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Rashin gurɓataccen ƙasa shine babban matsala da ayyukan ɗan adam ke haifarwa.Rarraba sararin samaniya na abubuwa masu haɗari masu haɗari (PTEs) sun bambanta a yawancin birane da yankunan birane.Saboda haka, yana da wuya a yi la'akari da abubuwan da ke cikin PTEs a cikin irin wannan ƙasa. An samo samfurori na 115 daga Frydek Mistek a Jamhuriyar Czech. Calcium (Mg) da potassium (K) an ƙaddara ta amfani da potassium (Calcium da potassium) Inductively coupled plasma emission spectrometry.Maganin amsawar amsa shine Ni kuma masu tsinkaye sune Ca, Mg, da K. Matrix daidaitawa tsakanin madaidaicin amsawa da madaidaicin tsinkaye yana nuna daidaituwa mai gamsarwa tsakanin abubuwan. (166.946 mg / kg) sun kasance mafi girma fiye da sauran hanyoyin da ake amfani da su.Mixed model for Empirical Bayesian Kriging-Multiple Linear Regression (EBK-MLR) aiki mara kyau, kamar yadda shaida ta coefficients na ƙaddara kasa da 0.1. The Empirical Bayesian Kriging-Support Vector Machine-EBSEK model tare da mafi kyau model. (95.479 mg/kg) da MAE (77.368 mg/kg) dabi'u da high coefficient na ƙudiri (R2 = 0.637) EBK-SVMR samfurin kayan kawa fitarwa da aka gani ta amfani da wani kai-tsara taswira.Clustered neurons a cikin jirgin sama na matasan samfurin CakMgMR-EBK da aka kwatanta a cikin manyan biranen launi na CakMg-EBK-S. Sakamakon ya nuna cewa hada EBK da SVMR wata hanya ce mai inganci don tsinkayar adadin Ni a cikin ƙasan birane da 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 haɓaka iri. Baya ga 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. na nickel tushen takin mai magani 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 iya yin fiye da cutarwa fiye da kyau.The toxicity na ƙasa mafi girma da baƙin ƙarfe. A matsayin wani muhimmin sinadari mai gina jiki don ci gaban shuka1.A cewar Liu3, an gano Ni a matsayin muhimmin kashi na 17 da ake buƙata don bunƙasa shuka da bunƙasa. Baya ga rawar nickel wajen bunƙasa shuka da bunƙasa, ɗan adam yana 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'antu daban-daban. nickel-tushen gami da electroplated articles da aka 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 kuma na halitta kafofin, amma da farko, Ni ne na halitta4 tushen maimakon nickel. fashewa, ciyayi, gobarar gandun daji, da tsarin ilimin kasa; 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 gurɓataccen ƙasa a cikin kusa da muhallin da ke kusa da su sune masu samar da nickel-Copper-tushen smelters da ma'adinai.Ƙasa mafi girma a kusa da Sudbury nickel-Copper matatar a Kanada yana da mafi girman matakan nickel a 26,000 mg/kg11. A bambanci, gurbatawa daga Rasha ya haifar da karuwar nickel a cikin Norwegian. kasa11.A cewar Alms et al. 12, adadin HNO3-extractable nickel a cikin babban yankin noma ƙasar noma (nickel samar a Rasha) jeri daga 6.25 zuwa 136.88 mg / kg, daidai da 30.43 mg / kg da wani tushe taro na 25 mg / kg. Bisa ga kabata 11 na kasar gona da takin mai magani na phosphorous a cikin birane da aikace-aikace. a lokacin lokutan amfanin gona iri-iri na iya haifar da ko gurɓata ƙasa. Abubuwan da ke haifar da nickel a cikin mutane na iya haifar da ciwon daji ta hanyar mutagenesis, lalacewar chromosomal, tsararrun Z-DNA, gyare-gyaren gyare-gyare na DNA, 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 halittu, lalata muhalli, da kimanta tasirin muhalli. Har zuwa yau, hasashen sararin samaniya na abubuwa masu guba masu guba (PTEs) irin su Ni a cikin ƙasa ya kasance mai wahala da cin lokaci ta hanyar amfani da hanyoyin ƙasa na zamani. Taswirar ƙasa mai tsinkaya (PSM) .A cewar Minasny da McBratney16, taswirar ƙasa mai tsinkaya (DSM) ya tabbatar da zama sanannen yanki na kimiyyar ƙasa. al. 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 yana 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 hanyoyin da aka yi amfani da su a kan tasirin gida. Geostatistics, gami da kriging na gabaɗaya, haɗin gwiwa, kriging na yau da kullun, empirical Bayesian kriging, hanyar kriging mai sauƙi da sauran sanannun dabarun shiga tsakani don taswira ko hasashen PTE, halayen ƙasa, da nau'ikan ƙasa.
