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Kusvibiswa kwevhu idambudziko guru rinokonzerwa nemabasa evanhu.The spatial distribution of potential toxic elements (PTEs) inosiyana munzvimbo dzakawanda dzemaguta uye nharaunda.Nekudaro, zvakaoma kufanotaura spatially zviri mukati mePTEs muvhu rakadaro.A total of 115 samples akawanikwa kubva kuFrydek Mistek in the Czech Republic.), magnesiums concentration (Czech), potassium nickM (Calcium) ichishandisa NickKg inductively coupled plasma emission spectrometry.Kupindurwa kwemhinduro ndeyeNi uye vanofanotaura vari Ca, Mg, uye K.Matrix yekubatanidza pakati pemhinduro yakasiyana-siyana uye chirevo chekufananidzira chinoratidza kuwirirana kunogutsa pakati pezvinhu.Zvigumisiro zvekufungidzira zvakaratidza kuti Support Vector Machine Regression (SVMR) yakaita zvakanaka, kunyange zvazvo midzi yaro inofungidzirwa inoreva square error / kg.3mg16 inoreva kukanganisa square (RMSO69) uye 2 (2 mg) 6 (2 mg) inoreva kukanganisa kwesquare (RMSE6) uye 2 (2mg) 6 (2mg / 2016) (2015) (2MR) inoreva kukanganisa kwekona (5kg. .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-Support Vector Machine Regression (EBK/10 kg) ine best model SVM7 (EBK / SVM4) muenzaniso wakanakisisa waiva MA7SERM. (77.368 mg/kg) kukosha uye high coefficient of determination (R2 = 0.637) EBK-SVMR modeling technique output inobuda inoonekwa pachishandiswa mepu inozvironga.Clustered neurons mundege ye hybrid model CakMg-EBK-SVMR component inoratidza akawanda mavara mapatani anofanotaura mhedzisiro yemudhorobha uye SVMR mudhorobha uye SVMR. inzira inoshanda yekufembera huwandu hweNi muvhu remudhorobha neparutivi rweguta.
Nickel (Ni) inonzi micronutrient yezvirimwa nokuti inobatsira mumhepo nitrogen fixation (N) uye urea metabolism, iyo yose inodiwa pakukura kwembeu. Pamusoro pekubatsira kwayo pakumera kwembeu, Ni inogona kuita sefungal uye bhakitiriya inhibitor uye inosimudzira kukura kwezvirimwa. mafetereza anogadzirwa neckel to optimize nitrogen fixation2.Kuramba kuchiiswa nickel-based fertilizer kupfumisa ivhu uye kuwedzera kugona kwenyemba kugadzirisa nitrogen muvhu kunoramba kuchiwedzera nickel concentration muvhu.Kunyange zvazvo nickel iri micronutrient yezvirimwa, kuwandisa kwayo muvhu kunogona kukuvadza zvakanyanya muvhu kupfuura nikel iron uye toxic acid muvhu. trient yekukura kwemiti1. Maererano neLiu3, Ni yakawanikwa iine 17th inokosha inodiwa pakukura kwemiti uye kukura. Pamusoro pekuita nickel mukukura kwemiti uye kukura, vanhu vanoida kune zvakasiyana-siyana zvekushandisa. Electroplating, kugadzirwa kwe nickel-based alloys, uye kugadzirwa kwemagetsi emagetsi uye spark nickel industry yose inoda kushandiswa kwemaindasitiri akasiyana-siyana. -based alloys uye electroplated zvinyorwa zvakashandiswa zvakanyanya mukitchenware, ballroom accessories, chikafu muindasitiri yezvekudya, magetsi, waya uye tambo, jet turbines, kuvhiya implants, machira, uye kuvaka ngarava5. kuputika kwe canic, zvinomera, kutsva kwemasango, uye maitiro e geological;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 yakapoteredza inonyanya kunyungudiswa kwenickel-copper-based smelters nemigodhi.Ivhu repamusoro rakapoteredza Sudbury nickel-copper refinery muCanada raiva nepamusoro-soro yekusvibisa nickel pa 26,000 mg / kg11.Mukupesana, nickel kugadzirwa kweRussia kune nickel yakakwirira mukugadzirwa kwevhu. 11. Maererano neAlms 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 mumwaka wekurima wefosforasi kupinza kana kusvibisa ivhu.Zvinogona kuitika zve nickel muvanhu zvinogona kutungamirira kukenza kuburikidza ne mutagenesis, chromosomal damage, Z-DNA chizvarwa, yakavharidzirwa kugadziriswa kweDNA excision, kana epigenetic process13.Mukuongorora kwemhuka, nickel yakawanikwa iine simba rekuita zvirwere zvakasiyana-siyana, uye carcinogenic nickel complexes inogona kuwedzera tumarara akadaro.
