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Gurɓatar ƙasa babbar matsala ce da ayyukan ɗan adam ke haifarwa.Rarraba sararin samaniya na abubuwa masu haɗari masu haɗari (PTEs) sun bambanta a yawancin birane da biranen birni.Saboda haka, yana da wuya a iya hango hasashen abubuwan da ke cikin PTEs a cikin irin wannan ƙasa. An samu samfuran 115 daga Frydek Mistek a Jamhuriyar Czech. Calcium (Mg) da potassium (Calcium) (Calcium) (Calcium) da potassium (Calcium) da magnesium (Calcium) da magnesium (Calcium) da potassium (Calcium) da magnesium (Calcium) da potassium (Calcium) da ma'aurata suka yanke a cikin Jamhuriyar Czech. d plasma emission spectrometry.Madaidaicin amsawa shine Ni kuma masu tsinkaya sune Ca, Mg, da K. Matsakaicin daidaitawa tsakanin madaidaicin amsawa da madaidaicin tsinkaya yana nuna daidaituwa mai gamsarwa tsakanin abubuwan. sun kasance mafi girma fiye da sauran hanyoyin da ake amfani da su.Mixed model for Empirical Bayesian Kriging-Multiple Linear Regression (EBK-MLR) yayi aiki mara kyau, kamar yadda aka nuna ta hanyar ƙididdiga na ƙayyadaddun ƙayyadaddun kasa da 0.1. The Empirical Bayesian Kriging-Support Vector Machine Regression (EBK-SVMR) samfurin (EBK-SVMR) shine mafi kyawun samfurin (EBK-SVMR). 8 mg/kg) dabi'u da babban ƙididdiga na ƙaddara (R2 = 0.637) .A EBK-SVMR ƙirar ƙirar ƙirar ƙirar ƙira an hango ta ta amfani da taswirar tsarin kai.Clustered neurons a cikin jirgin na samfurin matasan CakMg-EBK-SVMR bangaren yana nuna ƙirar launi da yawa waɗanda ke annabta ƙima da ƙima a cikin ƙasa da sakamakon binciken SVK. s a cikin birane da ƙasa na birni.
Nickel (Ni) ana daukarsa a matsayin micronutrients don tsire-tsire saboda yana ba da gudummawar haɓakar nitrogen na yanayi (N) da urea metabolism, duka biyun ana buƙata don haɓakar iri. Ban da gudummawar da yake bayarwa ga ci gaban iri, Ni na iya aiki azaman mai hana fungal da ƙwayoyin cuta da haɓaka ci gaban shuka. Rashin nickel a cikin ƙasa yana ba da damar shuka don shanye shi, alal misali na cokali na chlorosis. tushen taki don inganta nitrogen fixation2.Ci gaba da aikace-aikace na nickel tushen takin mai magani don wadatar da ƙasa da kuma ƙara da ikon da legumes gyara nitrogen a cikin ƙasa ci gaba da ƙara da nickel maida hankali a cikin ƙasa.Ko da yake nickel ne a micronutrient ga shuke-shuke, da wuce kima ci a cikin ƙasa zai iya yin fiye da cutarwa fiye da good.The toxicity na nickel-tushen takin mai magani a matsayin ƙasa mai muhimmanci girma girma da plc girma. A cewar Liu3, an gano Ni a matsayin muhimmin kashi na 17 da ake bukata don ci gaban shuka da kuma girma. Baya ga rawar nickel a cikin ci gaban shuka da girma, mutane suna buƙatar shi don aikace-aikace iri-iri.Electroplating, samar da gami na tushen nickel, da kera na'urori masu kunna wuta da walƙiya a cikin masana'antar kera, duk abubuwan da ake amfani da su a cikin masana'antu na nickel sun haɗa da abubuwan da ke tattare da nickel daban-daban. Yadu amfani a kitchenware, ballroom na'urorin haɗi, abinci masana'antu kayayyaki, lantarki, waya da na USB, jet turbines, tiyata implants, Textiles, da shipbuilding5.Ni-arziki matakan a cikin kasa (watau surface kasa) an dangana ga duka anthropogenic da na halitta kafofin, amma da farko, Ni ne na halitta tushen maimakon anthropogenic4,6.Natural kafofin na anthropogenic4,6.Natural kafofin na nickelation, wuta gasa da wuta, sun hada da wutar lantarki.duk da haka, tushen anthropogenic sun hada da batir nickel / cadmium a cikin masana'antar karfe, electroplating, arc waldi, dizal da man fetur, da kuma fitar da yanayi daga konewar kwal da sharar gida da sludge incineration Nickel accumulation7,8.A cewar Freedman da Hutchinson9 da Manyiwa et al.10, manyan hanyoyin gurbatar ƙasa a cikin kusa da muhallin da ke kusa da su sune masu samar da nickel-Copper-tushen smelters da ma'adinai.A saman ƙasa a kusa da Sudbury nickel-Copper matatar a Kanada yana da mafi girman matakan nickel gurbatawa a 26,000 mg/kg11.A bambanci, gurbatawa daga kasar Norway ya haifar da mafi girma nickel samar a Norway. da al.12, adadin HNO3-extractable nickel a cikin yankin na saman noma ƙasar (nickel samar a Rasha) jeri daga 6.25 zuwa 136.88 mg / kg, daidai da ma'anar 30.43 mg / kg da tushe taro na 25 mg/kg. Bisa ga kabata 11 na ƙasa taki a cikin al'adun gargajiya da aikace-aikace a lokacin kabata 11 taki a cikin birane. s na iya sanyawa ko gurɓata ƙasa.Irin tasirin nickel a cikin ɗan adam zai iya haifar da ciwon daji ta hanyar mutagenesis, lalacewar chromosomal, tsararrun Z-DNA, katange DNA gyare-gyare, ko tsarin epigenetic.
