Kea leboha ka ho etela Nature.com.Mofuta oa sebatli oo u o sebelisang o na le ts'ehetso e fokolang bakeng sa CSS.Bakeng sa boiphihlelo bo botle ka ho fetisisa, re khothaletsa hore u sebelise sebatli se ntlafalitsoeng (kapa tima mokhoa oa ho lumellana ho Internet Explorer).Khabareng, ho netefatsa tšehetso e tsoelang pele, re tla bonts'a sebaka sena ntle le mekhoa le JavaScript.
Tšilafalo ea mobu ke bothata bo boholo bo bakoang ke mesebetsi ea batho.Kabo ea sebaka sa lintho tse ka bang chefo (PTEs) e fapana libakeng tse ngata tsa litoropo le tse haufi le litoropo.Ka hona, ho thata ho bolela esale pele litaba tsa PTE mobung o joalo.Kakaretso ea lisampole tse 115 li ile tsa fumanoa ho tsoa ho Frydek Mistek in the Czech Republic.), magnesium nickium (potasiamo) le potassium nickM inductively coupled plasma emission spectrometry.Phetoho ea karabelo ke Ni le li-predictors ke Ca, Mg, le K.Matrix ea correlation pakeng tsa phetoho ea karabo le phetoho ea ho bolela esale pele e bontša kamano e khotsofatsang pakeng tsa lintlha.Liphetho tsa ho bolela esale pele li bontšitse hore Tšehetso ea Vector Machine Regression (SVMR) e sebetsa hantle, le hoja motso oa eona o hakanngoa o bolela phoso ea square/kg.916 (RMSE6) (2016 mg) (2MSE6) e bolela phoso ea square (RMSE6) (5kg. .946 mg / kg) e ne e phahame ho feta mekhoa e meng e sebelisitsoeng.Mehlala e tsoakiloeng bakeng sa Empirical Bayesian Kriging-Multiple Linear Regression (EBK-MLR) e sebetsa hampe, joalokaha ho pakoa ke li-coefficients tsa boikemisetso tse ka tlaase ho 0.1. The Empirical Bayesian Kriging-Support Vector Machine Regression (EBK5) e nang le mohlala o motle ka ho fetisisa oa MA / SVMR (EBK5) le SVM9 e tlaase ka ho fetisisa e ne e le MAM7. (77.368 mg/kg) boleng le coefficient e phahameng ea boikemisetso (R2 = 0.637) .The EBK-SVMR modeling output output e bonoa ka ho sebelisa 'mapa o itlhophisang.Clustered neuron in the plane of the hybrid model CakMg-EBK-SVMR component show multiple color Patterns displays the urban concentrations the SVM surfaces SVMR ke mokhoa o sebetsang oa ho lepa litekanyo tsa Ni ka har'a mobu oa litoropo le o haufi le toropo.
Nickel (Ni) e nkoa e le micronutrient bakeng sa limela hobane e kenya letsoho ho lokisa naetrojene ea sepakapaka (N) le metabolism ea urea, tseo ka bobeli li hlokahalang bakeng sa ho mela ha peo. Ho phaella ho tlatsetso ea eona ho mela ea peo, Ni e ka sebetsa e le inhibitor ea fungal le baktheria le ho khothalletsa tsoelo-pele ea limela. Manyolo a nang le ckel ho optimize nitrogen fixation2. Ho sebelisoa ha menontsha e entsoeng ka nickel ho matlafatsa mobu le ho eketsa bokhoni ba limela tsa linaoa ho lokisa naetrojene mobung ka ho tsoelang pele ho eketsa nickel concentration ea mobu. trient bakeng sa kholo ea limela1.Ho ea ka Liu3, Ni e fumanoe e le karolo ea 17 ea bohlokoa e hlokahalang bakeng sa nts'etsopele le kholo ea limela. Ho phaella ho karolo ea nickel ho nts'etsopele le kholo ea limela, batho ba e hloka bakeng sa mefuta e fapaneng ea likopo.Electroplating, tlhahiso ea li-alloys tse thehiloeng ho nickel, le ho etsoa ha lisebelisoa tsa ho khantša le ho kenya lisebelisoa tse fapaneng tsa indasteri ea spark nickel. Li-alloys le lisebelisoa tsa electroplated li sebelisitsoe haholo ho kitchenware, lisebelisoa tsa ballroom, thepa ea indasteri ea lijo, motlakase, terata le cable, li-jet turbines, li-implants tsa ho buoa, masela le kaho ea likepe5. ho foqoha ha li-canic, limela, mello ea meru le mekhoa ea jeoloji;leha ho le joalo, mehloli ea anthropogenic e kenyelletsa libeteri tsa nickel/cadmium indastering ea tšepe, electroplating, welding ea arc, diesel le mafura a mafura, le mesi e tsoang moeeng e tsoang ho cheso ea mashala le litšila le ho chesoa ha litšila Ho bokellana ha Nickel7,8.Ho latela Freedman le Hutchinson9 le Manyiwa et.10, mehloli e ka sehloohong ea tšilafalo ea mobu sebakeng se haufi le se haufi haholo ke li-smelters le merafo e thehiloeng ka koporo e entsoeng ka nickel. Mobu o ka holimo o pota-potileng sebaka sa ho hloekisa nickel-copper sa Sudbury Canada se ne se e-na le tšilafalo e phahameng ka ho fetisisa ea tšilafalo ea nickel ho 26,000 mg / kg11. 11.Ho latela Alms et al.12, palo ea HNO3-extractable nickel sebakeng se ka holimo ho lengoa naha (nickel tlhahiso ea Russia) ranges ho tloha 6.25 ho 136.88 mg/kg, e tsamaisanang le moelelo oa 30.43 mg/kg le ea motheo ea mahloriso ea 25 mg/kg.Ho ea ka kabata 11 ho ea ka kabata 11 mobung, tšebeliso ea mobu oa manyolo ka har'a manyolo ka nako ea manyolo kapa manyolo a phosphorus nakong ea manyolo ka nako ho kenya kapa ho silafatsa mobu.Liphello tse ka bang teng tsa nickel ho batho li ka lebisa ho kankere ka mutagenesis, tšenyo ea chromosomal, moloko oa Z-DNA, ho thibela ho lokisoa ha DNA, kapa mekhoa ea epigenetic13. Litekong tsa liphoofolo, nickel e fumanoe e e-na le bokhoni ba ho baka mefuta e sa tšoaneng ea lihlahala, 'me lik'hemik'hale tsa nickel tsa carcinogenic li ka mpefatsa lihlahala tse joalo.