Kuongororwa kwekusvibiswa kwevhu kwakabudirira munguva pfupi yapfuura nekuda kwehutano hwakasiyana-siyana hwehutano hunobva muhukama hwevhu-chirimwa, ivhu uye ivhu rehukama hwehupenyu, kuparara kwezvisikwa, uye kuongororwa kwemamiriro ekunze. ive 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 nekuzadzwa kwe spatial ivhu information systems through the use of in situal and laboratory systems in the spatial and laboratory systems". t 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 kwechiyero chekuyera data yakaparadzaniswa kubva kune iyo inoshandiswa pamigumisiro yenzvimbo ye gepoo , nzira inofungidzirwa inoitwa munharaunda iyi. manhamba, anosanganisira general kriging, co-kriging, common kriging, empirical Bayesian kriging, simple kriging method uye dzimwe nzira dzinozivikanwa dzekududzira mepu kana kufanotaura PTE, maitiro evhu, uye marudzi evhu.
Machine Learning Algorithms (MLA) inyanzvi itsva inoshandisa makirasi akakura asiri-mutsara edata, anofambiswa nealgorithms anonyanya kushandiswa pakuchera data, kuona mapatani mu data, uye anoshandiswa kakawanda kurongedza mune zvesainzi sainzi uye mabasa ekudzoka. 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 systems zvinosanganiswa nekusanzwisisa, zvinoguma zvichiita kuti kuwedzeredza kwemaitiro chaiwo32. Pasinei nemamiriro ezvinhu, kuenzanisa kwemuenzaniso wemuenzaniso, kuenzanisira kwemuenzaniso, kuenzanisira, kuenzanisira, kuenzanisa, kuenzanisira kumuenzaniso, kuenzanisira kwemuenzaniso, kuenzanisira kumuenzaniso wemuenzaniso. kufanotaura, kana kupindira33. Munguva pfupi yapfuura, imwe nzira itsva yeDSM yakabuda iyo inokurudzira kubatanidzwa kwe geostatistics uye MLA mukugadzira mepu nekufungidzira.Masayendisiti akawanda evhu nevanyori, vakadai saSergeev 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.Sevhi yeaya mahystem emhando yeAlgorithm yeAlgorithicial neural network (MLP)
Maererano naSergeev et al., kubatanidza maitiro akasiyana-siyana ekuenzanisira ane mikana yekubvisa kukanganisa uye kuwedzera kushanda kweiyo yakakonzerwa nemhando yakasanganiswa pane kugadzira muenzaniso wayo mumwe chete.Muchirevo chechinyorwa chino, bepa idzva rino rinopokana kuti zvakakosha kushandisa algorithm yakasanganiswa ye geostatistics neMLA kugadzira mhando dzakakwana dzemahybrid kufanotaura Ni hupfumi munzvimbo dzemudhorobha uye peri-urban. modhi uye musanganise neSupport Vector Machine (SVM) uye Multiple Linear Regression (MLR) modhi.Hybridization yeEBK neMLA chero ipi zvayo hazvizivikanwi.Mamodheru akasanganiswa akawanda anoonekwa musanganiswa wezvakajairwa, zvakasara, zvakadzokororwa kriging, uye MLA.EBK igeostatistical interpolation method inoshandisa nzvimbo isina kurongwa parameter pamusoro pemunda, kubvumira kushanduka kwenzvimbo39.EBK yakashandiswa mune zvakasiyana-siyana zvezvidzidzo, kusanganisira kuongorora kugoverwa kwekabhoni kabhoni muvhu repurazi40, kuongorora kusvibiswa kwevhu41 uye kugadzira mepu yevhu42.