Ƙididdigar gurɓataccen ƙasa ya bunƙasa a cikin 'yan lokutan nan saboda yawancin batutuwan da suka shafi kiwon lafiya da suka taso daga alakar shuka-shuke-shuke, ƙasa da alakar nazarin halittu, lalacewar muhalli, da kuma tasirin muhalli. Har zuwa yau, hasashen sararin samaniya na abubuwa masu guba (PTEs) irin su Ni a cikin ƙasa ya kasance mai wahala da cin lokaci ta hanyar amfani da hanyoyin ƙasa na zamani. PSM) .A cewar Minasny da McBratney16, taswirar ƙasa mai tsinkaya (DSM) ya tabbatar da zama babban yanki na ilimin kimiyyar ƙasa.17 ya bayyana cewa DSM na zamani ko PSM shine mafi inganci dabara don tsinkaya ko tsara taswirar rarraba sararin samaniya na PTEs, nau'ikan ƙasa da kaddarorin ƙasa.Geostatistics da Machine Learning Algorithms (MLA) sune dabarun ƙirar ƙirar DSM waɗanda ke ƙirƙirar taswira na dijital tare da taimakon kwamfutoci ta amfani da mahimman bayanai da ƙarancin bayanai.
Deutsch18 da Olea19 sun ayyana lissafin geostatistics a matsayin "tarin dabarun lambobi waɗanda ke hulɗa da wakilcin halayen sararin samaniya, galibi suna amfani da samfuran stochastic, kamar yadda binciken jerin lokaci ke siffanta bayanan ɗan lokaci."Da farko, geostatistics ya ƙunshi kimantawa na variograms, wanda ke ba da izinin ƙididdigewa da ayyana dogaro da ƙimar sararin samaniya daga kowane dataset20.Gumiaux et al.20 ya kara nuna cewa kimantawa na variograms a cikin geostatistics ya dogara ne akan ka'idoji guda uku, ciki har da (a) ƙididdige ma'auni na daidaitawar bayanai, (b) ganowa da ƙididdige anisotropy a cikin rashin daidaituwa na bayanai da (c) ban da la'akari da kuskuren kuskure na bayanan ma'auni da aka rabu da tasirin gida, ana amfani da fasaha da yawa a cikin abubuwan da aka yi amfani da su a kan tasirin gida. general kriging, co-kriging, talakawa kriging, empirical Bayesian kriging, sauki kriging hanya da sauran sanannun interpolation dabaru don taswira ko hango ko hasashen PTE, ƙasa halaye, da ƙasa iri.
Na'ura Learning Algorithms (MLA) ne in mun gwada da sabon dabara cewa ma'aikata mafi girma data ba mikakke classed, fueled da algorithms da farko amfani da bayanai ma'adinai, gano alamu a cikin bayanai, da kuma akai-akai amfani da rarrabuwa a kimiyya filayen kamar ƙasa kimiyyar da kuma mayar da ayyuka.Yawan bincike takardun dogara a kan MLA model don hango ko hasashen PTE a kasa, kamar Tan et al.22 (dazuzzukan bazuwar don ƙimar ƙarfe mai nauyi a cikin ƙasan gona), Sakizadeh et al.23.24 (CART don yin ƙirar ƙarfe mai nauyi da haɓakawa a cikin ƙasa) Sun et al.25.
Yin amfani da algorithms na DSM a cikin tsinkaya ko taswira yana fuskantar kalubale da yawa. Yawancin marubuta sun yi imanin cewa MLA ya fi girma fiye da geostatistics da kuma akasin haka.Ko da yake daya ya fi kyau fiye da ɗayan, haɗin haɗin biyu yana inganta matakin daidaito na taswira ko tsinkaya a cikin DSM15.Woodcock da Gopal26 Finke27;Pontius da Cheuk28 da Grunwald29 sun yi sharhi game da kasawa da wasu kurakurai a cikin taswirar ƙasa da aka annabta.Masana kimiyyar ƙasa sun gwada dabaru iri-iri don inganta inganci, daidaito, da tsinkaya na taswirar DSM da tsinkaya.Haɗin rashin tabbas da tabbatarwa yana ɗaya daga cikin fannoni daban-daban da aka haɗa cikin DSM don haɓaka inganci da ƙari.15 ya zayyana cewa halayen tabbatarwa da rashin tabbas da ƙirƙirar taswira da hasashe ya gabatar ya kamata a tabbatar da kansu don inganta ingancin taswira. Iyakokin DSM sun kasance saboda yanayin ƙasa mai tarwatsawa, wanda ya ƙunshi ɓangaren rashin tabbas;duk da haka, rashin tabbas a cikin DSM na iya tasowa daga maɓuɓɓuka masu yawa na kuskure, watau kuskuren haɗin kai, kuskuren samfurin, kuskuren wuri, da kuskuren nazari 31. Ƙirar ƙirar ƙira da aka haifar a cikin MLA da tsarin geostatistical suna da alaƙa da rashin fahimta, a ƙarshe yana haifar da oversimplification na ainihin tsari32. Ko da kuwa yanayin yanayin ƙirar ƙira, ƙila za a iya yin la'akari da sigogi na ƙirar ƙira, ƙila za a iya yin la'akari da ma'auni na ƙirar ƙira. 33. Kwanan nan, wani sabon yanayin DSM ya fito wanda ke inganta haɗin gwiwar geostatistics da MLA a cikin taswira da tsinkaya.Da yawa masana kimiyyar ƙasa da marubuta, irin su Sergeev et al.34;Subbotina et al.35;Tarasov et al.36 da Tarasov et al.37 sun yi amfani da ingantacciyar ingantacciyar ƙididdiga ta geostatistics da koyan injina don samar da samfuran gaurayawan waɗanda ke haɓaka haɓakar tsinkaya da taswira.quality.Wasu daga cikin wadannan matasan ko hade algorithm model ne 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-7 da kuma Cogress-MLP) 36.