Litlhahlobo tsa tšilafalo ea mobu li atlehile morao tjena ka lebaka la litaba tse ngata tse amanang le bophelo bo botle tse hlahisoang ke likamano tsa limela tsa mobu, mobu le mobu, ho senyeha ha tikoloho, le tlhahlobo ea phello ea tikoloho. ive soil mapping (PSM).According to Minasny and McBratney16, predictive soil mapping (DSM) has proven to be a prominent subdiscipline of soil science.Lagacherie and McBratney, 2006 define DSM as “the creation and filling of spatial soil information systems through the use of in situal and nonBrat-system systems”. t al.17 e hlalosa hore DSM kapa PSM ea mehleng ea kajeno ke mokhoa o atlehang ka ho fetisisa oa ho bolela esale pele kapa ho etsa 'mapa oa ho ajoa ha sebaka sa PTEs, mefuta ea mobu le thepa ea mobu.Geostatistics le Machine Learning Algorithms (MLA) ke mekhoa ea DSM ea ho etsa limmapa tse entsoeng ka digitized ka thuso ea lik'homphieutha tse sebelisang lintlha tsa bohlokoa le tse fokolang.
Deutsch18 le Olea19 li hlalosa geostatistics e le "pokello ea mekhoa ea lipalo e sebetsanang le kemelo ea litšobotsi tsa sebaka, haholo-holo ho sebelisa mehlala ea stochastic, joalo ka hore na tlhahlobo ea letoto la nako e khetholla data ea nakoana joang."Haholo-holo, geostatistics e kenyelletsa tlhahlobo ea li-variograms, tse lumellang Quantify le ho hlalosa ho its'epaha ha boleng ba sebaka ho tsoa ho dataset ka 'ngoe20.Gumiaux et al.20 e boetse e bontša hore tlhahlobo ea li-variograms ho geostatistics e thehiloe holim'a melao-motheo e meraro, ho kenyelletsa (a) computing tekanyo ea correlation ea data, (b) ho khetholla le ho etsa computing anisotropy ka ho se tšoane ha dataset le (c) ho phaella ho Ho phaella tabeng ea ho nahanela phoso ea tlhaho ea data ea tekanyo e arohaneng le liphello tse sebelisoang sebakeng sena, mekhoa e mengata e hakanngoang e boetse e sebelisoa sebakeng sa geostatistics. lipalo-palo, ho kenyeletsoa kriging ka kakaretso, co-kriging, kriging e tloaelehileng, kriging e matla ea Bayesian kriging, mokhoa o bonolo oa kriging le mekhoa e meng e tsebahalang ea ho fetolela ho etsa 'mapa kapa ho bolela esale pele PTE, litšobotsi tsa mobu, le mefuta ea mobu.
Machine Learning Algorithms (MLA) ke mokhoa o batlang o le mocha o sebelisang litlelase tse kholoanyane tsa data tseo e seng tsa linear, tse susumetsoang ke dikgato-tharabololo tse sebediswang haholo bakeng sa meepo ya data, ho tsebahatsa dipaterone ho data, le ho sebediswa kgafetsa ho arola mafapheng a mahlale a kang mahlale a mobu le mesebetsi ya ho kgutlela.Pampiri tse ngata tsa dipatlisiso di itshetlehile ka mefuta ya MLA ho bolela esale pele PTE mobung, jwalo ka Tan et al.22 (meru e sa tloaelehang bakeng sa tekanyo ea tšepe e boima mobung oa temo), Sakizadeh et al.23 (mohlala o sebelisa mechine ea li-vector ea tšehetso le marang-rang a maiketsetso a maiketsetso) tšilafalo ea mobu ) .Ho phaella moo, Vega et al.24 (CART bakeng sa ho etsa mohlala oa ho boloka tšepe e boima le adsorption mobung) Sun et al.25 (tshebediso ya cubist ke kabo ea Cd mobung) le dikgatotharabololo tse ding tse kang k-haufi moahelani, generalized boosted regression, le boosted regression Lifate e boetse e sebelisoa MLA ho bolela esale pele PTE mobung.
Tšebeliso ea li-algorithms tsa DSM ka ho bolela esale pele kapa 'mapa e tobana le mathata a mangata.Bangoli ba bangata ba lumela hore MLA e phahametse geostatistics le ka tsela e fapaneng.Le hoja e le' ngoe e molemo ho feta e 'ngoe, motsoako oa bobeli o ntlafatsa boemo ba ho nepahala ha 'mapa kapa ho bolela esale pele ho DSM15.Woodcock le Gopal26 Finke27;Pontius le Cheuk28 le Grunwald29 ba bua ka mefokolo le liphoso tse ling tse boletsoeng esale pele 'mapa oa mobu.Bo-rasaense ba mobu ba lekile mekhoa e mengata ea ho ntlafatsa katleho, ho nepahala, le ho tseba esale pele ka ho etsa 'mapa oa DSM le ho bolela esale pele15 e hlakisa hore boitšoaro ba netefatso le ho hloka bonnete tse hlahisoang ke popo ea 'mapa le ho bolela esale pele li lokela ho netefatsoa ka boikemelo ho ntlafatsa boleng ba' mapa.Mefokolo ea DSM e bakoa ke boleng ba mobu o hasantsoeng, o kenyelletsang karolo ea ho hloka botsitso;leha ho le joalo, ho hloka bonnete ba DSM ho ka 'na ha hlaha mehloling e mengata ea phoso, e leng phoso ea covariate, phoso ea mohlala, phoso ea sebaka, le Phoso ea tlhahlobo ea 31. Ho se nepahale ha mohlala ho hlahisitsoeng ke MLA le mekhoa ea geostatistical ho amahanngoa le ho hloka kutloisiso, qetellong ho lebisang ho fetelletseng ha mokhoa oa sebele32. ho bolela esale pele, kapa interpolation33.Haufinyane tjena, ho hlahile mokhoa o mocha oa DSM o khothalletsang ho kopanngoa ha geostatistics le MLA ho etsa limmapa le ho bolela esale pele.Bo-rasaense le bangoli ba 'maloa ba mobu, ba kang Sergeev et al.34;Subbotina et al.35;Tarasov et al.36 le Tarasov et al.37 e sebelisitse boleng bo nepahetseng ba lipalo-palo le thuto ea mochini ho hlahisa mefuta e nyalisitsoeng e ntlafatsang ts'ebetso ea ponelopele le 'mapa.quality.Some of these hybrid or combined algorithm models are Artificial Neural Network Kriging (ANN-RK), Multilayer Perceptron Residual Kriging (MLP-RK), Generalized Regression Neural Network Residual Kriging (GR- NNRK)36, Artificial Neural Network Kriging-Multilayer Perceptron (ANN-K-MLP)37 and Co-Kriging and Gaussian Process Regression38.