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 chinangwa chechinangwa?Uyewo, ndechipi chinhanho chekuongororwa kwehutano hunoenderana nekusimbiswa uye kuongororwa kwechokwadi?Naizvozvo, zvinangwa zvakananga zvechidzidzo ichi chaiva (a) kugadzira musanganiswa wakasanganiswa muenzaniso weSVMR kana MLR uchishandisa EBK (c c kuenzanisa mhedzisiro yemuenzaniso wepro) kuenzanisa mhedzisiro yemuenzaniso we NiDB ivhu remumaguta kana peri-urban , uye (d) mashandisirwo eSeOM kugadzira mepu yakasarudzika yenickel spatial variation.
Chidzidzo ichi chiri kuitwa 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 pe 4 ne 0 ′19 ° ′ 4 ne 0 ′19 ° ′ 4 ′ 1 ′ 49 ° ′. urefu huri pakati pe225 ne327 m;zvisinei, iyo Koppen classification system yemamiriro ekunze enharaunda inonzi Cfb = temperate oceanic climate, Kune mvura yakawanda inonaya kunyange mumwedzi yakaoma.Kupisa kunosiyana zvishoma mugore rose pakati -5 °C ne24 °C, kashoma kudonha pasi -14 °C kana kupfuura pamusoro pe30 °C paavhareji 70 ° C uye 72 C. Nzvimbo yenzvimbo yese inosvika 1,208 square kilometers, ine 39.38% yevhu rakarimwa uye 49.36% yemasango akavharwa. Ukuwo, nzvimbo inoshandiswa muchidzidzo ichi inosvika 889.8 square kilometres. MuOstrava, indasitiri yesimbi nesimbi inoshanda zvakanyanya. sion) uye alloy steels (nickel inowedzera simba re alloy uku ichichengetedza yakanaka ductility uye kuoma kwayo), uye kurima kwakanyanya senge phosphate fetiraiza application uye kugadzirwa kwezvipfuyo tsvakurudzo inogona kuwanika nickel munharaunda (semuenzaniso, kuwedzera nickel kumakwayana kuwedzera kukura kwemakwayana uye mombe shoma shoma) maitiro.Zvinhu zvevhu zviri nyore kusiyaniswa kubva muruvara rwevhu, maumbirwo, uye zviri mukati mecarbonate.Maumbirwo evhu ari pakati nepakati kusvika pakanaka, anobva kuzvinhu zvevabereki.Ane colluvial, alluvial kana aeolian in nature.Dzimwe nzvimbo dzevhu dzinoita sedzakarukana pamusoro uye pasi pevhu, kazhinji dzine kongiri uye bleaching.Zvisinei, cambisols uye stagnosols ndiwo anonyanya kuzivikanwa kubva ku48 regions 4 kubva ku48 regions kubva ku48 yevhu. 5 m, macambisols anotonga muCzech Republic49.
Mepu yenzvimbo yekudzidza [Mepu yenzvimbo yekudzidza yakagadzirwa pachishandiswa ArcGIS Desktop (ESRI, Inc, shanduro 10.7, URL: https://desktop.arcgis.com).]