A cewar Sergeev et al., hadawa daban-daban tallan kayan kawa dabaru yana da m don kawar da lahani da kuma ƙara yadda ya dace da sakamakon matasan model maimakon bunkasa ta guda model.A cikin wannan mahallin, wannan sabon takarda jayayya da cewa shi wajibi ne don amfani da wani hadadden algorithm na geostatistics da MLA don ƙirƙirar mafi kyau duka matasan model don hango ko hasashen Ni enrichment a birane da kuma peri-birane zai reban da model na EBY a cikin birane da kuma yankunan karkara na Bayesi. Mix shi tare da Support Vector Machine (SVM) da Multiple Linear Regression (MLR) model.Hybridization na EBK tare da kowane MLA ba a sani ba.The mahara gauraye model gani ne haduwa na talakawa, saura, regression kriging, da kuma MLA.EBK ne geostatistical interpolation Hanyar cewa utilizes a spatially stochastic tsari da filin bazuwar a matsayin filin da bazuwar tsari da ba a cikin gida. ing for spatial variation39.EBK an yi amfani da a iri-iri na karatu, ciki har da nazarin rarraba Organic carbon a cikin gonaki40, tantance kasa gurbatawa41 da kuma taswirar ƙasa Properties42.
A gefe guda, Graph-Organizing Self (SeOM) algorithm ne na koyo wanda aka yi amfani da shi a cikin labarai daban-daban kamar Li et al.43, Wang et al.44, Hossain Bhuiyan et al.45 da Kebonye et al.46 Ƙayyade halayen sararin samaniya da haɗakar abubuwa.Wang et al.44 ya bayyana cewa SeOM wata fasaha ce ta ilmantarwa mai karfi da aka sani don ikonta na rukuni da kuma tunanin matsalolin da ba na layi ba.Ba kamar sauran fasahohin ƙididdiga na ƙididdiga irin su ƙididdigar manyan sassa, gungumen azaba, gungu na matsayi, da yanke shawara mai yawa, SeOM ya fi kyau wajen tsarawa da gano tsarin PTE. Bisa ga Wang et al.44, SeOM na iya haɗawa da rarraba nau'ikan ƙwayoyin cuta masu alaƙa da kuma samar da hangen nesa mai zurfi na bayanai.SeOM za ta hango bayanan hasashen Ni don samun mafi kyawun samfurin don kwatanta sakamakon don fassarar kai tsaye.
Wannan takarda yana da nufin samar da samfurin taswirar taswira mai ƙarfi tare da daidaito mafi kyau don tsinkayar abubuwan nickel a cikin birane da yankunan birni.Muna tunanin cewa amincin samfurin gauraye ya dogara ne akan tasirin wasu samfurori da aka haɗe zuwa tsarin tushe. Mun yarda da kalubalen da ke fuskantar DSM, kuma yayin da waɗannan kalubalen ke fuskantar a bangarori da yawa, haɗuwa da ci gaba a cikin tsarin geostatistics da MLA.Saboda haka, za mu yi ƙoƙari mu amsa tambayoyin bincike waɗanda za su iya haifar da samfurori masu gauraye. Duk da haka, yadda daidai yake da samfurin a tsinkayar abubuwan da aka yi niyya? Har ila yau, menene matakin ƙimar ƙimar inganci dangane da inganci da kimantawa daidai? Saboda haka, ƙayyadaddun manufofin wannan binciken sun kasance (a) ƙirƙirar samfurin cakuda ga SVMR ko MLR ta amfani da EBK a matsayin ƙirar ƙira, (b) kwatanta mafi kyawun ƙirar ƙima a cikin ƙirar ƙima (bc) ƙirar ƙirar ƙira a cikin ƙirar ƙima. Ƙasar birni, da (d) aikace-aikacen SeOM don ƙirƙirar taswirar babban ƙuduri na bambancin sararin samaniya.
Ana gudanar da binciken a cikin Jamhuriyar Czech, musamman a cikin gundumar Frydek Mistek a cikin yankin Moravia-Silesian (duba Hoto 1) .Geography na yankin binciken yana da matukar damuwa kuma yawanci yana cikin yankin Moravia-Silesian Beskidy, wanda shine ɓangare na gefen waje na tsaunin Carpathian. Yankin binciken yana tsakanin 49 ° ′ 0 ′ 0 ′ 2 ′ 2 ′ 2 da 49 ° 2 ′ 2 ′ 2 ′ 2 da 49 ° 2 ′ 2 ′ 2 ′ 2 da 49 ° 2 ′ 2 ′ 2. tsakanin 225 da 327 m;Duk da haka, tsarin rarraba Koppen don yanayin yanayin yankin an ƙididdige shi azaman Cfb = yanayin yanayin teku, Akwai ruwan sama mai yawa har ma a cikin watanni bushe. Yanayin zafi ya bambanta dan kadan a cikin shekara tsakanin -5 ° C da 24 ° C, da wuya fadowa ƙasa -14 °C ko sama da 30 °C, yayin da matsakaicin yanki na 7 na preci 6.5 da aka kiyasta tsakanin 7 da 5 mm a kowace shekara. yankin yana da murabba'in murabba'in kilomita 1,208, tare da 39.38% na ƙasa da aka noma da 49.36% na ɗaukar gandun daji. Yana ƙara ƙarfin gami yayin da yake riƙe da kyakkyawan ductility da taurinsa), da kuma aikin noma mai ƙarfi kamar aikace-aikacen takin phosphate da kuma samar da dabbobi shine bincike mai yuwuwar tushen nickel a cikin yankin (misali, ƙara nickel zuwa raguna don haɓaka ƙimar girma a cikin raguna da ƙarancin abinci). launi, tsari, da abun ciki na carbonate.The ƙasa rubutu ne matsakaici zuwa lafiya, samu daga iyaye material.Su ne colluvial, alluvial ko aeolian a cikin yanayi.Wasu kasar gona yankunan bayyana mottled a cikin surface da subsoil, sau da yawa tare da kankare da bleaching.Duk da haka, cambisols da stagnosols ne mafi na kowa ƙasa iri a cikin region48.5 ranging daga girma m.4. Jamhuriyar Czech49.