Ho latela Sergeev et al., ho kopanya mekhoa e fapaneng ea ho etsa mohlala ho na le monyetla oa ho felisa mefokolo le ho eketsa ts'ebetso ea mofuta oa lebasetere o hlahisoang ho fapana le ho nts'etsapele mofuta oa eona o le mong.Moelelong ona, pampiri ena e ncha e pheha khang ea hore hoa hlokahala ho sebelisa algorithm e kopaneng ea geostatistics le MLA ho theha optimal hybrid models to predict Ni enrichment in urban and peri-base Bay. mohlala le ho o kopanya le Support Vector Machine (SVM) le Multiple Linear Regression (MLR) modeli.Hybridization of EBK with any MLA is not known.Mefuta e mengata e tsoakiloeng e bonoang ke metsoako ea tloaelehileng, residual, regression kriging, le MLA.EBK is a geostatistical interpolation method that utilizes a spatially stochastation field with an spatially stochastic field/ tšimong, ho lumella phapang ea sebaka39.EBK e sebelisitsoe liphuputsong tse fapaneng, ho kenyeletsoa ho hlahloba kabo ea carbon carbon mobung oa polasi40, ho hlahloba tšilafalo ea mobu41 le ho etsa 'mapa oa thepa ea mobu42.
Ka lehlakoreng le leng, Self-Organising Graph (SeOM) ke algorithm ea ho ithuta e sebelisitsoeng lihloohong tse fapaneng tse kang Li et al.43, Wang et al.44, Hossain Bhuiyan et al.45 le Kebonye et al.46 Fumana litšobotsi tsa sebaka le lihlopha tsa likarolo.Wang et al.44 hlalosa hore SeOM ke mokhoa o matla oa ho ithuta o tsejoang ka bokhoni ba eona ba ho kopanya le ho nahana ka mathata a sa tsitsang.Ho fapana le mekhoa e meng ea ho lemoha mohlala e kang principal component analysis, fuzzy clustering, hierarchical clustering, and multi-criteria liqeto liqeto, SeOM e molemo ho hlophisa le ho khetholla mekhoa ea PTE.According to Waccording.44, SeOM e ka arola sebaka sa kabo ea li-neurons tse amanang le ho fana ka pono e phahameng ea data.
Pampiri ena e ikemiselitse ho hlahisa mofuta o matla oa 'mapa o nepahetseng ka ho fetesisa oa ho bolela esale pele litaba tsa nickel mobung oa litoropo le o haufi le litoropo.ka hona, re tla leka ho araba lipotso tsa lipatlisiso tse ka 'nang tsa fana ka mehlala e tsoakiloeng.Leha ho le joalo, mohlala o nepahetse hakae ho bolela esale pele ntho eo re e reretsoeng?Hape, ke boemo bofe ba tlhahlobo ea katleho e thehiloeng ho netefatso le tlhahlobo ea ho nepahala?Ka hona, lipakane tse khethehileng tsa thuto ee e ne e le (a) ho etsa mohlala o kopantsoeng oa motsoako oa SVMR kapa MLR o sebelisa EBK (c) bapisa mohlala o motle ka ho fetisisa oa motsoako oa EBK e le mohlala oa pro mobu oa litoropo kapa o haufi le litoropo , le (d) ts'ebeliso ea SeOM ho theha 'mapa o nang le qeto e phahameng oa phetoho ea sebaka sa nickel.
Thuto e ntse e etsoa Czech Republic, haholo-holo seterekeng sa Frydek Mistek sebakeng sa Moravia-Silesian (sheba setšoantšo sa 1). Geography ea sebaka sa boithuto se matsutla-tsutla 'me boholo ba sona ke karolo ea sebaka sa Moravia-Silesian Beskidy, e leng karolo ea moeli o ka ntle oa Lithaba tsa Carpathian. Sebaka sa boithuto se pakeng tsa 4 le 0'19 ° 4 le 0 ′19 ° 4 le 0 49 ° ′ 49 ′ 49 °. bophahamo bo pakeng tsa 225 le 327 m;leha ho le joalo, mokhoa oa ho arola Koppen bakeng sa boemo ba leholimo ba sebaka seo o lekantsoe joalo ka Cfb = boemo ba leholimo bo futhumetseng ba leoatle, Ho na le pula e ngata esita le likhoeling tse omileng.Mocheso o fapana hanyane ho pholletsa le selemo pakeng tsa -5 °C le 24 °C, ka seoelo o theohelang ka tlase ho −14 °C kapa ka holimo ho 30 °C le kaholimo ho 30 °C selemo le selemo. Sebaka sa sebaka sohle ke lisekoere-k'hilomithara tse 1,208, se nang le 39.38% ea mobu o lengoang le 49.36% ea meru. sion) le tšepe ea motsoako (nickel e eketsa matla a motsoako ha e ntse e boloka ductility ea eona e ntle le toughness), le temo e matla e kang kopo ea manyolo a phosphate le tlhahiso ea liphoofolo tse ruuoang ke lipatlisiso mehloli e ka bang teng ea nickel sebakeng seo (mohlala, ho eketsa nickel ho likonyana ho eketsa litekanyetso tsa kholo ea likonyana le likhomo tse fokolang) . dithulaganyo.Thepa ea mobu e ka khetholoha habonolo ho tloha ho 'mala oa mobu, sebopeho, le lintho tse nang le carbonate.Motsoako oa mobu o mahareng ho isa ho o motle, o nkiloeng ho thepa ea motsoali.Li na le tlhaho ea colluvial, alluvial kapa aeolian.Mabaka a mang a mobu a hlaha a le matheba ka holim'a mobu le ka tlas'a mobu, hangata a na le konkreite le bleaching.Leha ho le joalo, cambisols le stagnosols ke mefuta e tloaelehileng ea 4 ho tloha ho elevation48 sebakeng sa 48 ho tloha ho elevation48. 5 m, li-cambisol li laola Czech Republic49.
