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Kusvibiswa kwevhu idambudziko guru rinokonzerwa nemabasa evanhu.Kuparadzirwa kwenzvimbo yezvinhu zvingangove zvine chepfu (PTEs) kunosiyana munzvimbo zhinji dzemumaguta uye dziri pedyo neguta.Nekudaro, zvinonetsa kufanotaura nepakati pezvinhu zvePTEs muvhu rakadaro.Kukwana kwe115 samples dzakawanikwa kubva kuFrydek Mistek ku Czech Republic.), magnesium nickel kutarisisa kwakagadziriswa kushandisa inductively coupled plasma emission spectrometry.Kupindurwa kwemhinduro ndeyeNi uye vanofanotaura vari Ca, Mg, uye K.Matrix yekubatanidza pakati pemhinduro yakasiyana uye inofananidzira yakasiyana inoratidza kuwirirana kunogutsa pakati pezvinhu.Zvigumisiro zvekufungidzira zvakaratidza kuti Support Vector Machine Regression (SVMR) yakaita zvakanaka, kunyange zvazvo inofungidzirwa kuti root. absolute error (MAE) (166.946 mg/kg) dzaive dzakakwirira kudarika dzimwe nzira dzakashandiswa.Mixed models for Empirical Bayesian Kriging-Multiple Linear Regression (EBK-MLR) inoita zvisina kunaka, sezvinoratidzwa ne coefficients of determination less than 0.1.The Empirical Bayesian Kriging-Sups model, EBK-Sups Model yainzi Regression Machine ine yakaderera RMSE (95.479 mg/kg) uye MAE (77.368 mg/kg) uye yakakwirira coefficient of determination (R2 = 0.637) .The EBK-SVMR modelling technique output inobuda inoonekwa pachishandiswa mepu inozvironga.Clustered neuron mundege ye hybrid modelK inofanotaura maconcentrations emurban modelK SVR inofanotaura CakMg SVR. uye peri-urban ivhu.Zvabuda zvinoratidza kuti kubatanidza EBK neSVMR inzira inoshanda yekufungidzira Ni concentrations mudhorobha neperi-urban ivhu.
Nickel (Ni) inonzi micronutrient yemiti nokuti inobatsira kumhepo ye nitrogen fixation (N) uye urea metabolism, iyo yose inodiwa pakukura kwembeu.Kuwedzera kune mupiro wayo pakumera kwembeu, Ni inogona kuita sefungal uye bhakitiriya inhibitor uye inosimudzira kukura kwemiti. zvinoda kuiswa kwefotereza ine nickel-based optimize nitrogen fixation2.Kuramba kuchiiswa mafetireza ane nickel kuti ivhu ripfumise uye kuwedzera kugona kwenyemba kugadzirisa nitrogen muvhu kunoramba kuchiwedzera nickel concentration muvhu.Kunyange zvazvo nickel iri micronutrient yezvirimwa, kuwandisa kwayo muvhu kunogona kuita muchetura muvhu kudarika nick nick yakanaka. kutorwa kwesimbi semushonga unokosha wekukura kwemiti1. Maererano naLiu3, Ni yakawanikwa iine 17th inokosha inokosha inodiwa pakukura kwemiti uye kukura.Kuwedzera kune nickel basa mukukura kwemiti uye kukura, vanhu vanozvida kune zvakasiyana-siyana zvekushandisa.Electroplating, kugadzirwa kwe nickel-based alloys, uye kugadzirwa kwezvigadziriswe zvekugadzira motokari uye kugadzirwa kweignitive indasitiri yemagetsi. Uye zvakare, nickel-based alloys uye electroplated zvinyorwa zvakashandiswa zvakanyanya mukitchenware, ballroom accessories, chikafu muindasitiri yezvekudya, magetsi, waya netambo, jet turbines, ekuvhiya implants, machira, uye kuvaka ngarava5. kupfuura anthropogenic4,6.Nzvimbo dzechisikigo dzenickel dzinosanganisira kuputika kwemakomo, zvinomera, moto wemasango, uye geological process; zvisinei, anthropogenic masosi anosanganisira nickel/cadmium mabhatiri muindasitiri yesimbi, electroplating, arc welding, dhiziri nemafuta emafuta, uye kuburitswa kwemuchadenga kubva mukupisa kwemarasha uye tsvina uye sludge incineration Nickel accumulation7,8.According to Freedman and Hutchinson9 and Manyiwat al. 10, nzvimbo huru dzekusvibiswa kwepamusoro pevhu munzvimbo yepedyo uye yakatarisana zvakanyanya kunyanya nickel-copper-based smelters nemigodhi.Ivhu repamusoro rakapoteredza Sudbury nickel-copper refinery muCanada raiva nepamusoro-soro yekusvibiswa kwe nickel pa 26,000 mg / kg11.Mukusiyana kweRussia nickel kugadzirwa kwepamusoro kune nickel mukugadzirwa kwepamusoro kune nickel. Norwegian ivhu11.According to Alms et al. 12, huwandu hweHNO3-extractable nickel munzvimbo yepamusoro inorimika yenharaunda (nickel production muRussia) yakabva pa6.25 kusvika 136.88 mg/kg, inoenderana nehuremu hwe30.43 mg/kg uye baseline concentration ye25 mg/kg.Maererano nekabata 11, kushandiswa kwevhu remurban phosphorus kana muvhu rekurima mwaka unotevedzana wezvirimwa unogona kupinza kana kusvibisa ivhu.Zvinogona kuitika zve nickel muvanhu zvinogona kutungamirira kugomarara kuburikidza ne mutagenesis, chromosomal damage, Z-DNA generation, blocked DNA excision repair, kana epigenetic process13.Mukuedza kwemhuka, nickel yakawanikwa iine simba rekukonzera zvakasiyana-siyana, uye carcinogenic nickel nickel complexes inogona kubuda.