Taswirar yanki na nazari [An ƙirƙiri taswirar yankin binciken ta amfani da ArcGIS Desktop (ESRI, Inc, sigar 10.7, URL: https://desktop.arcgis.com).]
An samo jimlar 115 samfurin saman ƙasa daga ƙasan birane da yankunan birni a cikin gundumar Frydek Mistek. Samfurin samfurin da aka yi amfani da shi shine grid na yau da kullum tare da samfurori na ƙasa da aka raba 2 × 2 km baya, kuma an auna saman ƙasa a zurfin 0 zuwa 20 cm ta amfani da na'urar hannu (Leica Zeno 5 na'urar GPS, samfurin da aka yi amfani da shi a cikin kayan aiki na GPS, da samfurin da aka yi amfani da shi a cikin kayan aiki na GPS, da kuma samfurin da aka yi amfani da shi a cikin samfurin GPS). s sun bushe iska don samar da samfurori da aka tarwatsa, tarwatsa ta hanyar tsarin injiniya (Fritsch diski niƙa), da kuma sieved (girman sieve 2 mm) . Place 1 gram na dried, homogenized da sieved ƙasa samfurori a fili mai lakabi teflon. ly da ƙyale samfurori su tsaya na dare don amsawa (aqua regia shirin) . Sanya supernatant a kan farantin karfe mai zafi (zazzabi: 100 W da 160 ° C) don 2 h don sauƙaƙe tsarin narkewar samfuran, sannan a kwantar da hankali. Canja wurin supernatant zuwa 50 ml volumetric flask da diluted da ruwa mai tsafta zuwa 50 ml da ruwa mai tsafta da ruwa 50. Bugu da ƙari, 1 ml na maganin dilution an diluted tare da 9 ml na ruwa mai tsabta kuma an tace shi a cikin bututu na 12 ml da aka shirya don PTE mai kwakwalwa. rmo Fisher Scientific, Amurka) bisa ga daidaitattun hanyoyin da yarjejeniya.Tabbatar da Tabbatar da Tabbatarwa da Gudanarwa (QA / QC) hanyoyin (SRM NIST 2711a Montana II Soil) .PTEs tare da iyakokin ganowa a ƙasa da rabi an cire su daga wannan binciken. Ƙididdigar ganowa na PTE da aka yi amfani da shi a cikin wannan binciken shine 0.0004., don tabbatar da inganci ta hanyar bincike na kowane nau'i. tabbatar da cewa an rage kurakurai, an yi nazari sau biyu.
Empirical Bayesian Kriging (EBK) yana daya daga cikin fasahohin interpolation da yawa na geostatistical da ake amfani da su wajen yin tallan kayan kawa a fannoni daban-daban kamar kimiyyar ƙasa. Tabbatarwa da shirye-shiryen da ke da alaƙa da wannan makirci na semivariogram wanda ya ƙunshi wani ɓangare mai mahimmanci na isassun hanyar kriging.Tsarin interpolation na EBK ya bi ka'idoji uku da Krivoruchko50 ya gabatar, (a) samfurin yana kimanta semivariogram daga bayanan shigarwa (b) sabon ƙimar da aka annabta ga kowane wuri mai shigar da bayanai dangane da wurin da aka samar da bayanan bayanan da aka haɗa da Bay) da aka haɗa ta ƙarshe da simulugram. An ba da tsarin ƙididdiga a matsayin na baya
Inda \ (Prob \ hagu (A \ dama) \) wakiltar gabanin, \ (Prob \ hagu (B \ dama) \) m yiwuwa ne watsi a mafi yawan lokuta, \ (Prob (B, A) \ ) . The semivariogram lissafin dogara ne a kan Bayes 'ka'idar, wanda ya nuna da propensity na lura datasets cewa za a iya halitta daga semivariograms da za a iya ƙirƙira daga semivariograms, sa'an nan kuma Bay'a iya ƙaddara daga semivariograms. ƙirƙira bayanan abubuwan lura daga semivariogram.