'Mapa oa sebaka sa boithuto ['Mapa oa sebaka sa boithuto o entsoe ho sebelisoa ArcGIS Desktop (ESRI, Inc, mofuta oa 10.7, URL: https://desktop.arcgis.com).]
Kakaretso ea lisampole tse 115 tsa mobu o ka holimo li ile tsa fumanoa mobung oa litoropo le o haufi le toropo seterekeng sa Frydek Mistek. Mohlala oa mohlala o sebelisitsoeng e ne e le marang-rang a tloaelehileng a nang le lisampole tsa mobu tse arohaneng ka 2 × 2 km ka thōko, 'me mobu o ka holimo o ne o lekanyetsoa ka botebo ba 0 ho ea ho 20 cm ka ho sebelisa mochine o tšoaroang ka letsoho oa GPS (Leica Zeno 2000 ke li-package tsa GPS tse phuthetsoeng hantle, li-saplobele tse 5 tsa GPS). .Mehlala e ne e omisitsoe ke moea ho hlahisa lisampole tse silafalitsoeng, tse silafalitsoeng ke mochine oa mochine (Fritsch disc leloala), le sieved (sieve size 2 mm) . Beha 1 gram ea mobu o omisitsoeng, o nang le homogenized le o sieved ka libotlolong tsa teflon tse ngotsoeng ka ho hlaka. e 'ngoe bakeng sa acid e' ngoe le e 'ngoe), koahela hanyenyane 'me u lumelle disampole ho ema bosiu bo le bong bakeng sa karabelo (lenaneo la aqua regia) .Beha supernatant holim'a poleiti ea tšepe e chesang (mocheso: 100 W le 160 °C) bakeng sa 2 h ho nolofatsa ts'ebetso ea tšilo ea lijo tsa lisampole, ebe u pholile.Fetisetsa supernatant ho 50 ml ea metsi a hloekisitsoeng ka metsi a 50 ml le filthara ea metsi a 50 ml. supernatant ka har'a 50 ml PVC tube e nang le metsi a deionized. Ho phaella moo, 1 ml ea tharollo ea dilution e ile ea hlapolloa ka 9 ml ea metsi a hloekisitsoeng 'me e tlhotliloeng ka har'a tube ea 12 ml e lokiselitsoeng bakeng sa PTE pseudo-concentration.The concentrations of PTEs (As, Cd, Cr, Cu, Mn, Ni, Cabled by Mgn, KCPD) e ne e khethiloe ke MgN, KCP, PTE ma Optical Emission Spectroscopy) (Thermo Fisher Scientific, USA) ho ea ka mekhoa e tloaelehileng le tumellano.Ho netefatsa mekhoa ea ho netefatsa boleng le taolo (QA/QC) (SRM NIST 2711a Montana II Soil) .PTEs tse nang le meeli ea ho lemoha ka tlase ho halofo e ne e sa kenyelletsoa thutong ena.Moeli oa ho lemoha oa PTE o sebelisitsoeng thutong ea boleng ba 000 e 'ngoe le e 'ngoe ea ts'ebetso ea boleng ba 000. e netefatsoa ka ho hlahloba litekanyetso tsa litšupiso.Ho netefatsa hore liphoso li fokotsehile, ho ile ha etsoa tlhahlobo e habeli.
Empirical Bayesian Kriging (EBK) is one of many geostatistical interpolation techniques used in modelling in various fields such as soil science.Ho fapana le mekhoa e meng ea kriging interpolation, EBK e fapane le mekhoa ea khale ea kriging ka ho nahana ka phoso e hakantsoeng ke mohlala oa semivariogram. mokhoa oa ho hloka botsitso le mananeo a amanang le moralo ona oa semivariogram o etsang karolo e rarahaneng haholo ea mokhoa o lekaneng oa kriging. Ts'ebetso ea ho fetolela ea EBK e latela mekhoa e meraro e hlahisitsoeng ke Krivoruchko50, (a) mohlala o hakanya semivariogram ho tsoa ho dataset ea ho kenya (b) boleng bo boletsoeng esale pele bakeng sa dataset e 'ngoe le e 'ngoe e hahelletsoeng ho tsoa ho mofuta o mong le o mong oa data o hlahisoang Lated dataset.Molao wa equation wa Bayesian o fanoa e le bokamorao
Moo \(Prob\left(A\right)\) e emelang ea pele, \(Prob\left(B\right)\) monyetla oa marginal o hlokomolohuoa maemong a mangata, \(Prob (B,A)\ ) .Palo ea semivariogram e ipapisitse le molao oa Bayes, o bonts'ang propensity ea observation ea observation datasets of the rule of the semiovaries the rule of the bay semiovaries that can be made the Bay semiovaries. e bolelang hore na ho na le monyetla oa ho theha dataset ea litebello ho tsoa ho semivariogram.