Kuongororwa kwekusvibiswa kwevhu kwakabudirira munguva pfupi yapfuura nekuda kwehutano hwakasiyana-siyana hunokonzerwa nehukama hwevhu-chirimwa, ivhu nevhu rehukama hwehupenyu, kuparara kwezvisikwa, uye kuongororwa kwemamiriro ekunze. yakanyanya kunatsiridza predictive ivhu mapping (PSM).According to Minasny and McBratney16, predictive soil mapping (DSM) has proved to be a prominent subdiscipline of ivhu science.Lagacherie and McBratney, 2006 define DSM as "kusika uye kuzadza kwe spatial ivhu ruzivo masisitimu kuburikidza nekushandiswa kwenzvimbo yepasi uye nzira dzerabhoritari. inference systems”.McBratney et al. 17 inotsanangura kuti DSM yemazuva ano kana PSM ndiyo inonyanya kushanda nzira yekufanotaura kana kuisa mapeji ekugoverwa kwepakati kwePTEs, marudzi evhu uye zvinhu zvevhu.Geostatistics uye Machine Learning Algorithms (MLA) iDSM modelling techniques inogadzira mamepu edigitized nerubatsiro rwemakombiyuta anoshandisa data inokosha uye shoma.
Deutsch18 neOlea19 vanotsanangura geostatistics se "muunganidzwa wenhamba dzematekiniki dzinobata nekumiririrwa kwenzvimbo, kunyanya kushandisa stochastic modhi, senge maitirwo enguva yekuongorora data data yenguva." Kunyanya, geostatistics inosanganisira kuongororwa kwevariograms, iyo inobvumira Quantify uye kutsanangura kutsamira kwemaitiro enzvimbo kubva kune yega dataset20.Gumiaux et al. 20 inotsanangura zvakare kuti kuongororwa kwevariograms mu geostatistics kunobva pamitemo mitatu, kusanganisira (a) computing chiyero che data correlation, (b) kuziva uye computing anisotropy mu dataset disparity uye (c) mukuwedzera kune Kuwedzera pakufunga kukanganisa kwechipimo che data yechiyero yakaparadzaniswa kubva kune mhedzisiro inofungidzirwa yenzvimbo iyi mhedzisiro yenzvimbo, maitiro anofungidzirwa enzvimbo. anoshandiswa mune geostatistics, kusanganisira general kriging, co-kriging, yakajairika kriging, empirical Bayesian kriging, nyore kriging nzira uye dzimwe dzinonyatsozivikanwa nzira dzekududzira kumepu kana kufanotaura PTE, maitiro evhu, uye marudzi evhu.
Machine Learning Algorithms (MLA) inyanzvi itsva inoshandisa makirasi akakura asiri-mutsara data, inokurudzirwa nealgorithms inonyanya kushandiswa pakuchera data, kuona mapatani mu data, uye yakadzokororwa kushandiswa pakuiswa muzvikamu zvesainzi senge sainzi yevhu uye kudzorera mabasa.Mapepa akawanda ekutsvakurudza anovimba nemienzaniso yeMLA kufanotaura PTE muvhu, seTan et al. 22 (masango asina kujairika eheavy metal estimation muvhu rekurima), Sakizadeh et al. 23 (modelling uchishandisa kutsigira vector michina uye artificial neural network) kusvibiswa kwevhu) .Mukuwedzera, Vega et al. 24 (CART yekuenzanisira heavy metal retention uye adsorption muvhu) Sun et al. 25 (kushandiswa kwe cubist ndiko kugoverwa kweCd muvhu) uye mamwe maalgorithms akadai se k-pedyo muvakidzani, generalized boosted regression, uye boosted regression Miti yakaisawo MLA kufanotaura PTE muvhu.
Kushandiswa kweDSM algorithms mukufanotaura kana mepu kunotarisana nematambudziko akawanda.Vanyori vakawanda vanotenda kuti MLA yakakwirira kune geostatistics uye zvinopesana.Kunyange zvazvo imwe iri nani pane imwe, kusanganiswa kwezviviri izvi kunovandudza chiyero chekururama kwemepu kana kufanotaura muDSM15.Woodcock uye Gopal26 Finke27; Pontius naCheuk28 naGrunwald29 vanotaura pamusoro pekusakwana uye zvimwe zvikanganiso mukufanotaura mepu yevhu.Masayendisiti evhu akaedza nzira dzakasiyana-siyana dzekugadzirisa kushanda, kunyatsoita, uye kufanotaura kweDSM mepu nekufungidzira.Kusanganiswa kwekusava nechokwadi uye kuongorora ndechimwe chezvinhu zvakasiyana-siyana zvakabatanidzwa muDSM kuti zvigadzirise uye zvigadzirise. 15 inotsanangura kuti maitiro ekusimbisa uye kusava nechokwadi kunounzwa nekugadzirwa kwemepu uye kufanotaura kunofanirwa kusimbiswa zvakazvimiririra kuti ivandudze kunaka kwemepu.Mipimo yeDSM imhaka yemhando yevhu yakapararira, iyo inosanganisira chikamu chekusagadzikana; zvisinei, kushayikwa kwechokwadi muDSM kunogona kubuda kubva kune dzakawanda zvinyorwa zvekukanganisa, zvinoti covariate kukanganisa, kukanganisa kwemuenzaniso, kukanganisa kwenzvimbo, uye analytical Error 31.Modelling zvisizvo zvakakonzerwa muMLA uye geostatistical maitiro akabatanidzwa nekusanzwisisa, pakupedzisira zvichiita kuti kuwedzeredza kwemaitiro chaiwo32. Pasinei nemamiriro ezvinhu, kuenzanisa kwemuenzaniso, kuenzaniswa kwemuenzaniso, kuenzanisira kwemuenzaniso, kuenzanirana kweparameter. masvomhu muenzaniso kufanotaura, kana interpolation33. Munguva pfupi yapfuura, itsva DSM muitiro yakabuda kuti inokurudzira kubatanidzwa geostatistics uye MLA mumepu uye kufanotaura.Several ivhu masayendisiti uye vanyori, akadai Sergeev et al. 34; Subbotina et al. 35; Tarasov et al. 36 uye Tarasov et al. 37 vakashandisa iyo chaiyo mhando ye geostatistics uye muchina kudzidza kugadzira mahybrid modhi anovandudza kugona kwekufanotaura uye mepu. mhando.Mamwe emhando idzi dzakasanganiswa kana dzakasanganiswa dzealgorithm dzinoti 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-MLP-anK3K Processor-Multilayer Perceptron (ANKsi-3K) uye ANNRK3K. Kudzokera shure38.