Na'ura mai ba da tallafi shine algorithm na koyo na na'ura wanda ke haifar da mafi kyawun raba hyperplane don rarrabe iri ɗaya amma ba azuzuwan masu zaman kansu na layi ba.Vapnik51 ya ƙirƙiri algorithm rarrabuwar niyya, amma kwanan nan an yi amfani da shi don magance matsalolin da ke da alaƙa da koma baya.A cewar Li et al.52, SVM yana ɗaya daga cikin mafi kyawun dabarun ƙira kuma an yi amfani da na'ura na Reportion SVM a fannoni daban-daban. ) an yi amfani da shi a cikin wannan bincike.Cherkassky da Mulier53 sun fara SVMR a matsayin kernel-based regression, lissafin wanda aka yi amfani da shi ta hanyar yin amfani da tsarin layi na layi tare da ayyuka na sararin samaniya na kasashe da yawa.55, epsilon (ε)-SVMR yana amfani da bayanan da aka horar don samun samfurin wakilci a matsayin aikin epsilon-marasa hankali wanda aka yi amfani da shi don taswirar bayanan da kansa tare da mafi kyawun epsilon bias daga horarwa akan bayanan da suka dace. An yi watsi da kuskuren nisa na saiti daga ainihin ƙimar, kuma idan kuskuren ya fi girma fiye da ε (ε), kaddarorin ƙasa suna rama shi. Vapnik51 ya gabatar yana nunawa a ƙasa.
inda b yake wakiltar madaidaicin bakin kofa, \(K \ hagu ({x}_{,}{ x}_{k} \ dama) \) yana wakiltar aikin kernel, \(\alpha \) yana wakiltar Lagrange multiplier, N yana wakiltar bayanan ƙididdiga, \({x}_{k}\) yana wakiltar shigar bayanai, kuma \(y\us) shine maɓalli na S. Aiki na tushen radial (RBF) .An yi amfani da kernel na RBF don ƙayyade mafi kyawun samfurin SVMR, wanda yake da mahimmanci don samun mafi kyawun hukuncin saiti na C da kernel parameter gamma (γ) don bayanan horo na PTE. Na farko, mun kimanta saitin horo sannan kuma gwada aikin samfurin akan saitin tabbatarwa. Hanyar tuƙi kuma hanyar da aka yi amfani da ita ita ce darajar sig.
A mahara linzamin kwamfuta model (MLR) ne mai regression model cewa wakiltar dangantakar da ke tsakanin mayar da martani m da kuma yawan tsinkaya masu canji ta hanyar amfani da linzamin kwamfuta sigogi da aka lasafta ta amfani da mafi ƙanƙanta murabba'i hanya. Matsalolin atory.Ma'aunin MLR shine
inda y shine madaidaicin amsa, \(a \) shine shiga tsakani, n shine adadin masu tsinkaya, \({b}_{1}\) shine juzu'in juzu'i na ma'auni, \({x}_{i}\) yana wakiltar ma'anar tsinkaya ko ma'anar bayyanawa, kuma \ ({\varepsilon }_{i} \) yana wakiltar kuskuren da aka sani a matsayin samfurin, wanda kuma shine ragowar.
An samo samfurori masu gauraya ta hanyar sandwiching EBK tare da SVMR da MLR. Wannan ana yin haka ta hanyar cire dabi'un da aka annabta daga EBK interpolation. Abubuwan da aka annabta da aka samu daga ca, K, da Mg da aka yi amfani da su ana samun su ta hanyar haɗin kai don samun sababbin masu canji, irin su CaK, CaMg, da KMg. Abubuwan da aka samu, CaK, CaMg, da KMg suna samuwa na hudu, CaKM da Mg. Ca, K, Mg, CaK, CaMg, KMg da CaKMg.Waɗannan masu canji sun zama masu tsinkayar mu, suna taimakawa wajen tsinkayar adadin nickel a cikin ƙasa na birane da na birni.An yi amfani da algorithm na SVMR akan masu tsinkaya don samun samfurin gauraye Empirical Bayesian Kriging-Support Vector Machine (EBK_SVM) . -Multiple Linear Regression (EBK_MLR) .Yawanci, masu canji Ca, K, Mg, CaK, CaMg, KMg, da CaKMg ana amfani da su azaman covariates a matsayin tsinkaya na abun ciki na Ni a cikin birane da yankunan birni. Mafi kyawun samfurin da aka samu (EBK_SVM ko EBK_MLR) za a nuna shi a cikin zane-zane na zane-zane ta hanyar amfani da zane-zane.
Yin amfani da SeOM ya zama sanannen kayan aiki don tsarawa, kimantawa, da kuma kisa bayanai a cikin sashin kuɗi, kiwon lafiya, masana'antu, ƙididdiga, kimiyyar ƙasa, da ƙari.An ƙirƙira SeOM ta amfani da hanyoyin sadarwa na wucin gadi da hanyoyin ilmantarwa marasa kulawa don tsari, kimantawa, da tsinkaya. ana amfani da su azaman n shigarwa-girma vector variables43,56.Melssen et al.57 Bayyana haɗin venain shigar da venal a cikin cibiyar sadarwar tazara ta hanyar shigarwar da ke cikin awo tare da madaidaiciyar vixtalic, Madauwari, ko ƙayyadaddiyar ƙa'idodi (QE), samfurin Seom tare da 0.086 da 0.904, bi da bi, wanda shine naúrar taswirar 55-taswira (5 × 11) .The ne ya ƙaddara gwargwadon adadin nodes ɗin
Adadin bayanan da aka yi amfani da shi a cikin wannan binciken shine samfurori na 115. An yi amfani da tsarin bazuwar don raba bayanan a cikin bayanan gwaji (25% don tabbatarwa) da kuma tsarin bayanan horo (75% don daidaitawa) .An yi amfani da bayanan horo don samar da samfurin regression (calibration), kuma ana amfani da bayanan gwajin don tabbatar da iyawar gabaɗaya58. Anyi amfani da wannan don tantance dacewa da samfurin da aka yi amfani da shi a cikin nau'i-nau'i iri-iri na nickll. ation tsari, maimaita sau biyar. Abubuwan da aka samar ta hanyar EBK interpolation ana amfani da su azaman masu tsinkaya ko ma'anar bayani don tsinkayar ma'auni (PTE) . Ana yin amfani da samfurin a cikin RStudio ta amfani da ɗakin karatu na kunshe-kunshe (Kohonen), ɗakin karatu (kulawa), ɗakin karatu (modelr), ɗakin karatu ("e1071"), ɗakin karatu ("plyr")), ɗakin karatu ("kayan aiki" da kayan aiki).