Mochini oa vector oa ts'ehetso ke mokhoa oa ho ithuta oa mochine o hlahisang hyperplane ea ho arola hantle ho khetholla lihlopha tse ts'oanang empa li sa ikemela ka mokhoa o ikemetseng. ion - SVMR) e ile ea sebelisoa tlhahlobisong ena.Cherkassky le Mulier53 ba ile ba bula SVMR e le mokhoa oa ho khutlela morao oa kernel-based, eo palo ea eona e ileng ea etsoa ho sebelisoa mokhoa oa ho khutlela morao o nang le mesebetsi e mengata ea libaka.55, epsilon (ε) -SVMR e sebelisa dataset e koetlisitsoeng ho fumana mohlala oa boemeli e le ts'ebetso ea epsilon-insensitive e sebelisoang ho etsa 'mapa oa data ka mokhoa o ikemetseng le molemo ka ho fetisisa oa epsilon bias ho tloha koetlisong ea data e amanang.Phoso ea sebaka se seng se setiloe e hlokomolohuoa ho tloha boleng ba sebele,' me haeba phoso e le kholo ho feta ε(ε), thepa ea mobu ea koetliso ea subate ea mobu e boetse e fokotsa thepa ea ho koetlisa mobu e sephara ea vector. s. The equation e hlahisitsoeng ke Vapnik51 e bontšitsoe ka tlase.
moo b e emelang sekhahla sa scalar, \(K\left({x}_{,}{ x}_{k}\right)\) e emetse kernel function, \(\alpha\) e emetse Lagrange multiplier, N E emela dataset ea linomoro, \({x}_{k}\) e emelang ho kenya data, le \.Orne le SVR e sebelisoang ke senotlolo sa data, le \(yrne) ke senotlolo sa data. radial basis function (RBF) .RBF kernel e sebelisoa ho fumana mokhoa o nepahetseng oa SVMR, o leng bohlokoa ho fumana tekanyo e poteletseng ka ho fetisisa ea kotlo ea C le kernel parameter gamma (γ) bakeng sa data ea koetliso ea PTE. Ntlha ea pele, re ile ra hlahloba setsi sa koetliso ebe re hlahloba ts'ebetso ea mohlala holim'a sete sa ho netefatsa.Mokhoa oa tsamaiso ea tsamaiso le boleng ba Radima ke sigima.
A multiple linear regression model (MLR) is a regression model e emelang kamano e teng pakeng tsa ho arabela ho feto-fetoha le palo e fapaneng ea ho bolela esale pele ka ho sebelisa linear pooled parameters balwa ho sebelisa bonyane squares method.Ho MLR, a least squares model is a predictive function of the soil properties after election of explanatory variables.Hoa hlokahala ho sebelisa karabo ho theha kamano ea linear e ne e le tlhaloso ea tlhaloso ea tlhaloso P.P. tory variables.The MLR equation is
moo y e leng phapang ea karabelo, \(a\) ke sekhechana, n ke palo ea ba lekanyetsang lintho esale pele, \({b}_{1}\) ke phokotso e sa fellang ea li-coefficients, \({x}_{i}\) e emela sephetho kapa tlhaloso e fapaneng,' me \({\varepsilon }_{i}\) e emela phoso, hape e tsejoang e le mohlala.
Mefuta e tsoakiloeng e fumanoe ka sandwiching EBK e nang le SVMR le MLR. Sena se etsoa ka ho ntša litekanyetso tse boletsoeng esale pele ho tsoa ho EBK interpolation. Litekanyetso tse boletsoeng esale pele tse fumanoeng ho Ca, K, le Mg tse kentsoeng li fumanoa ka mokhoa oa ho kopanya ho fumana mefuta e mecha, joalo ka CaK, CaMg, le KMg. mefuta e fapaneng e fumanoeng ke Ca, K, Mg, CaK, CaMg, KMg le CaKMg. Liphetoho tsena li ile tsa fetoha li-predictors tsa rona, tsa thusa ho bolela esale pele likhahla tsa nickel mobung oa litoropo le oa peri-urban.The algorithm ea SVMR e ile ea etsoa ho li-predictors ho fumana mohlala o tsoakaneng oa Empirical Bayesian Kriging-Support Vector Machine (EBK_lySVM) e boetse e na le pipe e kopantsoeng ea Empiri ka mokhoa oa ho fumana mokhoa o fapaneng oa Empiri. Bayesian Kriging-Multiple Linear Regression (EBK_MLR).Ka tloaelo, mefuta e Ca, K, Mg, CaK, CaMg, KMg, le CaKMg li sebelisoa e le li-covariates e le li-predictors tsa Ni content mobung oa litoropo le peri-urban.Mohlala o amohelehang ka ho fetesisa o fumanoeng (EBK_SVM kapa EBK_SVM or EBKM or EBK_R) khaba 2.
Ho sebelisa SeOM e fetohile sesebelisoa se tummeng sa ho hlophisa, ho hlahloba le ho bolela esale pele lintlha tsa lefapha la lichelete, tlhokomelo ea bophelo bo botle, indasteri, lipalo-palo, saense ea mobu, le tse ling.SeOM e bōpiloe ka ho sebelisa marang-rang a maiketsetso a methapo ea kutlo le mekhoa ea ho ithuta e sa laoleheng bakeng sa mokhatlo, tlhahlobo, le ho bolela esale pele. boleng li sebelisoa e le n input-dimensional vector variables43,56.Melssen et al.57 hlalosa ho hokahanngoa ha mochine oa ho kenya marang-rang ka mokhoa o le mong oa ho kenya letsoho ho ea ho vector ea tlhahiso e nang le vector e le 'ngoe ea boima.Tlhaloso e hlahisoang ke SeOM ke 'mapa o nang le mahlakore a mabeli a nang le li-neurone tse fapaneng kapa li-node tse lohiloeng ho limmapa tsa hexagonal, chitja, kapa tsa lisekoere tsa topological ho latela proximity ea bona.Ho bapisa boholo ba 'mapa bo thehiloeng ho mepotric, OME ea mohlala le phoso ea 00 QE (mohlala oa 0Q) 86 le 0.904, ka ho latellana, e khethiloe, e leng yuniti ea 'mapa oa 55 (5 × 11) .Sebopeho sa neuron se khethoa ho ea ka palo ea li-node ho equation ea empirical.