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, wato kuskuren haɗin kai, kuskuren samfurin, kuskuren wuri, da kuma kuskuren nazari 31. Ƙididdigar ƙididdiga 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 ƙirar ƙira, ba tare da la'akari da yanayin ƙirar ƙirar ƙira ba, ba tare da la'akari da yanayin ƙirar ƙira ba. tsinkaya, ko interpolation33. Kwanan nan, 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 Coussi-MLP) 36. Juya baya38.
A cewar Sergeev et al., hada daban-daban tallan kayan kawa dabaru yana da damar kawar da lahani da kuma ƙara da inganci na sakamakon matasan model maimakon bunkasa ta guda model.A cikin wannan mahallin, wannan sabon takarda jayayya cewa shi wajibi ne don amfani da hade algorithm na geostatistics da MLA don ƙirƙirar mafi kyau duka matasan model to hango ko hasashen Ni enrichment a birane da kuma peri-Biri-Biri yankunan za reban EBY binciken a cikin birane da yankunan karkara na Bayesi. a matsayin tushe model da kuma Mix shi da Support Vector Machine (SVM) da Multiple Linear Regression (MLR) model.Hybridization na EBK tare da wani 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 da utilizes a spatially interpolation hanya da cewa utilizes a spatially filin da bazuwar filin da bazuwar a cikin gida. an ayyana ma'auni na yanki a kan filin, ba da izinin bambancin sararin samaniya39. An yi amfani da EBK a cikin bincike daban-daban, ciki har da nazarin rarraba carbon carbon a cikin gonaki40, tantance gurɓataccen ƙasa41 da taswirar ƙasa42.
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. don 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, takamaiman maƙasudin wannan binciken sun kasance (a) ƙirƙirar samfurin haɗuwa don SVMR ko MLR ta yin amfani da EBK a matsayin samfurin tushe, (b) kwatanta mafi kyawun ƙirar ƙima a cikin ƙirar ƙira. Ƙasar birni ko kewayen 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 yana da yawa daga cikin yankin Moravia-Silesian Beskidy, wanda ke cikin ɓangaren waje na tsaunin Carpathian. Yankin binciken yana tsakanin 49 ° ′ 0 ′ 0 ′ 2 ′ 0 ′ 2 ′ 2 ′ 2 ′ 0 ′ 2 ′ 2 ′ 2 ′ 2 ′ 2 ′ 1 ′ 2 ′ 2 ′ 49° da kuma tsayin yana tsakanin 225 da 327 m; duk da haka, tsarin rarraba Koppen don yanayin yanayi na yankin an ƙididdige shi a matsayin Cfb = yanayi mai zafi na teku, Akwai ruwan sama mai yawa ko da a cikin watanni bushe. Yanayin zafi ya bambanta kadan a cikin shekara tsakanin -5 ° C da 24 ° C, da wuya ya fado kasa -14 °C ko sama da 30 °C, yayin da matsakaicin matsakaicin shekara 7.5 da aka kiyasta tsakanin preci 6.5. yankin na dukan yankin ne 1,208 murabba'in kilomita, tare da 39.38% na noma ƙasar da 49.36% na gandun daji cover. A daya hannun, yankin da aka yi amfani da a cikin wannan binciken ne game da 889.8 murabba'in kilomita. A cikin kuma a kusa da Ostrava, da karfe masana'antu da karfe ayyuka ne sosai aiki.Metal masana'antu, da karfe da aka yi amfani da karfe masana'antu a cikin bakin karfe masana'antu, misali, a cikin masana'antu. lalata) da kuma gami karfe (nickel yana kara karfin gami yayin da yake kiyaye kyakykyawan ductility da taurinsa), da kuma aikin noma mai zurfi kamar aikace-aikacen takin phosphate da kuma samar da dabbobi shine bincike mai yuwuwar tushen nickel a yankin (misali, ƙara nickel zuwa raguna don haɓaka ƙimar girma