An yi amfani da sigogi daban-daban na tabbatarwa don ƙayyade mafi kyawun samfurin da ya dace da tsinkayar ƙididdiga na nickel a cikin ƙasa kuma don kimanta daidaiton samfurin da ingancinsa.An kimanta samfurori na hybridization ta amfani da kuskuren kuskure (MAE), kuskuren kuskuren kuskure (RMSE), da R-squared ko ƙaddarar ƙididdiga (R2) .RMSE yana bayyana bambancin ma'auni na ma'auni a cikin amsawa da ma'anar ma'auni. ikon samfurin, yayin da MAE ke ƙayyade ainihin ƙimar ƙididdiga. Ƙimar R2 dole ne ya kasance mai girma don kimanta samfurin cakuda mafi kyau ta amfani da ma'auni na tabbatarwa, mafi kusa da darajar shine 1, mafi girma daidai. Bisa ga Li et al.59, ƙimar ma'aunin R2 na 0.75 ko mafi girma ana ɗaukar kyakkyawan tsinkaya;daga 0.5 zuwa 0.75 an yarda da aikin samfurin, kuma a ƙasa 0.5 shine aikin samfurin da ba a yarda da shi ba. Lokacin zabar samfurin ta amfani da hanyoyin tantance ma'auni na RMSE da MAE, ƙananan dabi'un da aka samu sun isa kuma an yi la'akari da mafi kyawun zabi. Ƙididdigar da ke gaba ta kwatanta hanyar tabbatarwa.
inda n yana wakiltar girman ƙimar da aka lura\({Y}_{i}\) yana wakiltar martanin da aka auna, kuma \({\ widehat{Y}}_{i}\) kuma yana wakiltar ƙimar amsawar da aka annabta, don haka, na farkon i.
An gabatar da bayanan ƙididdiga na masu tsinkaya da masu amsawa a cikin Table 1, suna nuna ma'ana, daidaitattun daidaituwa (SD), ƙididdiga na bambancin (CV), m, matsakaicin, kurtosis, da skewness. Ƙididdigar ƙididdiga da ƙididdiga na abubuwa suna cikin raguwar tsari na Mg Saboda ma'auni daban-daban na abubuwan da aka yi amfani da su, abubuwan da aka tsara na bayanai na abubuwan da aka rarraba suna nuna nau'i-nau'i daban-daban. skewness da kurtosis na abubuwan sun kasance daga 1.53 zuwa 7.24 da 2.49 zuwa 54.16. ked.CVs da aka kiyasta na abubuwan kuma sun nuna cewa K, Mg, da Ni suna nuna matsakaicin matsakaici, yayin da Ca yana da matukar girma.
Daidaitawar ma'auni mai mahimmanci tare da abubuwan amsawa sun nuna daidaituwa mai gamsarwa tsakanin abubuwa (duba Hoto 3) Daidaitawa ya nuna cewa CaK ya nuna matsakaicin matsakaici tare da darajar r = 0.53, kamar yadda CaNi.Ko da yake Ca da K suna nuna ƙungiyoyi masu sassaucin ra'ayi tare da juna, masu bincike irin su Kingston et al.68 da Santo69 sun nuna cewa matakansu a cikin ƙasa suna da bambanci sosai. sulle, potassium Magnesium nitrate, da kuma ana amfani da shi ga kasa don cal, k da kuma 0.63 da kuma 0.63 da kuma 0,63 da kuma 0.63 da kuma 0,63 da kuma 0,63 da kuma 0.55, a matsayin Nickley Rabium, Calcium, da kuma 0,83 da kuma 0,63 da kuma 0,63 da 0.63 da 0.63 da kuma 0,63 Kuma dukansu magnesium da alli suna rage tasirin cututtukan ruwa na nickel a cikin ƙasa.
Matrix na daidaitawa don abubuwan da ke nuna alaƙar da ke tsakanin masu tsinkaya da amsawa (Lura: wannan adadi ya haɗa da ƙaddamarwa tsakanin abubuwa, matakan mahimmanci suna dogara ne akan p <0,001).
