Page 212 - 《水资源与水工程学报》2025年第2期
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                 vidualmodeltoensurethemodel’sgeneralizationabilityandaccuracy.Finally,thefinalpredictionre
                 sultwasobtainedbyperformingaweightedaverageoftheoutputsfromeachindividualmodel.Theresults
                                                                                2
                 showthattheMLSTM modelimprovedthecoefficientofdetermination (R)ofthesoilmoistureat1,
                12,24,and48hto0.951,0.932,0.870,and0.815,respectively,accordingtothedatasetofsoil
                 moisturecontentaveragesfrom foursoillayers.TheMLSTM modeleffectivelyenhancedthemedium
                 andlongtermpredictionaccuracyofsoilmoistureforjujubetrees,withparticularlysignificantimprove
                 mentsinpredictionsat24and48h.Thesefindingscanprovideastrongsupportforthepreciseirrigation
                 managementofjujubetrees ,thusimprovingwateruseefficiencyandavoidingunnecessarywaterwaste.
                 Keywords:predictionofsoilmoisture;multiheadlongshorttermmemory(MLSTM);sparrowsearch
                 algorithm(SSA);kfoldcrossvalidation;southernXinjiangdripirrigationjujube
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            1 ./01
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                 VW4]( 4W) JGvnÄÑûl£NÆÇ,                       Nªõ˜‘’ÒÓ。Filipovi Q         [18] YXJX<Æ
            u$%TÝ{`an。(vnÏÐp@、 ÆÇ|m、                           Ñ、 ÔI.Óê{ìUæQ©©Ž LSTMí%, Ý
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            þn×1。pP (ZiziphusjujubaMill.) Uæ(vn                ž‰}oTo~O6(Iî。ElSaadani Q                [19] È
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            ˜ [1] 。45ÔIÈGÉӗœÊ, (vnTFG^                        To~mg„P„ê’É; (R =0.9), 3‰,
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            ÷ƒˆÌ>, ÀŸ Ì>ß«í%»¼o~I†‘                            Y!Ô, É9N‘’í%ºwPÏèRSm¹ºN
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            ìZAk×Uï½Ì>Q©À‘’oUij                      [7] , '   ñëRI¬¬è(multiheadlongshortternmemo
            ±BLïìZAdÁà5šÓ&, Ú«é•Ý.                            ry ,MLSTM) í%, åº}„‘’oU 1、12、24、48
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            ï    、 \ï¡j     、 ßÎ$‘Y         、 BCþ,V‘          h To~Q˜˜™。(í%4 4 û LSTMí%MÝ,
            Yg×    [11] 、 ŽÂ Boruta \ï¡j  [12] 、 tXÕkè‘        éûí%†xY‰}„÷BgT„÷ƒˆÌ>Uæ
            Y [13] Q。n{, ZkïìZAí%º…†cÃT„                       ©Ž, ÀóΡKPí%©ÉÁy»¼›6…†, ý
            ÷ƒˆÌ>„¹ºZXT•ÅN, óôKÁþ«µ                            dX×To~Q˜‘’Áy。(í%”XYȝ4
            ¶„÷ŸuúâJƒÃNQ©, <Ms?»¼l.                            WpPo~Q˜T!cђÞg, ”æP¿TÞ
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            KPŒ, ()7ºÙŽ‘Bî†ì(multilayer
            perceptron,MLP)、 ¸Û7#I¬¬è(artificialneu           2.1 ÛTs\]
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            ralnetwork,ANN) 、 {•I¬¬è(recurrentneu                 Úe5 2021T 4t 20Ï—10t 31Ï、2022
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            ralnetwork ,RNN) QgZAí%。¶b,Ade                   T 4 t 20Ï—10t 31ϺVW;zÙÚST0
            yemi Q [17] µ—‘NL=5cñëRI¬¬è                       WØ= 224T 5P X n ( H ¬ 79°16′44″, _ p
            (longshorttermmemory,LSTM) T«¦o~mg‘               37°13′01″) »¼, …ÜÚenºŽû4Wvn!°
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