Zvose zvinokwana 115 zvevhu repamusoro zvakawanikwa kubva kune ivhu remumaguta uye peri-urban mudunhu reFrydek Mistek. Muenzaniso wemuenzaniso wakashandiswa waiva girasi renguva dzose rine sampuli dzevhu dzakaparadzana 2 × 2 km kubva kune imwe nzvimbo, uye ivhu repamusoro rakapimwa pakadzika 0 kusvika 20 cm uchishandisa ruoko rwakabatwa neGPS device (Leica Zeno 5 mabhegi akaiswa mulabhoreti, Ziplobele akaiswa mumabhegi eGPS, Ziplobele akaiswa mumarabhori e5 uye mabhegi akaiswa muSaplobele zvakanaka GPS). .Mienzaniso yacho yakaomeswa nemhepo kuti ibudise sampuli dzakaputika, dzakaputirwa ne mechanical system (Fritsch disc mill), uye sieved (sieve size 2 mm) Isa 1 giramu yevhu rakaomeswa, homogenized uye sieved ivhu mumabhodhoro eteflon akanyorwa zvakajeka. Mumudziyo wega wega weTeflon, shandisa 3% 5 ml yeHCC 37 ml yeHCC uye 3 ml ye 37 ml ye HC imwe yeasidhi imwe neimwe), vhara zvishoma uye bvumira masampuli kuti amire usiku hwose nekuda kwekuita (chirongwa cheaqua regia) .Isa iyo supernatant pane inopisa simbi ndiro (tembiricha: 100 W uye 160 °C) kwemaawa maviri kuti zvive nyore kugayiwa kwemasamples, wozotonhodza.Tumira iyo supernatant kune 50 ml yemvura inoyerera ine 50 mllluteftion yemvura ine 50 mllluteft. supernatant mu 50 ml PVC chubhu nemvura yakasvibiswa.Kuwedzera, 1 ml ye dilution solution yakasvibiswa ne 9 ml yemvura yakasvibiswa uye yakasvibiswa mu 12 ml chubhu yakagadzirirwa PTE pseudo-concentration.The concentrations of PTEs (As, Cd, Cr, Cu, Mn, Ni, Pbled by Mgn, KCP) yakatemwa neMgn, KP, Pb, Cpled, KP ma Optical Emission Spectroscopy) (Thermo Fisher Scientific, USA) maererano nemaitiro akaenzana uye chibvumirano.Iva nechokwadi cheKuvimbiswa Kwehutano uye Kudzora (QA / QC) maitiro (SRM NIST 2711a Montana II Soil) .PTEs nemiganhu yekuona iri pasi pehafu yekuwedzera yakabviswa kubva muchidzidzo ichi.Muganho wekutsvaga wePTE.0u00 yehutano hwekuongorora hutsika hwakanga huri 4 yehutano hwekuongorora. inovimbiswa kuburikidza 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. nzira yekusavimbika uye zvirongwa zvine chekuita nekuronga uku kwesemivariogram iyo inoumba chikamu chakaoma kwazvo chenzira yakakwana yekriging.Iyo nzira yekududzira yeEBK inotevera nzira nhatu dzakataurwa naKrivoruchko50, (a) muenzaniso unofungidzira semivariogram kubva kune yekuisa dataset (b) iyo itsva inofanotaurwa kukosha kwakavakirwa pane imwe neimwe dataset semiuvari modhi inogadzira imwe neimwe dataset semiovari lated dataset.Mutemo weBayesian equation unopiwa seshure
Apo \(Prob\left(A\kurudyi)\) inomiririra yekutanga, \(Prob\left(B\right)\) marginal probability inofuratirwa muzviitiko zvakawanda, \(Prob (B,A)\ ) .Kuverenga kwesemivariogram kunobva pamutemo weBayes, unoratidza kurerekera kwekucherechedzwa kwekutarisa kweiyo datasets iyo inogona kugadzirwa kubva kuBay datasets. iyo inotaura kuti zvingangoita sei kugadzira dataset yekutarisa kubva kune semivariogram.
A support vector machine is algorithm learning algorithm inogadzira yakakwana inopatsanura hyperplane kuti isiyanise zvakafanana asi kwete linearly yakazvimirira makirasi.Vapnik51 yakagadzira intest classification algorithm, asi ichangobva kushandiswa kugadzirisa matambudziko akatarisana neregression.Maererano naLi et al.52, SVM ndeimwe yeakanakisa classifier makirasi maitiro uye akashandiswa akasiyana-siyana eSVMSports emushonga weRegression. ion - SVMR) yakashandiswa mukuongorora uku.Cherkassky naMulier53 vakapayona SVMR se-kernel-based regression, iyo computation yakaitwa uchishandisa mutsara wekugadzirisa maitiro ane nzvimbo dzakawanda dzenyika.55, epsilon (ε) -SVMR inoshandisa dhata yakadzidziswa kuti iwane muenzaniso wekumiririra se epsilon-insensitive function iyo inoshandiswa kumepu 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 rekudzidzisa dhigirii rekugadzirisa subset yemuenzaniso wedhadha inogadzirisawo subset yedhigirii yedheta. s.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 rekuisa, uye \..Ornes anoshandiswa SVG ndiyo kiyi yedata, uye \(yrns\) SV data an radial basis function (RBF) .RBF kernel inoshandiswa kuti ione iyo yakakwana yeSVMR model, iyo inokosha kuti iwane yakanyanya kujeka chirango chekuisa chinhu C uye kernel parameter gamma (γ) yePTE data yekudzidzira.Kutanga, takaongorora chirongwa chekudzidzisa ndokuzoedza kushanda kwemuenzaniso pakugadzirisa.