Maererano naSergeev et al., kubatanidza maitiro akasiyana-siyana ekuenzanisira ane mikana yekubvisa kukanganisa uye kuwedzera kushanda kweiyo yakakonzerwa nemhando yakasanganiswa panzvimbo yekugadzira muenzaniso wayo mumwe chete.Muchirevo chechinyorwa chino, bepa idzva rino rinopokana kuti zvakakosha kushandisa algorithm ye geostatistics neMLA yakasanganiswa kuti igadzire mamodheru akakwana ekufanotaura hupfumi hweNi mumaguta uye peri-urban Bay. (EBK) seyokutanga modhi uye woisanganisa neSupport Vector Machine (SVM) uye Multiple Linear Regression (MLR) modhi.Hybridization yeEBK chero ipi zvayo MLA haizivikanwi.Mishonga yakawanda yakasanganiswa inoonekwa misanganiswa yezvakajairwa, zvakasara, regression kriging, uye MLA.EBK igeostatistical interpolation method iyo inoshandisa nzvimbo ye spastic process non-stationary/stationary random field with defined localization parameters over the field, kubvumira kushanduka kwenzvimbo39.EBK yakashandiswa muzvidzidzo zvakasiyana-siyana, kusanganisira kuongorora kugoverwa kwekabhoni kabhoni muvhu repurazi40, kuongorora kusvibiswa kwevhu41 uye kugadzira mapeji evhu42.
Kune rimwe divi, Self-Organising Graph (SeOM) is a algorithm yekudzidza iyo yakashandiswa muzvinyorwa zvakasiyana seLi et al. 43, Wang nevamwe. 44, Hossain Bhuiyan et al. 45 naKebonye et al.46 Sarudzai hunhu hwenzvimbo uye kuunganidzwa kwezvinhu.Wang et al. 44 inotsanangura kuti SeOM inyanzvi yekudzidza ine simba inozivikanwa nekugona kwayo kuunganidza uye kufungidzira matambudziko asina mutsara.Kusiyana nemamwe maitiro ekuzivikanwa kwepattern akadai seyeprincipal component analysis, fuzzy clustering, hierarchical clustering, uye multi-criteria sarudzo yekuita, SeOM iri nani pakuronga uye kuziva PTE mapatani. 44, SeOM inogona kuunganidza nzvimbo kugovera neuroni dzine hukama uye kupa yakakwirira-resolution data visualization.SeOM ichaona Ni yekufanotaura dhata kuti iwane yakanakisa modhi kuratidza mhedzisiro yekududzira kwakananga.
Pepa rino rine chinangwa chekugadzira chimiro chemepu chakasimba chine huchokwadi hwekufanotaura nickel zvirimo muivhu remudhorobha nepakati pemadhorobha. Isu tinofungidzira kuti kuvimbika kweiyo yakasanganiswa modhi kunonyanya kuenderana nekurudziro yemamwe mamodheru akabatana neiyo base modhi. saka, tichaedza kupindura mibvunzo yetsvakurudzo inogona kupa mienzaniso yakavhenganiswa.Zvisinei, muenzaniso wakarurama sei pakufanotaura chinhu chinotarirwa?Uyezve, ndeipi nhanho yekuongorora kwekushanda kunobva pakusimbiswa uye kuongororwa kwechokwadi?Naizvozvo, zvinangwa zvakananga zvechidzidzo ichi chaiva (a) kugadzira musanganiswa wakasanganiswa muenzaniso weSVMR kana MLR uchishandisa EBK (c) kuenzanisa mhedzisiro yemuenzaniso wepamusoro, kuenzanisa mhedzisiro yemuenzaniso wepro. Niconcents muivhu remudhorobha kana peri-urban , uye (d) mashandisirwo eSeOM kugadzira mepu ineshongedzo yepamusoro yekusiyana kwenickel spatial.
Chidzidzo ichi chirikuitwa muCzech Republic, kunyanya mudunhu reFrydek Mistek mudunhu reMoravia-Silesian (ona Mufananidzo 1) .Nzvimbo yenzvimbo yekudzidza yakaoma zvikuru uye inonyanya chikamu cheMoravia-Silesian Beskidy nharaunda, iyo iri chikamu chekunze kweMakomo eCarpathian.Nzvimbo yekudzidza iri pakati peN1′10 ° 4 ne2'19 ° ′ 4 ′ 49 ° 0'E, uye urefu huri pakati pe225 ne327 m; zvisinei, iyo Koppen classification system yemamiriro ekunze enharaunda inonzi Cfb = temperate oceanic climate, Kune mvura zhinji inonaya kunyangwe mumwedzi yakaoma.Tembiricha dzinosiyana zvishoma mugore rose pakati -5 °C ne24 °C, isingawanzo kudonha pasi -14 °C kana pamusoro peavhareji ye30 °C uye 52 ° C, avhareji ye30 ° C uye 52 C. mm47. Iyo inofungidzirwa nzvimbo yekuongorora yenzvimbo yese inosvika 1,208 square kilometers, ine 39.38% yevhu rakarimwa uye 49.36% yemasango akavharwa.Kune rimwe divi, nzvimbo yakashandiswa muchidzidzo ichi inosvika 889.8 square kilometers.Mu nekupoterera Ostrava, indasitiri yesimbi uye simbi inoshandiswa muindasitiri yesimbi inoshaikwa. (semuenzaniso wekupokana nekuora kwemuchadenga) uye masimbi alloy (nickel inowedzera simba reiyo alloy uku ichichengetedza yakanaka ductility uye kuomarara), uye kurima kwakanyanya senge fosifati yekushandisa uye kugadzirwa kwezvipfuyo itsvakurudzo inogona kuwanika nickel munharaunda (semuenzaniso, kuwedzera nickel kumakwayana kuwedzera kukura kwekukura mumakwayana emombe uye kushandiswa kwemaindasitiri munzvimbo dzayo dze nickel). electroplating, kusanganisira electroplating nickel uye electroless nickel plating process.Ivhu zvinhu zviri nyore kusiyaniswa kubva muruvara rwevhu, marongerwo, uye carbonate content.Ivhu remukati riri pakati nepakati, rinobva kune mubereki zvinhu.Izvo zvinonzi colluvial, alluvial kana aeolian muzvisikwa.Dzimwe nzvimbo dzevhu dzinoita semottled in the surface and subsoil, kazhinji dzinenge dziri mukongiri nemhando dzakasimba. nharaunda48.Nekukwirira kubva pa455.1 kusvika 493.5 m, macambisol anotonga Czech Republic49.