a cikin raguna da ƙarancin ciyar da dabbobi). electroless nickel plating processes.Soil Properties suna da sauƙin rarrabe daga ƙasa launi, tsarin, da kuma carbonate content.The ƙasa texture ne matsakaici zuwa lafiya, samu daga iyaye material.They ne colluvial, alluvial ko aeolian a yanayi.Some ƙasa yankunan bayyana mottled a cikin surface da subsoil, sau da yawa tare da kankare da bleaching. Duk da haka, ƙasa cambisols ne na kowa a yankin da kuma mafi stats. daga 455.1 zuwa 493.5 m, cambisols sun mamaye 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 samfurori na 115 na saman ƙasa daga 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 ƙasa a zurfin 0 zuwa 20 cm ta amfani da na'urar da aka yi da hannu (Leica Zeno 5 na'urar GPS, da aka yi amfani da shi a cikin samfurin GPS, da aka yi amfani da shi a cikin jakar da aka yi amfani da shi a cikin GPS. dakin gwaje-gwaje.The samfurori da aka bushe-bushe don samar da pulverized samfurori, pulverized da wani inji tsarin (Fritsch Disc niƙa), da kuma sieved (sieve size 2 mm) . Place 1 gram na dried, homogenized da sieved ƙasa samfurori a fili labeled teflon kwalabe. A cikin kowane Teflon jirgin ruwa, rarraba 7 ml na 6 ml na 35% HHC atomatik da 35% H. dispenser - daya ga kowane acid), rufe da sauƙi kuma ba da izinin samfurori su tsaya a cikin 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 narkewa na samfurori, sa'an nan kuma kwantar da hankali. Canja wurin supernatant zuwa 50 ml na ruwa tare da 50 ml na ruwa. Wannan, tace mai dilumi a cikin bututun mai a cikin 50 ml pvc bututu tare da narkewar ruwa mai narkewa tare da ruwa mai narkewa. (Inductively Coupled Plasma Optical Emission Spectroscopy) (Thermo Fisher Scientific, Amurka) bisa ga daidaitattun hanyoyin da yarjejeniya.Tabbatar da Ingancin Tabbatarwa da Kulawa (QA / QC) hanyoyin (SRM NIST 2711a Montana II Soil) PTEs tare da iyakokin ganowa a ƙasa da rabi an cire su daga wannan binciken. 0.0004.(kai) . Bugu da ƙari, ana tabbatar da tsarin kula da inganci da ingantaccen tsari don kowane bincike ta hanyar nazarin ka'idojin tunani.Don tabbatar da cewa an rage girman kurakurai, an yi nazari sau biyu.
Empirical Bayesian Kriging (EBK) yana daya daga cikin fasahohin interpolation da yawa na geostatistical da aka yi amfani da su wajen yin tallan kayan kawa a fannoni daban-daban kamar kimiyyar ƙasa.Ba kamar sauran fasahohin kriging interpolation ba, EBK ya bambanta da hanyoyin kriging na gargajiya ta hanyar la'akari da kuskuren da aka ƙididdigewa ta hanyar ƙirar semivariogram. yi hanya don rashin tabbas da shirye-shiryen da ke da alaƙa da wannan ƙirƙira na semivariogram wanda ya ƙunshi wani yanki mai mahimmanci na isassun hanyar kriging.Tsarin interpolation na EBK ya bi ka'idodi uku da Krivoruchko50 ya gabatar, (a) ƙirar ƙididdigewa na semivariogram daga bayanan shigarwa (b) sabon ƙimar da aka annabta ga kowane wuri da aka ƙididdige shi da ƙima da ƙima ga kowane wuri da aka ƙididdigewa daga wurin da aka ƙididdigewa. simulated dataset.An ba da ka'idar lissafin Bayesian 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 'dokar, wanda ya nuna da propensity na lura datasets cewa za a iya ƙirƙira daga semivariograms ta amfani da semivariograms. yadda zai yiwu a ƙirƙiri bayanan abubuwan lura daga semivariogram.