A multiple linear regression model (MLR) is a regression model inomiririra hukama pakati pemhinduro yakasiyana uye nhamba yepredicor variables nekushandisa linear pooled parameters inoverengwa uchishandisa nzira shoma yemaskweya.MuMLR, imwe nhanho yema squares modhi ibasa rekufungidzira rezvivakwa zvevhu mushure mekusarudzwa kwetsanangudzo dzakasiyana.Zvakakosha kushandisa mhinduro kuti ugadzire mutsara wakashandiswa mutsara wetsananguro mutsara wakasiyana-siyana. tory variables.The MLR equation is
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.
Misanganiswa mhando dzakawanikwa ne sandwiching EBK ine SVMR uye MLR. Izvi zvinoitwa nekutora zvakafanotaurwa hunhu kubva kuEBK kududzira. Izvo zvakafanotaurwa zvakakosha zvakawanikwa kubva kune yakapindirwa Ca, K, uye Mg inowanikwa kuburikidza ne combinatorial maitiro kuti uwane mitsva mitsva, yakadai seCaK, CaMg, uye KMg. misiyano yakawanikwa iCa, K, Mg, CaK, CaMg, KMg uye CaKMg. Izvi zvakasiyana-siyana zvakava fungidziro dzedu, zvichibatsira kufanotaura nickel concentrations mumaguta uye peri-urban ivhu.The SVMR algorithm yakaitwa pane zvakafanotaurwa kuti iwane yakavhenganiswa muenzaniso Empirical Bayesian Kriging-Support Vector Machine (EBK_SVR) inowanawo pipemical algorithms kuwana mutsara weMpiriri weEmpiri muenzaniso weEmpirical model. Bayesian Kriging-Multiple Linear Regression (EBK_MLR).Kazhinji, the variables Ca, K, Mg, CaK, CaMg, KMg, and CaKMg are used as covariates as predictor of Ni content in urban and peri-urban soils.The most acceptable model found (EBK_SVM or EBK_SVM or EBK_SVM or EBKM or EBK_R). mhodzi 2.
Kushandisa SeOM kwave chishandiso chakakurumbira chekuronga, kuongorora, uye kufanotaura data mubazi rezvemari, hutano, indasitiri, nhamba, sainzi yevhu, uye nezvimwe.SeOM inogadzirwa uchishandisa artificial neural network uye nzira dzekudzidza dzisina kutariswa dzesangano, kuongorora, uye kufanotaura.Muchidzidzo ichi, SeOM yakashandiswa kufungidzira Ni concentrations in the best peri-urban modeling ivhu. valuation inoshandiswa se n input-dimensional vector variables43,56.Melssen et al.57 inotsanangura kubatanidzwa kwevector yekupinda mukati meneural network kuburikidza nechikamu chimwe chete chekuisa kune yakabuda vector ine imwechete uremu vector.Kubuda kunogadzirwa neSeOM mepu miviri-dimensional inosanganisira neuroni dzakasiyana kana node dzakarukwa mumapepu ane hexagonal, circular, kana square topological mepu maererano nekuswedera kwavo.Kuenzanisa mapikoni emapupu anoenderana nemepotric, OME modhi uye kukanganisa . 86 uye 0.904, maererano, inosarudzwa, iyo iine 55-map unit (5 × 11) .Nuron structure inotarirwa maererano nenhamba yemanodhi 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).Dataseti yekudzidziswa inoshandiswa kugadzira regression modhi (calibration), uye dhatabheti rebvunzo rinoshandiswa kuratidza kugona kwegeneralization58.Izvi zvakaitwa kuti zviongorore zvese zvakashandiswa mumamodeli evhu rakasiyana-siyana. -validation process, inodzokororwa kashanu.Zvikamu zvinogadzirwa neEBK interpolation zvinoshandiswa sekufungidzira kana zvinotsanangurwa zvinofananidzira kufanotaura chinangwa chakasiyana (PTE) .Modeling inoitwa muRStudio uchishandisa mapeji library(Kohonen), library(caret), library(modelr), library("e1071″), library("plyr"), library" library" ("prospect library") library ("prospect library") "prospect library" "Metric" library ("Metatric library)" ("Catary Metric").