Mepu yenzvimbo yekudzidza [Mepu yenzvimbo yekudzidza yakagadzirwa pachishandiswa ArcGIS Desktop (ESRI, Inc, shanduro 10.7, URL: https://desktop.arcgis.com).]
Ivhu repamusoro rinosvika 115 rakawanikwa kubva kumaguta emumaguta uye kunharaunda yedhorobha mudunhu reFrydek Mistek. Muenzaniso wemuenzaniso wakashandiswa waive gidhi renguva dzose rine masampuli evhu akaparadzana 2 × 2 km kubva kune imwe, uye ivhu repamusoro rakayerwa pakadzika 0 kusvika 20 cm pachishandiswa chigadziro cheGPS chakabatwa nemaoko (Leica Zeno 5 mabhegi akaiswa muZiplobele akaiswa muGps, maSamples 5 akaiswa muGPS). kurabhoritari.Samples dzakaomeswa nemhepo kuti dzibudise pulverized samples, pulverized by a mechanical system (Fritsch disc mill), uye sieved (seve size 2 mm) Isa 1 giramu yevhu rakaoma, homogenized uye sieved sampuli dzevhu mumabhodhoro eteflon akanyorwa zvakajeka. Mumudziyo wega wega weTeflon, bvisa 5% 5% yeHC, shandisa 5% HC ml ye5% 5%. HNO3 (uchishandisa otomatiki dispenser - imwe yeasidhi imwe neimwe), vhara zvishoma uye bvumira masampuli kuti amire husiku hwese kuti aite (aqua regia program) .Isai supernatant pane inopisa simbi ndiro (tembiricha: 100 W uye 160 °C) kwe2 h kuti ifambise kugayiwa kwemaitiro emasamples, wobva watonhodza vhoriyamu 5 ml uye inotonhorera ku50 ml inotonhorera. ml nemvura yakasvibiswa.Mushure mezvo, sefa iyo yakasvibiswa supernatant mu 50 ml PVC chubhu nemvura yakasvibiswa.Kuwedzera, 1 ml yemushonga we dilution yakasvibiswa ne 9 ml yemvura yakasvibiswa uye yakasvibiswa mu 12 ml tube yakagadzirirwa PTE pseudo-concentration.The concentrations of PTEs, Cdb, Cdb, Cdb, PTEs, Cdb, Cdb, PTEs, Cdb Mg, K) yakagadziriswa neICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy) (Thermo Fisher Scientific, USA) maererano nemaitiro akaenzana uye kubvumirana.Ensure Quality Assurance and Control (QA / QC) maitiro (SRM NIST 2711a Montana II Ivhu) .PTEs yakashandiswa kubva pamiganhu iyi yekudzidza yakashandiswa kubva pachikamu ichi chekudzidza. yaiva 0.0004.(iwe) .Kuwedzera, kutonga kwehutano uye nzira yekusimbisa hutano hwekuongorora kwega kwega hunosimbiswa nekuongorora zvinyorwa zvinyorwa.Kuona kuti zvikanganiso zvakaderedzwa, kuongorora kaviri kwakaitwa.
Empirical Bayesian Kriging (EBK) ndeimwe yenzira dzakawanda dze geostatistical interpolation dzinoshandiswa mukuenzanisira munzvimbo dzakasiyana-siyana dzakadai sesayenzi yevhu.Kusiyana nedzimwe nzira dzekriging interpolation, EBK inosiyana nenzira dzechinyakare dzekriging nekutarisa kukanganisa kunofungidzirwa nemuenzaniso wesemivariogram. Semivariogram.Nzvimbo dzekududzira dzinoita nzira yekusava nechokwadi uye zvirongwa zvine chekuita nekuronga uku kwesemivariogram iyo inoumba chikamu chakaoma kwazvo chenzira yakakwana yekriging.Kududzira nzira yeEBK inotevedza nzira nhatu dzakatsanangurwa naKrivoruchko50, (a) muenzaniso unofungidzira semivariogram yenzvimbo yakafanotaurwa kukosha kubva kune yega yega dataset inoburitsa kukosha semivariogram uye (c) yekupedzisira A modhi inoverengerwa kubva kudheta rakateedzerwa.Mutemo weBayesian equation unopiwa seshure.
Apo \(Prob\left(A\right)\) inomiririra yekutanga, \(Prob\left(B\right)\) marginal probability inofuratirwa muzviitiko zvakawanda, \(Prob (B,A)\ ) .Kuverenga kwesemivariogram kunobva pamutemo weBayes, uyo unoratidza kurerekera kwekutarisa kwekutarisa datasets iyo inogona kugadzirwa kubva kusemiovari yedatabases inogona kugadzirwa kubva semiovari yedata. Mutemo weBayes, unotaura kuti zvingangoita sei kugadzira dataset yezvekutarisa kubva kune semivariogram.
Muchina wekutsigira vector muchina wekudzidza algorithm unoburitsa yakakwana yekuparadzanisa hyperplane kuti isiyanise zvakafanana asi kwete linearly yakazvimirira makirasi.Vapnik51 yakagadzira intest classification algorithm, asi ichangobva kushandiswa kugadzirisa matambudziko ekudzokera shure.Maererano naLi et al.52, SVM ndeimwe yeakanakisa classifier maitiro uye akashandiswa akasiyana siyana eSVMupport. Vector Machine Regression - SVMR) yakashandiswa mukuongorora uku.Cherkassky uye Mulier53 vakapayona SVMR se-kernel-based regression, iyo computation yakaitwa uchishandisa mutsara wekugadzirisa mutsara nemabasa akawanda enyika. kuna Vohland et al. 55, epsilon (ε) -SVMR inoshandisa dataset yakadzidziswa kuti iwane muenzaniso wekumiririra se epsilon-insensitive function iyo inoshandiswa kuisa mapepa e data yakazvimiririra nekanakisisa epsilon bias kubva pakudzidziswa pane data yakabatana.Kukanganisa kwepakati pekare kunoregererwa kubva pakukosha kwechokwadi, uye kana kukanganisa kwakakura kudarika ε (ε), iyo ivhu rekudzidzira rinoripirawo dhigirii yakaoma yemuenzaniso wedeta. subset of support vectors.Equation yakakurudzirwa neVapnik51 inoratidzwa pazasi.