Na'ura mai goyan baya shine algorithm koyo na inji 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 a fannoni daban-daban na SVM. An yi amfani da Regression - SVMR) a cikin wannan bincike.Cherkassky da Mulier53 sun fara SVMR a matsayin kernel-based regression, wanda aka yi amfani da lissafin wanda aka yi amfani da shi ta hanyar amfani da linzamin linzamin linzamin kwamfuta tare da ayyuka na sararin samaniya na kasashe da dama. da al. 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), ƙayyadaddun ƙasa yana ramawa ga ma'auni na tallafi na ƙananan bayanai. vectors.An nuna lissafin da Vapnik51 ya gabatar 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, da kuma \(y\) aikin da ake amfani da S. shine aikin tushen radial na Gaussian (RBF) .An yi amfani da kwaya na RBF don ƙayyade mafi kyawun samfurin SVMR, wanda ke da mahimmanci don samun mafi girman fa'idar saiti na penalty C da kernel parameter gamma (γ) don bayanan horo na PTE. Na farko, mun kimanta saitin horo sannan kuma gwada aikin ƙirar ƙirar akan saitin tabbatarwa.
Matsakaicin ƙira mai yawa na layi (MLR) ƙirar regression ce wacce ke wakiltar alaƙar da ke tsakanin madaidaicin amsa da adadin madaidaicin tsinkaya ta hanyar amfani da ma'auni masu ƙima da aka lissafta ta amfani da mafi ƙarancin murabba'i. dangantaka da ma'anoni masu bayyanawa.Ma'auni na 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 hanyar haɗin gwiwa don samun sababbin masu canji, irin su CaK, CaMg, da KMg. Abubuwan da ke cikin CaK, CaMg, da KMg. Abubuwan da ke cikin CaK, CaMg, da KMg sun kasance na hudu, CaKM da Mg. Ma'anar da aka samu sune Ca, K, Mg, CaK, CaMg, KMg da CaKMg. Wadannan masu canji sun zama masu tsinkayar mu, suna taimakawa wajen yin la'akari da yawan nickel a cikin birane da yankunan birane. An yi amfani da algorithm na SVMR akan masu tsinkaya don samun samfurin gauraye na Empirical Bayesian Kriging-Support Vector Machine (EBK_lySVM, ML Sismis) Empirical Bayesian Kriging-Multiple Linear Regression (EBK_MLR) .Yawanci, masu canji Ca, K, Mg, CaK, CaMg, KMg, da CaKMg ana amfani da su azaman covariates a matsayin masu tsinkayar abun ciki na Ni a cikin ƙasan birni da kewayen birni. Mafi karɓan samfurin da aka yarda da shi (EBK_SVM ko EBK_ML) wanda aka samu na gani da kansa. Ana nuna wannan binciken a hoto na 2.
Yin amfani da SeOM ya zama sanannen kayan aiki don tsarawa, kimantawa, da kuma kisa bayanai a cikin sassan kuɗi, kiwon lafiya, masana'antu, ƙididdiga, kimiyyar ƙasa, da ƙari. An ƙirƙiri SeOM ta amfani da hanyoyin sadarwa na wucin gadi da hanyoyin koyo marasa kulawa don tsari, kimantawa, da tsinkaya. Ana amfani da ƙimar SeOM azaman n shigarwa-girma vector masu canji43,56.Melssen et al. • Bayyana hanyar shigar da injin din da aka gabatar a cikin hanyar shiga guda ɗaya ta hanyar sanannun taswirar awo da ke haifar da shi a kan awo, madaidaicin kuskure (QE), samfurin Topographic tare da 0.086 da 0.904, bi da bi, an zaɓa, wanda shine naúrar taswira 55 (5 × 11) . An ƙaddara tsarin neuron bisa ga adadin nodes a cikin ma'auni mai mahimmanci.
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) . Ana 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 yanayin da aka yi amfani da shi a cikin samfurin nickll. Tsarin tabbatarwa sau goma sau goma, maimaita sau biyar. Abubuwan da aka samar da EBK interpolation ana amfani da su azaman 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 ("e1071"), ɗakin karatu ("e1071") ), ɗakin karatu ("PLyr" Toolbra), ɗakin karatu ("plyr" Toolbra) ("Metrics").
