametersusing theIVYAalgorithmimproved the timeseriesdecompositionqualityand further enhanced themodel’sprediction performance.The TVFEMD-BLSO/AO/IVYA/EGO-FLN modeldemonstratedoutstandingpredictionperformanceonthetrainingset，withameanabsolutepercentageerror（MAPE）of 0.002 % ＼～0.077% and a coefficient of determination （ R2 ）of 0.999 7＼～1.000 0. The MAPE in the prediction set was 0.006%＼～0.459 % ， and R2 was0.996 6＼～1.000 0.Compared with the TVFEMD-PSO-FLN model，the TVFEMD-BLSO/AO/IVYA/EGO-FLN model showedsignificantimprovements inbothfitingperformanceandpredictionaccuracyOptimizingFLNhyperparametersusingBLSO， AO，IVYA，ndEGOagorithmseffectivelyimprovedmodelpeformance，withtheIVYAalgorithmexhibitingthemostnotableoptimization effect.

Key Words：time-varyingfilter empirical modedecomposition（TVFEMD）；algorithmoptimization；fastlearning network（FLN）; soil moisture content；prediction

土壤含水量是描述土壤干濕程度，反映旱情最直接、最重要的指標(biāo)之一，提高土壤含水量時(shí)間序列預(yù)測(cè)精度對(duì)于旱情預(yù)警、農(nóng)業(yè)生產(chǎn)、生態(tài)系統(tǒng)保護(hù)和水資源管理具有重要意義。（剩余10412字）