apo b inomiririra scalar threshold, \(K\left({x}_{,}{ x}_{k}\kurudyi)\) inomiririra kernel function, \(\ alpha\) inomiririra Lagrange multiplier, N Inomiririra dhatabheti renhamba, \({x}_{k}\) inomiririra data kupinza, uye \.Ornels inoshandiswa SV data, uye \(yn\) ndiyo kiyi yedata. iGaussian radial basis function (RBF) .RBF kernel inoshandiswa kuti ione iyo yakakwana yeSVMR model, iyo inokosha kuti iwane yakanyanya kujeka chirango chakaiswa chinhu C uye kernel parameter gamma (γ) yePTE yekudzidzisa data.Chokutanga, takaongorora chirongwa chekudzidzisa ndokubva taedza kushanda kwemuenzaniso pakugadzirisa kushandiswa.
A multiple linear regression model (MLR) is a regression model inomiririra hukama pakati pemhinduro chinja uye nhamba yepredicor variables nekushandisa linear pooled parameters akaverengerwa achishandisa nzira shoma yema squares.MuMLR, a least squares model is a predictive function of ivhu zvinhu mushure mekusarudzwa kwetsanangudzo variables.Zvakakosha kushandisa mhinduro kuti uise mutsara wakashandiswa mutsara wakasiyana-siyana wetsanangudzo yemutsara.P nezvinotsanangudza.Equation yeMLR ndiyo
apo y ndiyo mhinduro yemhinduro, \(a\) ndiyo inodimburira, n inhamba yezvinofembera, \({b}_{1}\) kudzokororwa kwechidimbu kwemakoefifiti, \({x}_{i}\) inomiririra kufanotaura kana kutsanangura, uye \({\varepsilon }_{i}\) inomiririra kukanganisa zvakare mumuenzaniso.
Mamixed modhi akawanikwa ne sandwiching EBK ine SVMR neMLR. Izvi zvinoitwa nekutora hunhu hwakafanotaurwa kubva kuEBK kududzira. Izvo zvakafanotaurwa zvakakosha zvakawanikwa kubva kune yakapindirana Ca, K, uye Mg inowanikwa kuburikidza ne combinatorial maitiro kuti uwane mitsva mitsva, yakadai seCaK, CaMg, uye KMg. CaKMg.Zvose, zvipembenene zvakawanikwa ndezveCa, K, Mg, CaK, CaMg, KMg uye CaKMg.Izvi zvakasiyana-siyana zvakava zvigadziridzo zvedu, zvichibatsira kufanotaura nickel concentrations mumaguta uye peri-urban ivhu.Iyo SVMR algorithm yakaitwa pane zvakafanotaurwa kuti iwane yakavhenganiswa muenzaniso Empirical Bayesian Kriging-Supports, EBmilar variables, EBmilar Kriging-Support. kuburikidza neMLR algorithm kuti uwane yakasanganiswa modhi Empirical Bayesian Kriging-Multiple Linear Regression (EBK_MLR).Kazhinji, mabhii eCa, K, Mg, CaK, CaMg, KMg, uye CaKMg anoshandiswa seanofananidzira sekufungidzira kweNi content mudhorobha uye peri-urban ivhu yakawanikwa (EBKMl inogamuchirwa inogamuchirwa) EBKMl modhi yakawanikwa. uchishandisa girafu rinozvironga.Kufambiswa kwebasa kwechidzidzo ichi kunoratidzwa mumufananidzo 2.
Kushandisa SeOM chave chishandiso chakakurumbira chekuronga, kuongorora, uye kufanotaura data mubazi rezvemari, hutano, indasitiri, nhamba, sainzi yevhu, nezvimwe.SeOM inogadzirwa uchishandisa artificial neural network uye nzira dzekudzidza dzisina kutariswa dzesangano, kuongorora, uye kufanotaura.Muchidzidzo ichi, SeOM yakashandiswa kufungidzira Ni concentrations yeNi kutarisisa kunoenderana neiyo yakanakisa data yedhorobha. muSeOM evaluation inoshandiswa se n input-dimensional vector variables43,56.Melssen et al. 57 inotsanangura kubatanidzwa kwevector yekupinda mune neural network kuburikidza nechikamu chimwe chete chekuisa kune yakabuda yevheti ine uremu humwe chete.Kubuda kunogadzirwa neSeOM imepu-dimensional mepu inosanganisira neuroni dzakasiyana kana node dzakarukwa muhexagonal, circular, kana square topological mepu maererano nekuswedera kwavo.Kuenzanisa mapeji emapupu anoenderana nemepotric, kuenzanisa kweMQE quant (mhosho yeMQE) , uye kukanganisa kwemepu (Q) ine 0.086 uye 0.904, maererano, inosarudzwa, iyo iine 55-map unit (5 × 11) .Nuron structure inotarirwa maererano nehuwandu hwemanodhi muempirical equation.
Nhamba yedata yakashandiswa muchidzidzo ichi isampuli 115. Nzira isina kurongeka yakashandiswa kupatsanura data kuita data rebvunzo (25% yekusimbisa) uye seti yedata yekudzidziswa (75% yekuenzanisa).Data rekudzidzisa rinoshandiswa kugadzira regression modhi (calibration), uye dataset yebvunzo inoshandiswa kuonesa kugona kwegeneralization58.Izvi zvakaitwa kuti kuongororesa zvese zvakashandiswa mumamodeli evhu. maitiro gumi ekugadzirisa-kupfuura, anodzokororwa kashanu.Zvinosiyana zvinogadzirwa neEBK kududzira zvinoshandiswa sevanofanotaura kana zvinotsanangurwa zvakasiyana-siyana kufanotaura chinangwa chinoshanduka (PTE) .Modeling inoitwa muRStudio uchishandisa mabhuku epakiti (Kohonen), library (caret), library (modelr), library ("e1071"), library("plyr" library), library("plyr" library) ("Metrics").