An yi amfani da sigogi daban-daban na tabbatarwa don ƙayyade mafi kyawun samfurin da ya dace don tsinkayar ƙididdiga na nickel a cikin ƙasa kuma don kimanta daidaiton samfurin da kuma tabbatar da shi.An ƙididdige samfurori na hybridization ta amfani da kuskuren kuskure (MAE), tushen kuskuren kuskure (RMSE), da R-squared ko ƙayyadaddun ƙayyadaddun ƙididdiga (R2). matakan suna kwatanta ikon tsinkaya na samfurin, yayin da MAE ke ƙayyade ainihin ƙimar ƙididdiga. Dole ne ƙimar R2 ta kasance mai girma don kimanta samfurin cakuda mafi kyau ta amfani da ma'auni na tabbatarwa, mafi kusa da darajar zuwa 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 ke 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. Mafi ƙarancin ƙima da ƙima na abubuwan suna cikin raguwar tsari na Mg
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 ba da shawarar cewa matakan su a cikin ƙasa sun bambanta da juna. Duk da haka, Ca da Mg suna adawa da K, amma CaK yana daidaitawa da kyau. Wannan na iya zama saboda aikace-aikacen takin mai magani irin su potassium carbonate, wanda shine 56% mafi girma a cikin potassium. Potassium an daidaita shi da daidaituwa tare da magnesium (KM r = 0.63), waɗannan abubuwa masu dangantaka da potassium sulfate a cikin masana'antar potassium sulfate. Nitrate, da potash ana amfani da ƙasa don ƙara yawan ƙarancin su.Nickel yana da alaƙa da haɗin gwiwa tare da Ca, K da Mg tare da ƙimar r = 0.52, 0.63 da 0.55, bi da bi. Alamar da ke tattare da calcium, magnesium, da PTEs irin su nickel suna da rikitarwa, amma duk da haka, duka magnesium a cikin abubuwan da ke hana calcium da calcium sun rage tasirin calcium da magnesium. sakamakon gubar 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 haɓaka na Ca a cikin Fig. 4 a yankin arewa maso yammacin taswirar rarraba sararin samaniya. Adadin ya nuna matsakaici zuwa babban Ca enrichment hotspots na arewa maso yammacin amfani da taswirar alli. quicklime (calcium oxide) don rage ƙasa acidity da kuma amfani da shi a karfe niƙa a matsayin 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 ƙara da alli abun ciki na earth71.Potassium kuma ya nuna zafi spots a arewa maso yamma da gabas na taswirar.The Arewa maso yammacin al'umma na iya zama matsakaici-matsayi ga al'umma na potassium. NPK da aikace-aikacen potash. Wannan ya dace da wasu nazarin, irin su Madaras da Lipavský72, Madaras et al.73, Pulkrabová et al.74, Asare et al.75, wanda ya lura cewa gyaran 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 bayyana 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. Wurare masu matsakaici a arewa maso yamma na taswira da kudu maso gabas na taswira. Ƙaddamarwa ta colloidal a cikin ƙasa yana rage ƙarancin magnesium a cikin ƙasa mai rawaya. chlorosis.Magnesium na tushen da takin mai magani, irin su potassium magnesium sulfate, magnesium sulfate, da Kieserite, bi da deficiency (shuke-shuke bayyana purple, ja, ko launin ruwan kasa, nuna magnesium rashi) a cikin kasa tare da al'ada pH range6.The tara na nickel a kan birane da yankunan-birane ƙasa saman na iya zama saboda anthropogenic samar da anthropogenic ayyuka kamar yadda agriculture samar da anthropogenic ayyuka.