Hoto na 4 yana kwatanta rarraba sararin samaniya na abubuwa. A cewar Burgos et al70, aikace-aikacen rarraba sararin samaniya wata dabara ce da ake amfani da ita don ƙididdigewa da kuma haskaka wurare masu zafi a cikin wuraren da ba su da kyau. Ana iya ganin matakan wadata na Ca a cikin Fig. 4 a cikin arewa maso yammacin taswirar rarraba sararin samaniya. Adadin ya nuna matsakaici zuwa babban Ca enrichment hotspot na arewa maso yammacin amfani da taswirar alli. cium oxide) don rage ƙasa acidity da kuma amfani da shi a karfe niƙa kamar alkaline oxygen a cikin steelmaking tsari.A daya hannun, sauran manoma fi son yin amfani da calcium hydroxide a acidic kasa don neutralize pH, wanda kuma qara da alli abun ciki na earth71.Potassium kuma ya nuna zafi spots a arewa maso yamma da gabas na map.The Arewa maso yammacin ne a manyan agricultural al'umma da kuma Potassium al'umma. daidai da sauran karatu, irin su Madaras da Lipavský72, Madaras et al.73, Pulkrabova et al.74, Asare et al.75, wanda ya lura cewa tabbatar da ƙasa da kuma jiyya tare da KCl da NPK sun haifar da babban abun ciki na K a cikin ƙasa.Samar da Potassium na sararin samaniya a arewa maso yammacin taswirar rarraba na iya zama saboda amfani da takin mai magani na potassium kamar potassium chloride, potassium sulfate, potassium nitrate, potash, da potash don ƙara yawan potassium na ƙasa mara kyau.Zádorová et al.76 da Tlustoš et al.77 ya kayyade cewa aikace-aikacen takin mai tushe na K yana haɓaka abun ciki na K a cikin ƙasa kuma zai ƙara haɓaka abubuwan gina jiki na ƙasa a cikin dogon lokaci, musamman K da Mg suna nuna wuri mai zafi a cikin ƙasa.Matsakaicin matsakaicin matsakaici a arewa maso yamma na taswira da kudu maso gabashin taswira. Gyaran colloidal a cikin ƙasa yana rage ƙarancin magnesium a cikin ƙasa mai shuke-shuken rawaya. irin su potassium magnesium sulfate, magnesium sulfate, da Kieserite, bi da kasawa (tsiran suna bayyana purple, ja, ko launin ruwan kasa, nuna rashin magnesium) a cikin kasa tare da al'ada pH range6.The tarawar nickel a kan birane da kuma yankunan karkara ƙasa saman na iya zama saboda anthropogenic ayyuka kamar noma da kuma muhimmancin karfe samar78 a bakin karfe.
Rarraba abubuwa na sararin samaniya [an ƙirƙiri taswirar rarraba sararin samaniya ta amfani da ArcGIS Desktop (ESRI, Inc, Shafin 10.7, URL: https://desktop.arcgis.com).]
Sakamakon samfurin aikin samfurin don abubuwan da aka yi amfani da su a cikin wannan binciken an nuna su a cikin Table 2. A gefe guda, RMSE da MAE na Ni duka suna kusa da sifili (0.86 RMSE, -0.08 MAE) . ta yin amfani da EBK don tsinkaya Ni an samo mafi kyau fiye da sakamakon John et al.54 ta yin amfani da kriging synergistic don hango hasashen S a cikin ƙasa ta amfani da bayanan da aka tattara iri ɗaya. Abubuwan EBK da muka yi nazari sun yi daidai da na Fabijaczyk et al.41, Yan et al.79, Beguin et al.80, Adhikary et al.81 da John et al.82, musamman K da Ni.
An kimanta aikin hanyoyin mutum don tsinkayar abun ciki na nickel a cikin birane da biranen birni ta hanyar amfani da aikin ƙirar (Table 3) .Tsarin haɓakawa da ƙima na ƙima ya tabbatar da cewa Ca_Mg_K tsinkaya tare da samfurin EBK SVMR ya ba da mafi kyawun aiki. Calibration model Ca_Mg_K-EBK_SVMR model Abk-EBK_SVMR 7 (kuskuren ma'anar kuskure) da kuskuren kuskure 6 (kuskuren kuskure) 95.479 mg/kg (RMSE) da 77.368 mg/kg (MAE) Ca_Mg_K-SVMR shine 0.663 (R2), 235.974 mg/kg (RMSE) da 166.946 mg/kg (MAE) da Ca_Mg-EBK_SVMR (0.643 = R2);Sakamakon RMSE da MAE ɗin su sun fi na Ca_Mg_K-EBK_SVMR (R2 0.637) (duba Table 3) . Bugu da ƙari, RMSE da MAE na Ca_Mg-EBK_SVMR (RMSE = 1664.64 da MAE = 1031.49) samfurin sune 137.5, da Ca_g. EBK_SVMR.Hakazalika, RMSE da MAE na Ca_Mg-K SVMR (RMSE = 235.974 da MAE = 166.946) samfurin 2.5 da 2.2 sun fi girma fiye da na Ca_Mg_K-EBK_SVMR RMSE da MAE, daidaitattun bayanan da aka ƙididdigewa tare da sakamakon da aka ƙididdigewa. ME da MAE an lura da su. A cewar Kebonye et al.46 da john et al.54, mafi kusancin RMSE da MAE zuwa sifili, mafi kyawun sakamako.SVMR da EBK_SVMR suna da ƙimar RSME da MAE mafi girma. na outliers.Wannan yana nufin cewa mafi iri-iri na dataset, da mafi girma da MAE da RMSE dabi'u. Daidaiton giciye-tabbatar da ca_Mg_K-EBK_SVMR gauraye samfurin domin tsinkaya Ni abun ciki a cikin birane da kewayen ƙasa ya 63.70% bisa ga Li et al.59, wannan matakin daidaito shine ƙimar aikin ƙira mai karɓa. Sakamakon yanzu ana kwatanta shi da wani binciken da Tarasov et al ya yi a baya.36 wanda samfurin matasan ya haifar da MLPRK (Multiyer Perceptron Residual Kriging), dangane da EBK_SVMR daidaiton kimantawa da aka ruwaito a cikin binciken na yanzu, RMSE (210) da kuma MAE (167.5) ya kasance mafi girma fiye da sakamakon mu a cikin binciken na yanzu (RMSE 95.479, MAE 77.368) na yanzu. Tarasov et al.36 (0.544), ya bayyana a fili cewa ƙididdiga na ƙaddara (R2) ya fi girma a cikin wannan samfurin gauraye. Ƙirar kuskure (RMSE da MAE) (EBK SVMR) don samfurin gauraye shine sau biyu ƙananan. Matsayin daidaiton tsinkaya na wannan ƙirar (EBK SVMR) shine 63.7%, yayin da daidaiton tsinkaya da Sergeev et al.34 shine 28%.