Mienzaniso yakasiyana-siyana yekusimbisa yakashandiswa kugadzirisa muenzaniso wakanakisisa wakakodzera kufanotaura nickel concentrations muvhu uye kuongorora kururamisa kwemuenzaniso uye kusimbiswa kwayo.Hybridization mienzaniso yakaongororwa uchishandisa zvinoreva kukanganisa zvachose (MAE), root mean square error (RMSE), uye R-squared kana coefficient determination (R2) . muenzaniso.RMSE uye kusiyanisa ukuru muzviyero zvakazvimiririra zvinotsanangura simba rinofanotaura remuenzaniso, nepo MAE inosarudza kukosha kwehuwandu hwehuwandu.R2 inokosha inofanira kuva yakakwirira kuti iongorore zvakanakisisa musanganiswa wemuenzaniso uchishandisa zvigadziro zvekusimbisa, iyo inoswedera pedyo nekukosha ndeye 1, iyo yakakwirira yakarurama.Maererano naLi et al. 59, R2 criterion value ye0.75 kana mukuru inoonekwa seyakanaka kufanotaura; kubva ku0.5 kusvika ku0.75 inogamuchirwa maitiro emuenzaniso, uye pasi pe0.5 haigamuchirwi kushanda kwemuenzaniso.Pakusarudza muenzaniso uchishandisa nzira dzekuongorora nzira dzeRMSE uye MAE, maitiro ezasi akawanikwa akanga akakwana uye akaonekwa sechisarudzo chakanakisisa.Equation inotevera inotsanangura nzira yekuongorora.
apo n inomiririra saizi yemutengo wakaonekwa\({Y}_{i}\) inomiririra mhinduro yakayerwa, uye \({\widehat{Y}}_{i}\) inomiririrawo kukosha kwemhinduro yakafanotaurwa, nekudaro, kune yekutanga i ongororo.
Tsanangudzo yenhamba yezvinofanotaura uye mhinduro dzakasiyana dzinounzwa muTable 1, ichiratidza zvinoreva, standard deviation (SD), coefficient of variation (CV), zvishoma, maximum, kurtosis, uye skewness.Zvishoma uye zvakanyanya kukosha zvezvinhu zviri mukuderera kweMg
Iko kuwirirana kwezvinofananidzira zvigadziriswe nezvinhu zvinopindura zvakaratidza kuwirirana kunogutsa pakati pezvinhu (ona Mufananidzo 3) .Kubatana kwakaratidza kuti CaK yakaratidza kuwirirana kwepakati ne r value = 0.53, sezvakaita CaNi.Kunyange zvazvo Ca naK vachiratidza kushamwaridzana kwakadzikama kune mumwe nemumwe, vatsvakurudzi vakadai saKingston et al. 68 uye Santo69 inoratidza kuti mazinga avo muvhu anopesana.Zvisinei, Ca neMg zvinopesana neK, asi CaK inopindirana zvakanaka.Izvi zvinogona kunge zvakakonzerwa nekushandiswa kwemafetiraiza akadai se potassium carbonate, iyo inopfuura 56% mu potassium.Potassium yakanga yakabatana zvine mwero ne magnesium (KM 63 pedyo, potassium ine hukama huviri). magnesium sulfate, potassium magnesium nitrate, uye potashi zvinoiswa kune ivhu kuti iwedzere kushomeka kwadzo. inoderedza mhedzisiro yekuwedzeredza magnesium, uye zvese magnesium uye calcium zvinoderedza chepfu mhedzisiro yenickel muvhu.
Correlation matrix yezvinhu zvinoratidza hukama pakati pevanofungidzira uye mhinduro (Cherechedza: iyi nhamba inosanganisira scatterplot pakati pezvinhu, kukosha kwemazinga kunobva p <0,001).
Mufananidzo 4 unoratidza kugoverwa kwenzvimbo yezvinhu.Maererano naBurgos et al70, kushandiswa kwenzvimbo yekugovera inyanzvi inoshandiswa kuverenga uye kuratidza nzvimbo dzinopisa munzvimbo dzakasvibiswa.Mazinga ekupfumisa eCa mumufananidzo 4 anogona kuonekwa kuchamhembe kwakadziva kumadokero kwemepu yekugovera nzvimbo.Mufananidzo unoratidza kupfumisa kupfumisa kwepamusoro-soro kweCasium. zvingangove zvichikonzerwa nekushandiswa kwequicklime (calcium oxide) kuderedza acidity yevhu uye kushandiswa kwayo muzvigayo zvesimbi sealkaline oxygen mukugadzirwa kwesimbi.Kune rumwe rutivi, vamwe varimi vanosarudza kushandisa calcium hydroxide muvhu rine acidic kuti neutralize pH, iyo inowedzerawo calcium content yevhu71.Potassium inoratidzawo nzvimbo dzinopisa kuchamhembe kwakadziva kumadokero uye kuchamhembe kwakadziva kumadokero, kuchamhembe kwakadziva kumadokero kwenharaunda yekurima yekurima-yepamusoro-soro yemepu. zvinogona kunge zvakakonzerwa neNPK uye potashi applications.Izvi zvinopindirana nezvimwe zvidzidzo, zvakadai seMadaras uye Lipavský72, Madaras et al.73, Pulkrabová et al.74, Asare et al.75, avo vakacherechedza kuti kugadzikana kwevhu uye kurapwa neKCl neNPK kwakaguma nehuwandu hweK huri muvhu. Spatial Potassium hupfumi kuchamhembe kwakadziva kumadokero kwemepu yekugovera inogona kunge yakakonzerwa nekushandiswa kwe potassium-based fertilizer yakadai se potassium chloride, potassium sulfate, potassium nitrate, potashi, uye potashi kuwedzera potassium yehuwandu hwevhu rakashata.Zádorová et al. 76 naTlustoš et al. 77 yakaratidza kuti kushandiswa kweK-based fertilizer kwakawedzera huwandu hweK muvhu uye kwaizowedzera zvakanyanya ivhu rekudya kwevhu munguva refu, kunyanya K uye Mg ichiratidza nzvimbo inopisa muvhu. Zvine mwero hotspots kuchamhembe kwakadziva kumadokero kwemepu uye kumaodzanyemba kwakadziva kumabvazuva kwemepu. chlorosis.Magnesium-based fetireza, senge potassium magnesium sulfate, magnesium sulfate, uye Kieserite, inobata kushomeka (zvinomera zvinoratidzika zvepepuru, zvitsvuku, kana shava, zvichiratidza kushomeka kwemagnesium) muvhu rine pH range6. production78.