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) . ance magnitude muzviyero zvakazvimiririra zvinotsanangura simba rekufungidzira remuenzaniso, nepo MAE inotarisa kukosha kwehuwandu hwehuwandu.R2 inokosha inofanira kuva yakakwirira kuti iongorore zvakanakisisa musanganiswa wemuenzaniso uchishandisa zvigadziro zvekusimbisa, iyo inoswedera pedyo kukosha 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), shoma, maximum, kurtosis, uye skewness.Zvishoma uye zvakanyanya kukosha zvezvinhu zviri mukuderera kweMg Nekuda kwekuyerwa kwakasiyana kwakayerwa kwezvikamu zvesampled, iyo data set kugoverwa kwezvinhu zvinoratidzira zvakasiyana skewness.The skewness uye kurtosis yezvinhu zvakabva pa1.53 kusvika 7.24 uye 2.49 kusvika 54.16, zvichiteerana.Zvinhu zvese zvakaverengerwa zvine skewness uye kurtosis mazinga ari pamusoro +1, nokudaro kugovera kwakarurama kunoratidza kuti dhairekitori yakarongeka.CV zvezvinhu zvinoratidzawo kuti K, Mg, uye Ni vanoratidza kusiyana kuri pakati nepakati, nepo Ca ine musiyano wakanyanya kukwira. MaCV eK, Ni naMg anotsanangura kugoverwa kwavo kweyunifomu. Uyezve, kugoverwa kweCa hakuna kufanana uye zvinyorwa zvekunze zvinogona kukanganisa nhanho yekupfumisa.
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 56% yakakwirira mu potassium.Potassium yakanga yakabatana zvine mwero ne magnesium (KM 6 potassium pedyo, magnesium sulfuri inobatanidza zvinhu zviviri izvi = Infa sulfate = 0). te, potassium magnesium nitrate, uye potashi zvinoiswa kune ivhu kuwedzera kushomeka kwadzo.Nickel inoenderana zvine mwero neCa, K neMg ine r value = 0.52, 0.63 uye 0.55, zvichiteerana. Hukama hunosanganisira calcium, magnesium, uye PTEs senge nickel yakaoma, magnesium inoderedza kuwandisa kwecalcium, asi inoderedza mhedzisiro yecalcium. zvese magnesium necalcium zvinoderedza chepfu 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 kune huwandu hwehuwandu hwehuwandu hwehuwandu hwehuwandu hwehuwandu hweCasium. kushandiswa kwequicklime (calcium oxide) kuderedza acidity yevhu uye kushandiswa kwayo muzvigayo zvesimbi sealkaline okisijeni mukugadzirwa kwesimbi.Kune rumwe rutivi, vamwe varimi vanosarudza kushandisa calcium hydroxide muvhu rine acidic kuti neutralize pH, iyo inowedzerawo calcium yevhu71.Potassium inoratidzawo nzvimbo dzinopisa kuchamhembe kwakadziva kumadokero uye kumabvazuva kwemepu.The Northto-high uye potasium pateni inogona kunge iri moderate huru yePotassium uye Potassium yePotassium. s.Izvi zvinopindirana nezvimwe zvidzidzo, zvakadai seMadaras uye Lipavský72, Madaras et al.73, Pulkrabová et al.74, Asare et al.75, uyo akacherechedza 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 kunowedzera K kugutsikana muvhu uye kwaizowedzera zvakanyanya ivhu rekudya zvinovaka muviri munguva refu, kunyanya K uye Mg kuratidza nzvimbo inopisa muvhu. Zvine mwero hotspots kuchamhembe kwakadziva kumadokero kwemepu uye kumaodzanyemba kwakadziva kumabvazuva kwemepu. ers, senge potassium magnesium sulfate, magnesium sulfate, uye Kieserite, inorapa kuperevedza (miti inoita yepepuru, tsvuku, kana shava, zvichiratidza kushomeka kwemagnesium) muvhu rine pH range6. Kuunganidzwa kwenickel paivhu remumadhorobha nekumaruwa kungakonzerwa nekukosha kweanthropogenic yekugadzira nickel simbi.