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) . MAE na wannan binciken ta amfani da EBK don tsinkaya Ni an gano ya fi 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 samfurin Ca_Mg_K-EBK_SVMR model abk R2, tushen kuskure (ma'anar kuskure) da kuskuren murabba'in R2. 0.637 (R2), 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 (ƙasasshen MAE). Ca_Mg_K-SVMR (0.663 mg/kg R2) 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 suna girmamawa 137.5, wanda ya fi girma fiye da 137.5. Ca_Mg_K-EBK_SVMR.Hakazalika, RMSE da MAE na Ca_Mg-K SVMR (RMSE = 235.974 da MAE = 166.946) samfurin 2.5 da 2.2 ya fi girma fiye da na Ca_Mg_K-EBK_SVMR da aka ƙididdige bayanan RMSE da girmamawa. tare da layin mafi dacewa. An lura da RSME mafi girma da MAE. 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. mai nuna alama na gaban outliers. Wannan yana nufin cewa mafi daban-daban da dataset, da mafi girma da MAE da RMSE dabi'u. Daidaiton giciye-tabbatar da ca_Mg_K-EBK_SVMR gauraye model 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 fi sakamakon mu a cikin binciken na yanzu (RMSE 95.479, MAE 77.368). (0.637) tare da na Tarasov et al. 36 (0.544), a bayyane yake 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. 0.637 (R2) .Matsayin daidaito na wannan samfurin (EBK SVMR) shine 63.7%, yayin da tsinkayen 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 abubuwan da suka shafi PTE a matsayin jirgin sama wanda ya ƙunshi nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan nau'ikan. Al'ada mai launi zuwa gaurons da mafi ƙarancin neurons.us na matsakaici mai yawa da kuma ƙarancin launi. high, tsarin rarraba tsari na sassan 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, tsinkayar samfurin abun ciki na nickel (CakMg) yana kama da sararin samaniya na rarraba nickel da ƙananan nunin 5, Figures da aka nuna a cikin matsakaici na nickel. Nickel maida hankali a cikin birane da yankunan birane. Hoto na 7 yana nuna hanyar kwane-kwane a cikin k-ma'anar rukuni akan taswira, an raba shi zuwa gungu uku dangane da ƙimar da aka annabta a cikin kowane samfurin. 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 yayin da tari 3. ya karbi samfurori na 8. An sauƙaƙe haɗin haɗin gwaninta na kashi bakwai don ba da izini don daidaitaccen fassarar gungu. Saboda yawancin tsarin anthropogenic da na halitta da ke shafar tsarin ƙasa, yana da wuya a sami bambance-bambancen nau'i mai kyau 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 ƙasan 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 sararin fasali fasali na tallan kayan kawa dabara ya nuna babban launi juna daga ƙasa zuwa sama a kan daidaitaccen rarraba taswirar taswira a cikin ma'auni na taswira. ya tabbatar da tsarin rarraba sararin samaniya na abubuwan da aka nuna ta EBK_SVMR (duba Hoto 5) .Sakamakon ya nuna cewa samfurin goyan bayan na'ura mai ba da izini (Ca Mg K-SVMR) ya annabta ƙaddamar da Ni a cikin ƙasa a matsayin samfurin guda ɗaya, amma ma'auni na tabbatarwa da daidaito na kimantawa suna nuna manyan kurakurai cikin sharuddan RMSE da MAE.A daya hannun, da yin amfani da samfurin flaemwed saboda fasaha na ML. ƙananan ƙimar ƙididdiga na ƙaddara (R2) .An sami sakamako mai kyau ta amfani da EBK SVMR da abubuwan da aka haɗa (CaKMg) tare da ƙananan RMSE da kurakurai MAE tare da daidaito na 63.7% . Ya bayyana cewa hadawa da EBK algorithm tare da na'ura na ilmantarwa algorithm na iya haifar da wani matasan algorithm wanda zai iya hango ko hasashen da maida hankali na PTEs a cikin ƙasa.Sakamakon Ni K na iya yin tsinkaya ta hanyar yin amfani da ƙaddamarwa na PTE a cikin ƙasa. na Ni a cikin ƙasa.Wannan yana nufin cewa ci gaba da aikace-aikacen takin gargajiya na tushen nickel da gurɓataccen masana'antu na ƙasa ta masana'antar ƙarfe yana da yanayin haɓaka haɓakar nickel a cikin ƙasa.Wannan binciken ya nuna cewa ƙirar EBK na iya rage matakin kuskure kuma inganta daidaiton ƙirar sararin samaniyar ƙasa a cikin birane ko yankunan birni-birane. Gaba ɗaya, muna ba da shawarar yin amfani da samfurin PEBK don yin amfani da ƙasa na PEBK. 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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Lokacin aikawa: Jul-22-2022