Kugoverwa kwenzvimbo kwezvinhu [mepu yekugovera nzvimbo yakagadzirwa pachishandiswa ArcGIS Desktop (ESRI, Inc, Version 10.7, URL: https://desktop.arcgis.com).]
Muenzaniso wekuita index index yezvikamu zvakashandiswa muchidzidzo ichi zvinoratidzwa muTable 2.Kune rumwe rutivi, RMSE uye MAE yeNi zvose zviri pedyo ne zero (0.86 RMSE, -0.08 MAE) .Kune rumwe rutivi, zvose RMSE uye MAE values yeK inogamuchirwa.RMSE uye MAE migumisiro yakanga yakakura kune calcium uye magnesium yakawanda uye data yakakura nekuda kweRMSE data. RMSE uye MAE yechidzidzo ichi vachishandisa EBK kufanotaura Ni vakawanikwa vari nani pane mhedzisiro yaJohn et al. 54 tichishandisa synergistic kriging kufanotaura maS concentrations muvhu tichishandisa data rakaunganidzwa rakafanana.Zvakabuda zveEBK zvatakadzidza zvinoenderana neizvo zveFabijaczyk et al. 41, Yan nevamwe. 79, Beguin nevamwe. 80, Adhikary et al. 81 naJohani nevamwe. 82, kunyanya K naNi.
Kuitwa kwemaitiro ega ega ekufanotaura nickel zvirimo muivhu remudhorobha nepakati pemadhorobha kwakaongororwa pachishandiswa maitiro emhando (Table 3) .Kusimbiswa kwemuenzaniso uye kuongorora kwechokwadi kwakasimbisa kuti Ca_Mg_K predictor yakasanganiswa neEBK SVMR model yakapa the best performance.Calibration model Ca_Mg_KMR square mean Ca_Mg_K-EBKR model Ca_Mg_K-EBKR yeRM2 absolute error (MAE) vaiva 0.637 (R2), 95.479 mg/kg (RMSE) uye 77.368 mg/kg (MAE) Ca_Mg_K-SVMR yaiva 0.663 (R2), 235.974 mg/kg (RMSE) uye 16kg (lessthewereE) yakanaka R2. Ca_Mg_K-SVMR (0.663 mg/kg R2) uye Ca_Mg-EBK_SVMR (0.643 = R2); yavo RMSE uye MAE mhinduro dzaive dzakakwirira kudarika dzeCa_Mg_K-EBK_SVMR (R2 0.637) (ona Tafura 3) . Uyezve, RMSE uye MAE yeCa_Mg-EBK_SVMR (RMSE = 1664.64 uye MAE = 1031.49) muenzaniso uye 17.5 yakakura kupfuura iyo 17.5. Ca_Mg_K-EBK_SVMR. Saizvozvowo, iyo RMSE neMAE yeCa_Mg-K SVMR (RMSE = 235.974 uye MAE = 166.946) modhi i2.5 uye 2.2 hombe pane ayo eCa_Mg_K-EBK_SVMR anoratidza sei RMSE data. ine mutsara wezvakanakisa zvakakwana.Yepamusoro RSME uye MAE yakaonekwa.Maererano naKebonye et al. 46 naJohn nevamwe. 54, kana RMSE neMAE iri pedyo kusvika zero, zvinova nani mhedzisiro.SVMR neEBK_SVMR dzine hukuru hwepamusoro hweRSME neMAE. Zvakaonekwa kuti fungidziro dzeRSME dzaive dzakakwirira nguva dzese pane dzeMAE, zvichiratidza kuvepo kwevakabuda kunze.Maererano neLegates, aMcCabso anoreva 3 kukanganisa ku8 (MAE) inokurudzirwa sechiratidzo chekuvapo kwevanobuda kunze.Izvi zvinoreva kuti iyo yakawanda yakasiyana-siyana yedataset, iyo yakakwirira yeMAE neRMSE inokosha.Kururamisa kwekuongorora kwe-cross-validation yeCa_Mg_K-EBK_SVMR yakavhenganiswa muenzaniso wekufanotaura Ni content mumaguta emuguta uye emuguta remuguta raiva 63.70% eLitcco. 59, iyi nhanho yekururama ndiyo inogamuchirwa yemuenzaniso wekuita chiyero.Zvigumisiro zviripo zvinofananidzwa nekudzidza kwekare naTarasov et al. 36 iyo yemhando yehybrid yakagadzira MLPRK (Multilayer Perceptron Residual Kriging), ine chekuita neEBK_SVMR yekuongorora indekisi yakashumwa muchidzidzo chazvino, RMSE (210) uye The MAE (167.5) yaive yakakwira kupfuura zvatakawana muchidzidzo chazvino (RMSE 95.479, MAE6 yerizvino yeR2,3) (0.637) neiyo yaTarasov et al. 36 (0.544), zviri pachena kuti coefficient of determination (R2) yakakwirira mumuenzaniso uyu wakavhenganiswa.Muganho wekukanganisa (RMSE uye MAE) (EBK SVMR) yemuenzaniso wakavhenganiswa wakaderera kaviri. Saizvozvowo, Sergeev et al.34 vakanyora 0.28 (R2) nokuda kweiyo yakagadziriswa hybrid model (Multila rekodhi rekodhi rekoredhi yemazuva ano yekudzidza) 0.637 (R2) .Nyero yekufanotaura kweiyo modhi (EBK SVMR) ndeye 63.7%, nepo kufanotaura kwechokwadi kwakawanikwa naSergeev et al. 34 ndeye 28% Mepu yekupedzisira (Fig. 5) yakagadzirwa uchishandisa EBK_SVMR modhi uye Ca_Mg_K sefanorongedzerwa inoratidza kufanotaura kwenzvimbo dzinopisa uye ine mwero kune nickel pamusoro penzvimbo yese yekudzidza.Izvi zvinoreva kuti kuwanda kwe nickel munzvimbo yekudzidza kunonyanya kuve kwakadzikama, ine huwandu hwakanyanya mune dzimwe nzvimbo dzakananga.
Mepu yekupedzisira yekufungidzira inomiririrwa pachishandiswa hybrid modhi EBK_SVMR uye kushandisa Ca_Mg_K sekufanofungidzira.[Mepu yekuparadzira nzvimbo yakagadzirwa pachishandiswa RStudio (vhezheni 1.4.1717: https://www.rstudio.com/).]