Palo ea lintlha tse sebelisitsoeng thutong ena ke disampole tse 115. Ho ile ha sebelisoa mokhoa o sa reroang oa ho arola lintlha ka lintlha tsa tlhahlobo (25% bakeng sa netefatso) le lihlopha tsa boitsebiso ba koetliso (75% bakeng sa ho lekanya). Lethathamo la boitsebiso ba koetliso le sebelisetsoa ho hlahisa mohlala oa ho fokotsa maemo (calibration), 'me dataset ea teko e sebelisetsoa ho netefatsa bokhoni ba kakaretso58. -Tlhahiso ea ho netefatsa, e phetoa ka makhetlo a mahlano
Mekhahlelo e fapaneng ea ho netefatsa e ile ea sebelisoa ho fumana mohlala o motle ka ho fetisisa o loketseng ho bolela esale pele likhahla tsa nickel mobung le ho hlahloba ho nepahala ha mohlala le ho netefatsoa ha oona.Mehlala ea Hybridization e ile ea hlahlojoa ho sebelisoa phoso e kholo ka ho fetisisa (MAE), phoso ea motso oa square (RMSE), le R-squared kapa coefficient determination (R2) . boholo ba ance ka mehato e ikemetseng e hlalosa matla a ho bolela esale pele a mohlala, ha MAE e etsa qeto ea boleng ba sebele ba palo. Theko ea R2 e tlameha ho ba e phahameng ho hlahloba mohlala o motle ka ho fetisisa oa motsoako o sebelisang li-parameter tsa ho netefatsa, boleng bo haufi le ho 1, ho feta ho nepahala.Ho ea ka Li et al.59, boleng ba criterion ea R2 ea 0.75 kapa ho feta bo nkoa e le selelekela se setle;ho tloha ho 0.5 ho ea ho 0.75 ke ts'ebetso e amohelehang ea mohlala, 'me ka tlase ho 0.5 ke ts'ebetso ea mohlala e sa amoheleheng.Ha ho khethoa mohlala o sebelisa mekhoa ea ho hlahloba litekanyetso tsa ho netefatsa RMSE le MAE, litekanyetso tse tlaase tse fumanoeng li ne li lekane 'me li nkoa e le khetho e ntle ka ho fetisisa.The equation e latelang e hlalosa mokhoa oa ho netefatsa.
moo n e emelang boholo ba boleng bo hlokometsoeng\({Y}_{i}\) e emelang karabelo e lekantsoeng, mme \({\widehat{Y}}_{i}\) e emela boleng ba karabo e boletsoeng esale pele, ka hona, bakeng sa lipono tsa pele.
Litlhaloso tsa lipalo-palo tsa li-predicor le mefuta-futa ea likarabo li hlahisoa ho Lethathamo la 1, ho bonts'a moelelo, ho kheloha ho tloaelehileng (SD), coefficient of variation (CV), bonyane, maximum, kurtosis, le skewness.Bonyane le boholo ba boleng ba likarolo li ka tatellano e fokotsehang ea Mg
Likamano tsa mefuta-futa ea li-predicor le likarolo tsa karabelo li bontšitse ho lumellana ho khotsofatsang pakeng tsa likarolo (bona Setšoantšo sa 3) .Khokahano e bontšitse hore CaK e bontšitse kamano e itekanetseng le boleng ba r = 0.53, joalo ka CaNi. Le hoja Ca le K ba bontša likamano tse itekanetseng, bafuputsi ba kang Kingston et al.68 le Santo69 li fana ka maikutlo a hore maemo a tsona mobung a fapane ka tsela e fapaneng.Leha ho le joalo, Ca le Mg li hanyetsa K, empa CaK e lumellana hantle.Sena se ka bakoa ke ho sebelisoa ha menontsha e kang potassium carbonate, e leng 56% e phahameng ho potasiamo.Potassium e ne e amana ka mokhoa o itekanetseng le magnesium (KM 63 haufi-ufi, potassium sulfate ke likarolo tse peli tse amanang le magnesium). Te, potasiamo magnesium nitrate, le potash li sebelisoa mobung ho eketsa maemo a bona a khaello.Nickel e ikamahanya ka mokhoa o itekanetseng le Ca, K le Mg ka r values = 0.52, 0.63 le 0.55, ka ho latellana.Likamano tse amang calcium, magnesium, le PTEs tse kang nickel li rarahaneng, empa magnesium ha e fokotsehe le liphello tse feteletseng tsa calcium. ka bobeli magnesium le calcium li fokotsa chefo ea nickel mobung.
Matrix ea khokahano bakeng sa likarolo tse bonts'ang kamano pakeng tsa li-predictors le likarabo (Hlokomela: setšoantšo sena se kenyelletsa sekhahla pakeng tsa likarolo, maemo a bohlokoa a thehiloe ho p <0,001).
Setšoantšo sa 4 se bontša kabo ea sebaka sa lintho.Ho ea ka Burgos et al70, tšebeliso ea kabo ea sebaka ke mokhoa o sebelisoang ho lekanya le ho totobatsa libaka tse chesang libakeng tse silafetseng.Maemo a ntlafatso a Ca setšoantšong sa 4 a ka bonoa karolong e ka leboea-bophirimela ea 'mapa oa kabo ea sebaka. tshebediso ya quicklime (calcium oxide) ho fokotsa acidity ea mobu le tšebeliso ea oona ka tshilo ya tshepe e le alkaline oxygen tshebetsong ya ho etsa steelmaking.Ka lehlakoreng le leng, balemi ba bang ba rata ho sebedisa calcium hydroxide mobung o nang le asiti ho neutralize pH, e leng se eketsang calcium content ya mobu71.Potassium also shows Hot spots in the northwest and the East of the map.The Northtowest and Potassium Applications s.Sena se lumellana le lithuto tse ling, tse kang Madaras le Lipavský72, Madaras et al.73, Pulkrabová et al.74, Asare et al.75, ea ileng a hlokomela hore ho tsitsisa ha mobu le ho phekoloa ka KCl le NPK ho ile ha fella ka litaba tse phahameng tsa K mobung.Matlafatso ea Potassium ka leboea-bophirima ho 'mapa oa kabo e ka ba ka lebaka la ts'ebeliso ea menontsha e thehiloeng ho potasiamo joalo ka potassium chloride, potassium sulfate, potassium nitrate, potash le potash ho eketsa litaba tsa potasiamo mobung o futsanehileng.Zádorová et al.76 le Tlustoš et al.77 e hlalositse hore ts'ebeliso ea manyolo a thehiloeng ho K e ekelitse litaba tsa K mobung 'me e tla eketsa haholo mobu oa limatlafatsi ka nako e telele, haholo-holo K le Mg e bontšang sebaka se chesang mobung.Hotspots e leka-lekaneng ka leboea-bophirimela ho' mapa le ka boroa-bochabela ho 'mapa. Colloidal fixation mobung e fokotsa bongata ba magnesium mobung o haelloang ke magnesium mobung o serolane. ers, tse kang potassium magnesium sulfate, magnesium sulfate, le Kieserite, alafa mefokolo (limela li bonahala li pherese, li khubelu, kapa li sootho, ho bontšang khaello ea magnesium) mobung o nang le pH e tloaelehileng6. Ho bokellana ha nickel holim'a mobu oa litoropo le libaka tse haufi le litoropo e ka 'na ea bakoa ke mesebetsi ea anthropogenic ea bohlokoa ba tlhahiso ea nickel le tlhahiso ea tšepe.