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 maitiro ​​eK anogamuchirwa.RMSE uye MAE migumisiro yakanga yakakura kune calcium uye magnesium yakakura uye migumisiro yeSERM yakakura nekuda kweSERM data uye KERM data. chidzidzo ichi uchishandisa EBK kufanotaura Ni zvakawanikwa zviri nani pane mhedzisiro yaJohn et al.54 tichishandisa synergistic kriging kufanotaura maS concentrations muvhu tichishandisa data rakaunganidzwa rakafanana.Zvakabuda zveEBK zvatakadzidza zvinoenderana neaFabijaczyk 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 kunyatsoongorora kwakasimbisa kuti Ca_Mg_K predictor yakasanganiswa neEBK SVMR model yakaburitsa the best performance.Calibration model a error a error a Ca_Mg_K-EBKRSE model Ca_Mg_K-EBKR (SVMR). MAE) yaiva 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 166.946 mg/kg (isina kukosha) yakawana Ca. 63 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) .Kuwedzera, iyo RMSE uye MAE yeCa_Mg-EBK_SVMR (RMSE = 1664.64 uye MAE = 1031.49) muenzaniso uye inoremekedza 13g_4 iyo 17. 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 RMSE neMAE anoratidza kuti dhata rakakurisa zvakadii neRMSE neMAE. RSME uye MAE zvakaonekwa.Maererano naKebonye et al.46 naJohn nevamwe.54, iyo RMSE neMAE iri pedyo kusvika zero, zviri nani zvabuda.SVMR neEBK_SVMR dzine uwandu hwepamusoro hweRSME neMAE hunokosha.Zvakaonekwa kuti fungidziro dzeRSME dzaive dzakakwirira nguva dzose pane MAE values, zvichiratidza kuvapo kwevanobuda kunze.Maererano neLegates, theMcCabMA inoreva kutadza3 ku8 inokurudzirwa sechiratidzo chekuvapo kwevanobuda kunze.Izvi zvinoreva kuti iyo dataset yakawanda, iyo yakakwirira yeMAE neRMSE.Kururamisa kwekuongorora kwe-cross-validation yeCa_Mg_K-EBK_SVMR yakasanganiswa modhi yekufembera Ni content muivhu remudhorobha nedhorobha raive 63.70%.59, iyi nhanho yekururama ndiyo inogamuchirwa yemuenzaniso wekuita chiyero.Zvigumisiro zviripo zvinofananidzwa nekudzidza kwekare naTarasov et al.36 ane hybrid model yakagadzira MLPRK (Multilayer Perceptron Residual Kriging), ine chekuita neEBK_SVMR accuracy evaluation index yakashumwa muchidzidzo chazvino, RMSE (210) uye The MAE (167.5) yaive yakakwira kupfuura zvatakawana muchidzidzo chazvino (RMSE 95.479, MAE6 ye60 ye70) ye2. iyo 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 akanyora 0.28 (R2) nokuda kweiyo yakagadziriswa hybrid model in the current model (Multila7 rekodha rekodha) 3 rekodha rekodha rekoresheni yemazuva ano (Multila) (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 kunyanya kufanana kwemavara emhando, iyo inonyanya kuenzaniswa nemhando dzemasamples. Neuroni dzakakwirira uye dzakawanda dzakaderera neuroni.Saka, CaK neCaMg vanogovana zvimwe zvakafanana ne-high-order neurons uye pasi-to-moderate color mapatani.Imienzaniso miviri inofanotaura kuiswa kweNi muvhu nekuratidzira pakati kusvika kune yakakwira mavara emavara akadai seakatsvuka, orenji uye yero.KMg modhi inoratidza akawanda emhando yepamusoro maitiro akavakirwa