Ana wakilta taswirar hasashe na ƙarshe ta amfani da ƙirar matasan EBK_SVMR da amfani da Ca_Mg_K azaman mai hasashen.
An gabatar da shi a cikin Hoto 6 sune PTE maida hankali ne a matsayin jirgin saman abun da ke ciki wanda ya ƙunshi nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan. high neurons da mafi ƙananan ƙananan ƙwayoyin cuta.Ta haka, CaK da CaMg suna raba wasu kamanceceniya tare da nau'o'in nau'i-nau'i masu yawa da ƙananan nau'i-nau'i na launi masu launi.Dukansu samfurori sunyi la'akari da maida hankali na Ni a cikin ƙasa ta hanyar nuna matsakaici zuwa manyan launuka masu launi irin su ja, orange da yellow.The KMg model nuni da yawa high launi alamu dangane da daidai rabbai, matsakaici launi da aka gyara daga matsakaici rabbai da kuma low zuwa matsakaicin sikelin da aka gyara na rabbai. samfurin ya nuna babban launi mai launi wanda ke nuna yiwuwar ƙaddamar da nickel a cikin ƙasa (duba Hoto 4) .Kayan samfurin CakMg samfurin jirgin sama yana nuna nau'in launi daban-daban daga ƙananan zuwa babba bisa ga ma'auni mai launi daidai. Bugu da ƙari kuma, tsinkayen samfurin na abun ciki na nickel (CakMg) yana kama da rarraba sararin samaniya na nickel da aka nuna a cikin Hoto 5., Dukansu jadawalai da ƙananan ƙididdiga na ƙasa mai girma. ure 7 yana nuna hanyar kwane-kwane a cikin k-ma'anar haɗakarwa akan taswira, rarraba zuwa gungu uku dangane da ƙimar da aka annabta a cikin kowane ƙirar.Hanyar kwane-kwane tana wakiltar mafi kyawun adadin gungu. Daga cikin samfuran ƙasa 115 da aka tattara, nau'in 1 ya sami mafi yawan samfuran ƙasa, 74.Cluster 2 ya karɓi samfuran samfuran 33. don ba da izinin fassarar gungu daidai.Saboda yawancin tsarin ɗan adam da na halitta da ke shafar samuwar ƙasa, yana da wahala a sami bambance-bambancen tsarin gungu yadda ya kamata a cikin taswirar SeOM78 da aka rarraba.
Fitowar sashin jirgin sama ta kowane Injin Tallafi na Empirical Bayesian Kriging Vector Machine (EBK_SVM_SeOM) m.
Abubuwan rarrabuwa daban-daban [an ƙirƙira taswirar SeOM ta amfani da RStudio (sigar 1.4.1717: https://www.rstudio.com/)]
Binciken na yanzu yana kwatanta dabarun ƙirar ƙirar nickel a cikin birane da ƙasa na birni.Binciken ya gwada dabarun ƙirar ƙira daban-daban, haɗa abubuwa tare da dabarun ƙirar ƙira, don samun hanya mafi kyau don tsinkayar ƙimar nickel a cikin ƙasa.The SeOM compositional planar sarari fasali na tallan kayan kawa dabara ya nuna babban launi juna daga ƙasa zuwa sama a kan daidaitaccen tsarin rarraba taswira a cikin ma'auni mai launi. tial rarraba abubuwan da aka nuna ta EBK_SVMR (duba Hoto 5) .A sakamakon nuna cewa goyon bayan vector inji regression model (Ca Mg K-SVMR) annabta maida hankali da Ni a cikin ƙasa a matsayin daya model, amma inganci da daidaito kimantawa sigogi nuna sosai high kurakurai cikin sharuddan RMSE da MAE.A daya hannun, da yin gyare-gyaren dabaran da aka yi amfani da low quality model na EBK aiki tare da m darajar aiki tare da EBK. (R2) .An samu sakamako mai kyau ta hanyar amfani da EBK SVMR da abubuwan da aka haɗa (CaKMg) tare da ƙananan RMSE da kurakurai MAE tare da daidaito na 63.7% . Yana nuna cewa hadawa da EBK algorithm tare da na'ura na ilmantarwa algorithm na iya samar da wani hybrid algorithm wanda zai iya tsinkayar ƙaddamarwar PTEs a cikin ƙasa. Sakamakon ya nuna cewa yin amfani da Ca Mg K a matsayin masu tsinkaya a cikin binciken da aka yi a cikin ƙasa na Nick na iya inganta ci gaba da nazarin ƙasa na Nick. - tushen takin mai magani da gurbatar masana'antu na ƙasa ta masana'antar ƙarfe yana da haɓakar haɓakar nickel a cikin ƙasa.Wannan binciken ya nuna cewa samfurin EBK zai iya rage matakin kuskure kuma inganta daidaiton tsarin rarraba sararin samaniya a cikin birane ko yankunan birni. Gaba ɗaya, muna ba da shawarar yin amfani da samfurin EBK-SVMR don tantancewa da tsinkayar PTE a cikin ƙasa;Bugu da kari, muna ba da shawarar yin amfani da EBK don haɓaka tare da algorithms na koyon injin iri daban-daban.An ƙididdige yawan ƙima ta amfani da abubuwa azaman covariates;duk da haka, yin amfani da ƙarin covariates zai inganta aikin samfurin, wanda za'a iya la'akari da iyakancewar aikin yanzu.Wani ƙayyadaddun wannan binciken shine yawan adadin bayanai shine 115. Saboda haka, idan an ba da ƙarin bayanai, za a iya inganta aikin da aka tsara na inganta hanyar hybridization.
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