Kabo ea libaka ['mapa oa kabo ea sebaka o entsoe ho sebelisoa ArcGIS Desktop (ESRI, Inc, Version 10.7, URL: https://desktop.arcgis.com).]
Liphetho tsa index ea ts'ebetso ea mohlala bakeng sa likarolo tse sebelisitsoeng thutong ena li bonts'itsoe ho Lethathamo la 2. Ka lehlakoreng le leng, RMSE le MAE ea Ni ka bobeli li haufi le zero (0.86 RMSE, -0.08 MAE) .Ka lehlakoreng le leng, litekanyetso tsa RMSE le MAE tsa K lia amoheleha. Liphetho tsa RMSE le MAE li ne li le kholoanyane bakeng sa calcium le magnesium le liphetho tse fapaneng tsa MAERM ka lebaka la data e fapaneng ea MAERM.Ca le KERM data. thuto ena e sebelisang EBK ho bolela esale pele hore Ni e fumanoe e le molemo ho feta liphello tsa John et al.54 sebelisa synergistic kriging ho noha S concentrations mobung sebelisa data e bokeletsoeng e tšoanang.The EBK outputs re ithutile correlate le tsa Fabijaczyk et al.41, Yan et al.79, Beguin et al.80, Adhikary et al.81 le Johanne le ba bang.82, haholo-holo K le Ni.
Ts'ebetso ea mekhoa ea motho ka mong bakeng sa ho lepa li-nickel mobung oa litoropo le haufi le litoropo e ile ea hlahlojoa ho sebelisoa ts'ebetso ea mehlala (Letlapa la 3) .Boinetefatso ba mohlala le tlhahlobo e nepahetseng e netefalitse hore Ca_Mg_K predictor e kopantsoeng le EBK SVMR model e hlahisitse ts'ebetso e ntle ka ho fetisisa.Calibration model ea square ea Calibration (SVMR) ea square model (SVM_KRM2RM2RM-EBK-EBK) mohlala oa calibration Calibration (SVMR) MAE) e ne e le 0.637 (R2), 95.479 mg/kg (RMSE) le 77.368 mg/kg (MAE) Ca_Mg_K-SVMR e ne e le 0.663 (R2), 235.974 mg/kg (RMSE) le 166.946 mg/kg (maele e se nang thuso, e ntle bakeng sa CaR2M’s) e ntle bakeng sa Ca. 63 mg/kg R2) le Ca_Mg-EBK_SVMR (0.643 = R2);liphetho tsa bona tsa RMSE le MAE li ne li le holimo ho feta tsa Ca_Mg_K-EBK_SVMR (R2 0.637) (sheba Letlapa la 3) . Ho phaella moo, RMSE le MAE ea Ca_Mg-EBK_SVMR (RMSE = 1664.64 le MAE = 1031.49) ea mohlala e kholo ho feta 13g_5 e leng 13g_49. K-EBK_SVMR. Ka mokhoa o ts'oanang, RMSE le MAE tsa Ca_Mg-K SVMR (RMSE = 235.974 le MAE = 166.946) li kholoanyane ka 2.5 le 2.2 ho feta tsa Ca_Mg_K-EBK_SVMR RMSE le MAE liphetho li bonts'a hore na data e lekantsoeng hantle hakae le RMSE le MAE ka ho latellana. RSME le MAE li ile tsa hlokomeloa.Ho ea ka Kebonye et al.46 le john le ba bang.54, ha RMSE le MAE di le haufi le lefela, diphetho di betere.SVMR le EBK_SVMR di na le boleng bo phahameng ba quantized RSME le MAE.Ho ile ha hlokomelwa hore ditekanyetso tsa RSME di ne di le hodimo ka mokgwa o tshwanang ho feta makgabane a MAE, ho bontsha boteng ba ba kantle.Ho ya ka Legates, the McCabso e bolela phoso3 ho McCabes8 e kgothaletswang e le sesupo sa boteng ba batho ba kantle.Sena se bolela hore ha dataset e ngatafala, e phahamisa boleng ba MAE le RMSE.Ho nepahala ha tekolo ya netefatso e fapaneng ya Ca_Mg_K-EBK_SVMR e kopantsweng ya mohlala bakeng sa ho bolela esale pele Ni content mobung wa ditoropo le ditoropo e ne e le 63.70%.According to Li.59, boemo bona ba ho nepahala ke tekanyo e amohelehang ea ts'ebetso ea mohlala.Liphetho tsa hona joale li bapisoa le thuto e fetileng ea Tarasov et al.36 eo mohlala oa eona oa lebasetere o thehileng MLPRK (Multilayer Perceptron Residual Kriging), e amanang le EBK_SVMR e nepahetseng ea tlhahlobo ea tlhahlobo e tlalehiloeng thutong ea morao-rao, RMSE (210) le The MAE (167.5) e ne e le holimo ho feta liphetho tsa rona thutong ea hona joale (RMSE 95.479, MAE6 ea hona joale ea 70, 3 ea hona joale ea R70). ea Tarasov et al.36 (0.544), ho hlakile hore coefficient of determination (R2) e phahame ka mokhoa ona o tsoakiloeng. Moeli oa phoso (RMSE le MAE) (EBK SVMR) bakeng sa mohlala o tsoakiloeng o ka tlaase ka makhetlo a mabeli. Ka mokhoa o ts'oanang, Sergeev et al.34 e ngotse 0.28 (R2) bakeng sa mofuta o tsoetseng pele oa lebasetere (Multila) oa morao-rao oa thuto ea hybrid (Multila) ea morao-rao ea Rekordron6 ea morao-rao. (R2).Boemo ba ho bolela esale pele ho nepahala ha mohlala ona (EBK SVMR) ke 63.7%, ha ho nepahala ha ho bolela esale pele ho fumanoe ke Sergeev et al.34 ke 28%.'Mapa oa ho qetela (setšoantšo sa 5) o entsoeng ka mokhoa oa EBK_SVMR le Ca_Mg_K e le selelekela se bonts'a likhakanyo tsa libaka tse chesang le nickel e itekanetseng ho ea sebakeng sohle sa thuto.Sena se bolela hore bongata ba nickel sebakeng sa thuto ke haholo-holo bo itekanetseng, bo nang le lintlha tse phahameng libakeng tse itseng tse itseng.