pachiyero chakanyatsoenderana uye yakaderera kusvika pakati-yepakati pemaitiro emavara emhando yemhando yemhando yepamusoro yemhando yemhando yemhando yekugovera, O, muenzaniso wemuvara wakajeka wepateni yemhando yepamusoro, iyo yakaratidza chiyero chemuvara wepateni. mufananidzo wepamusoro unoratidza huwandu hunogona kuita nickel muvhu (ona Mufananidzo 4) .Ndege yechikamu cheCakMg inoratidza mararamiro akasiyana-siyana kubva pasi kusvika kumusoro zvichienderana nechiyero chemavara chakarurama. Uyezve, kufanotaura kwemuenzaniso we nickel content (CakMg) yakafanana nekuparadzirwa kwepasi kwe nickel inoratidzwa muMufananidzo 5. ivhu.Mufananidzo wechinomwe unoratidza nzira ye contour mu k-zvinoreva mapoka pamepu, akakamurwa kuva mapoka matatu zvichienderana nekufanotaura kukosha mumuenzaniso wega wega.Nzira ye contour inomiririra nhamba yakakwana yemasumbu.Pazvikamu zvevhu zana negumi neshanu zvakaunganidzwa, chikamu chekutanga chakawana masamples evhu akawanda, 74.Cluster 2 yakagamuchira 38 sampuli musanganiswa we simponent 33 sampuli 33. Nekuda kwekuwanda kweanthropogenic uye maitiro echisikigo anokanganisa maumbirwo evhu, zvakaoma kuve nemapatani emasumbu akapatsanurwa muSeOM mepu78.
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 yedhorobha.Chidzidzo chakaedza nzira dzakasiyana dzekuenzanisira, kubatanidza zvinhu nemaitiro ekuenzanisa, kuwana nzira yakanakisa yekufanotaura nickel concentrations muvhu.Iyo SeOM compositional planar spatial features of the modelling technique yakaratidza chimiro chepamusoro cheruvara kubva pasi kusvika kumusoro pane spatial color distribution, inosimbisa iyo spatial color plate, inosimbisa mepu yevhu. tial kugoverwa kwezvikamu zvinoratidzwa 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 kwakanyanya maererano neRMSE uye MAE.Kune rumwe rutivi, iyo inobvumira employe ye EBR kune imwe nzira ye EBR, kune rumwe rutivi runokosha kune EBKR. of the coefficient of determination (R2) . Zvibereko zvakanaka zvakawanikwa uchishandisa EBK SVMR uye zvinhu zvakasanganiswa (CaKMg) ine RMSE yakaderera uye MAE zvikanganiso nekururama kwe 63.7% .Zvinosvika kuti kubatanidza EBK algorithm nemichina yekudzidza algorithm inogona kubudisa hybrid algorithm iyo inogona kufanotaura kukanganiswa kwePTEs yekuongorora kweMg iyo inofanotaura ivhu rekuongorora migumisiro yeMg iyo inofanotaura muvhu rekuongorora kweMTEs iyo inofanotaura kufungidzira kwehuwandu hweMg . ion yeNi muvhu.Izvi zvinoreva kuti kuenderera mberi kwekushandiswa kwefotereza ine nickel-based uye kusvibiswa kwemaindasitiri kwevhu neindasitiri yesimbi kune katsika kekuwedzera kuwanda kwenickel muvhu.Ongororo iyi yakaratidza kuti EBK modhi inogona kuderedza nhanho yekukanganisa uye kuvandudza kurongeka kweiyo modhi yekugovera nzvimbo yevhu mudhorobha kana peri-urban ivhu reSBRs kufanotaura ivhu reSVR muvhure-mune-urban propose PSVR modhi yePKM. ;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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