Inoratidzwa muMufananidzo 6 ndeye PTE yekutarisa se ndege yekugadzira inosanganisira neuroni yega yega.Hapana chimwe chezvikamu zvezvikamu zvakaratidza mufananidzo wemuvara wakafanana sezvakaratidzwa.Zvisinei, nhamba yakakodzera ye neuroni pamepu yakadhirowa ndeye 55.SeOM inogadzirwa uchishandisa mavara akasiyana-siyana, uye iyo yakawanda yakafanana nemhando yemavara, iyo inofananidzwa nemaitiro ezvinyorwa zvemuenzaniso, maererano neMCaro, zvichienderana nemamiriro ezvinhu, maererano neMCare, zvichienderana neMCaro, zvichienderana nemamiriro ezvinhu, maererano neMCare, zvichienderana neMCa. maitiro emavara akafanana kune imwechete high neurons uye yakawanda yakaderera neuroni.Nokudaro, CaK neCaMg vanogovana zvimwe zvakafanana nepamusoro-soro neuroni uye yakaderera-ku-yemwero yemavara emhando.Mienzaniso miviri inofanotaura kuwanda kweNi muvhu nekuratidzira pakati nepamusoro mavara emavara akadai sematsvuku, orenji uye yero.KMg modhi inoratidza akawanda emhando yepamusoro maitiro anoenderana nechaiyo proportions kusvika kune yakaderera yeruvara kugovera uye kudzika kwepakati peruvara kugovera. muenzaniso wezvikamu zvemuenzaniso wakaratidza chimiro chepamusoro chemavara chinoratidza mukana wekugadzirisa nickel muvhu (ona Mufananidzo 4) .Ndege yechikamu cheCakMg inoratidza mararamiro akasiyana-siyana kubva pasi kusvika kumusoro zvichienderana nechiyero chemavara chakarurama. Uyezve, kufanotaura kwemuenzaniso wezvinyorwa zve nickel (CakMg) zvakafanana nekuparadzirwa kwepakati kwepakati yeBoo 5 prographs inoratidzwa. yeNickel concentrations muivhu remudhorobha nepakati pemadhorobha.Mufananidzo wechinomwe unoratidza nzira ye contour mu k-zvinoreva mapoka pamepu, yakakamurwa kuita zvikwata zvitatu zvichienderana nekukosha kwakafanotaurwa mumuenzaniso wega wega.Nzira ye contour inomiririra nhamba yakakwana yemasumbu.Pamasamples 115 akaunganidzwa, chikamu 1 chakawana masamples e2 akawanda, c3luster akagamuchira akawanda evhu.C3luster 3 yakagamuchira sampuli 8. Iyo minomwe-chikamu planar predictor musanganiswa yakareruka kuti ibvumire kududzirwa kwakaringana kweboka.Nekuda kweanthropogenic yakawanda uye yakasikwa maitiro anokanganisa kuumbwa kwevhu, zvakaoma kuve nekusiyanisa kwakaringana mapatani emasumbu mune yakagoverwa SeOM map78.
Chikamu chinobuda nendege neimwe Empirical Bayesian Kriging Support Vector Machine (EBK_SVM_SeOM) inosiyana.[Mamepu eSeOM akagadzirwa pachishandiswa RStudio (version 1.4.1717: https://www.rstudio.com/).]
Zvikamu zvakasiyana-siyana zvemapoka [mepu dzeSeOM dzakagadzirwa pachishandiswa RStudio (vhezheni 1.4.1717: https://www.rstudio.com/).]
Ongororo yemazuva ano inonyatso ratidza maitiro ekuenzanisa nickel muivhu remudhorobha uye kunharaunda yeguta inosimbisa planar spatial kugoverwa kwezvikamu zvakaratidzwa neEBK_SVMR (ona Mufananidzo 5) . Mhedzisiro yacho inoratidza kuti tsigiro vector machine regression model (Ca Mg K-SVMR) inofanotaura kuwanda kweNi muvhu semuenzaniso mumwe chete, asi kuvimbiswa uye kunyatsoongorora maitiro anoratidza kukanganisa kwakakwirira zvikuru maererano neRMSE iyo nzira yekuenzanisa nemOn imwe neruoko rweMAE. EBK_MLR modhi inewo kukanganisa nekuda kweiyo yakaderera kukosha kweiyo coefficient yekutsunga (R2) . Zvibereko zvakanaka zvakawanikwa uchishandisa EBK SVMR uye zvinhu zvakasanganiswa (CaKMg) ine yakaderera RMSE uye MAE zvikanganiso nekururamisa kwe63.7%.Zvinozoitika kuti kubatanidza EBK algorithm ine yakasanganiswa yealgorithms yemuchina wekudzidza algorithm inogona kuburitsa muchina wekudzidza algorithm yeP. kushandisa Ca Mg K sevanofanotaura kufanotaura Nickel munzvimbo yekudzidza inogona kuvandudza kufanotaura kweNi muvhu.Izvi zvinoreva kuti kuenderera mberi kwekushandisa nickel-based fertilizer uye kusvibiswa kwemaindasitiri kwevhu neindasitiri yesimbi kune tsika yekuwedzera nickel muvhu.Chidzidzo ichi chakaratidza kuti EBK modhi inogona kuderedza mwero wekukanganisa uye kuvandudza kurongeka kweivhu remudhorobha. zvakazara, isu tinokurudzira kushandisa iyo EBK-SVMR modhi kuongorora uye kufanotaura PTE muvhu; uyezve, isu tinokurudzira kushandisa EBK kusanganisa neakasiyana muchina kudzidza algorithms.Ni concentrations yakafanotaurwa kushandisa zvinhu se covariates; zvisinei, kushandisa mamwe covariates kwaizovandudza zvikuru kushanda kwemuenzaniso, izvo zvinogona kuonekwa sechinogumira chebasa razvino.Kumwe kuganhurirwa kwechidzidzo ichi ndechokuti nhamba yedatasets ndeye 115. Naizvozvo, kana mamwe mashoko akapiwa, kushanda kweiyo yakarongwa optimized hybridization nzira inogona kuvandudzwa.
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Nguva yekutumira: Jul-22-2022