'Mapa oa ho bolela esale pele o emeloa ho sebelisoa mofuta o nyalisitsoeng oa EBK_SVMR le ho sebelisoa Ca_Mg_K joalo ka ponelopele.['Mapa oa kabo ea sebaka o entsoe ho sebelisoa RStudio (version 1.4.1717: https://www.rstudio.com/).]
E hlahisitsoe ho Setšoantšo sa 6 ke likhahla tsa PTE e le sefofane sa sebopeho se nang le li-neurone ka bomong. Ha ho le e 'ngoe ea lifofane tsa likarolo tse bontšang mokhoa o tšoanang oa mebala joalokaha ho bontšitsoe.Leha ho le joalo, palo e nepahetseng ea li-neurone ka' mapa o mong le o mong o entsoeng ke 55.SeOM e hlahisoa ho sebelisoa mebala e sa tšoaneng, 'me ha e ntse e tšoana le mebala ea mebala, ho bapisa haholoanyane thepa ea lisampole, ho latela mebala ea tsona, ho ea ka likarolo tsa 'mala o le mong, ho ea ka likarolo tse le 'ngoe tsa MCa, ho latela mebala e le 'ngoe. Ka hona, CaK le CaMg li arolelana lintho tse ling tse tšoanang le li-neurone tse phahameng haholo le lipaterone tsa mebala e tlase ho isa ho e leka-lekaneng. Mehlala ka bobeli e bolela esale pele hore ho na le Niron mobung ka ho bonts'a mebala e mahareng ho isa holimo ea mebala e kang khubelu, lamunu le mosehla. mohlala o phahameng oa 'mala o bontšang hore na nickel e ka' na ea e-ba teng mobung (sheba setšoantšo sa 4) . mobu.Setšoantšo sa 7 se tšoantšetsa mokhoa oa contour ho sehlopha sa k-mekhoa 'mapeng, se arotsoe ka lihlopha tse tharo ho latela boleng bo boletsoeng esale pele moetsong o mong le o mong.Mokhoa oa contour o emela palo e nepahetseng ea lihlopha.Ho lisampole tsa mobu tse 115 tse bokeletsoeng, sehlopha sa 1 se fumane lisampole tse ngata ka ho fetisisa tsa mobu, 74. Cluster 2 e ile ea fumana disampole tse 33 tsa simpone e le 33. pliified ho lumella tlhaloso e nepahetseng ea lihlopha
Sephetho sa sefofane se hlahisoang ke mochini o mong le o mong oa Empirical Bayesian Kriging Support Vector Machine (EBK_SVM_SeOM).['Mapa oa SeOM o entsoe ho sebelisoa RStudio (version 1.4.1717: https://www.rstudio.com/).]
Likarolo tse fapaneng tsa sehlopha sa lihlopha ['mapa oa SeOM o entsoe ho sebelisoa RStudio (mofuta oa 1.4.1717: https://www.rstudio.com/).]
Boithuto ba hajoale bo bontša ka ho hlaka mekhoa ea ho etsa mohlala bakeng sa likhakanyo tsa nickel mobung oa litoropo le o haufi le litoropo. Phuputso e ile ea leka mekhoa e fapaneng ea ho etsa mohlala, ho kopanya likarolo le mekhoa ea mohlala, ho fumana tsela e molemohali ea ho lepa likhakanyo tsa nickel mobung. kabo ea tial ea likarolo tse bontšitsoeng ke EBK_SVMR (bona Setšoantšo sa 5) . Liphetho li bonts'a hore mochini oa ts'ehetso oa vector regression model (Ca Mg K-SVMR) o bolela esale pele mahloriso a Ni mobung e le mohlala o le mong, empa litekanyetso tsa ho netefatsa le ho nepahala li bonts'a liphoso tse phahameng haholo ho latela RMSE le MAE. ea coefficient of determination (R2) .Liphetho tse ntle li ile tsa fumanoa ho sebelisoa EBK SVMR le metsoako e kopantsoeng (CaKMg) e nang le liphoso tse fokolang tsa RMSE le MAE ka ho nepahala ha 63.7%.Hoa bonahala hore ho kopanya EBK algorithm le mochine oa ho ithuta algorithm ho ka hlahisa algorithm e nyalisitsoeng e ka khonang ho bolela esale pele mahloriso a PTE e le hore Mg e ka bolela esale pele sebaka sa PTE e le ho bolela esale pele sebaka sa thuto ea Nig. ion of Ni mobung.Sena se bolela hore ts'ebeliso e tsoelang pele ea manyolo a thehiloeng ho nickel le tšilafalo ea liindasteri ea mobu ke indasteri ea tšepe e na le tšekamelo ea ho eketsa bongata ba nickel mobung.Phuputso ena e senotse hore mohlala oa EBK o ka fokotsa boemo ba phoso le ho ntlafatsa ho nepahala ha mohlala oa kabo ea sebaka sa mobu metseng ea litoropo kapa peri-urban ho sebelisa mobu oa projeke ka kakaretso PSVR ka kakaretso mobung oa propose ho ea ka har'a mebu ea libaka tsa marang-rang tsa PSVT. ;ho feta moo, re etsa tlhahiso ea ho sebelisa EBK ho kopanya ka mekhoa e sa tšoaneng ea ho ithuta mochine.leha ho le joalo, ho sebelisa li-covariate tse ngata ho ne ho tla ntlafatsa haholo ts'ebetso ea mohlala, e ka nkoang e le moeli oa mosebetsi oa hona joale.Moeli o mong oa thuto ena ke hore palo ea li-datasets ke 115. Ka hona, haeba ho fanoe ka lintlha tse ngata, ts'ebetso ea mokhoa o hlophisitsoeng o ntlafalitsoeng oa hybridization o ka ntlafatsoa.
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