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Gurɓatar ƙasa babbar matsala ce da ayyukan ɗan adam ke haifarwa. Rarraba wurare na abubuwa masu guba (PTEs) ya bambanta a yawancin yankunan birane da birane. Saboda haka, yana da wuya a yi hasashen abubuwan da ke cikin PTE a cikin irin waɗannan ƙasa a sarari. An samo jimillar samfura 115 daga Frydek Mistek a Jamhuriyar Czech. An ƙayyade yawan Calcium (Ca), magnesium (Mg), potassium (K) da nickel (Ni) ta amfani da inductively coupled plasma emission spectrometry. Maɓallin amsawa shine Ni kuma masu hasashen sune Ca, Mg, da K. Ma'aunin haɗin gwiwa tsakanin maɓallin amsawa da maɓallin hasashen yana nuna alaƙa mai gamsarwa tsakanin abubuwan. Sakamakon hasashen ya nuna cewa Tallafin Injin Tallafi (SVMR) ya yi aiki da kyau, kodayake ƙimar kuskuren tushe na murabba'i (RMSE) (235.974 mg/kg) da matsakaicin kuskuren cikakke (MAE) (166.946 mg/kg) sun fi sauran hanyoyin da aka yi amfani da su. Samfuran gauraye don Empirical Bayesian Kriging-Multiple Linear Regression (EBK-MLR) suna aiki. ba shi da kyau, kamar yadda aka nuna ta hanyar ƙididdigar tantancewa ƙasa da 0.1. Tsarin Empirical Bayesian Kriging-Support Vector Machine Regression (EBK-SVMR) shine mafi kyawun samfurin, tare da ƙimar RMSE mai ƙarancin girma (95.479 mg/kg) da MAE (77.368 mg/kg) ​​da kuma babban ƙimar tantancewa (R2 = 0.637). Ana iya ganin fitowar dabarar ƙirar EBK-SVMR ta amfani da taswirar tsara kai. Jijiyoyin da aka haɗa a cikin samfurin haɗin CakMg-EBK-SVMR suna nuna alamu masu launi da yawa waɗanda ke hasashen yawan Ni a cikin ƙasa ta birni da ta birni. Sakamakon ya nuna cewa haɗa EBK da SVMR dabara ce mai tasiri don annabta yawan Ni a cikin ƙasa ta birni da ta birni.
Ana ɗaukar Nickel (Ni) a matsayin ƙaramin sinadari ga tsirrai domin yana taimakawa wajen daidaita nitrogen a yanayi (N) da kuma metabolism na urea, waɗanda duka ake buƙata don haɓakar iri. Baya ga gudummawarsa ga haɓakar iri, Ni na iya aiki a matsayin mai hana fungal da ƙwayoyin cuta da kuma haɓaka haɓakar shuka. Rashin nickel a cikin ƙasa yana ba shuka damar shan sa, wanda ke haifar da chlorosis na ganye. Misali, wake da wake kore suna buƙatar amfani da takin mai tushen nickel don inganta gyaran nitrogen2. Ci gaba da amfani da takin mai tushen nickel don wadatar da ƙasa da haɓaka ikon wake don gyara nitrogen a cikin ƙasa akai-akai yana ƙara yawan sinadarin nickel a cikin ƙasa. Duk da cewa nickel ƙaramin sinadari ne ga tsirrai, yawan shan sa a cikin ƙasa na iya yin illa fiye da kyau. Guba na nickel a cikin ƙasa yana rage pH na ƙasa kuma yana hana ɗaukar ƙarfe a matsayin muhimmin sinadari don haɓakar shuka1. A cewar Liu3, an gano Ni shine muhimmin sinadari na 17 da ake buƙata don haɓaka shuka da haɓaka ta. Baya ga rawar da nickel ke takawa wajen haɓaka shuka da haɓaka ta, mutane suna buƙatar sa don aikace-aikace iri-iri. Lantarki, samar da tushen nickel ƙarfe, da ƙera na'urorin kunna wuta da kuma fulawoyi masu walƙiya a masana'antar kera motoci duk suna buƙatar amfani da nickel a sassa daban-daban na masana'antu. Bugu da ƙari, an yi amfani da ƙarfe masu tushen nickel da kayan lantarki a cikin kayan kicin, kayan haɗin ɗakin rawa, kayan masana'antar abinci, wutar lantarki, waya da kebul, injinan jet, kayan tiyata, yadi, da gina jirgi. Matakan Ni-mai-yawa a cikin ƙasa (watau, ƙasa mai saman ƙasa) an danganta su da tushen ɗan adam da na halitta, amma galibi, Ni tushen halitta ne maimakon na ɗan adam4,6. Tushen halitta na nickel sun haɗa da fashewar aman wuta, ciyayi, gobarar daji, da hanyoyin ƙasa; duk da haka, tushen ɗan adam ya haɗa da batirin nickel/cadmium a masana'antar ƙarfe, electroplating, walda arc, man dizal da mai, da hayakin yanayi daga ƙonewa da sharar gida da laluge. Tarin Nickel7,8. A cewar Freedman da Hutchinson9 da Manyiwa et al. 10, manyan hanyoyin gurɓatar ƙasa a saman ƙasa a cikin muhallin da ke kusa da shi da kuma kewaye da shi galibi sune na'urorin narkar da ma'adanai da aka yi da nickel da jan ƙarfe. Ƙasa mafi girma da ke kewaye da matatar nickel da jan ƙarfe ta Sudbury a Kanada tana da mafi girman matakan gurɓatar nickel a 26,000 mg/kg11. Sabanin haka, gurɓatar da aka samu daga samar da nickel a Rasha ta haifar da yawan nickel a cikin ƙasa ta Norway11. A cewar Alms et al. 12, adadin nickel da ake cirewa daga HNO3 a cikin babban ƙasar noma a yankin (samar da nickel a Rasha) ya kama daga 6.25 zuwa 136.88 mg/kg, wanda ya yi daidai da matsakaicin 30.43 mg/kg da kuma yawan tushen 25 mg/kg. A cewar kabata 11, amfani da takin phosphorus a cikin ƙasan noma a cikin ƙasa ta birni ko ta birni a lokacin lokutan amfanin gona na jere na iya haifar da ko gurɓata ƙasa. Illar nickel a cikin mutane na iya haifar da ciwon daji ta hanyar maye gurbi, lalacewar chromosomal, samar da Z-DNA, gyara cire DNA da aka toshe, ko hanyoyin epigenetic13. A cikin gwaje-gwajen dabbobi, an gano cewa nickel yana da yuwuwar haifar da nau'ikan ciwace-ciwacen daji, kuma hadaddun nickel masu cutar kansa na iya ƙara ta'azzara irin waɗannan ciwace-ciwacen.
Kimanta gurɓatar ƙasa ta bunƙasa a cikin 'yan lokutan nan saboda ɗimbin batutuwa da suka shafi lafiya da suka taso daga alaƙar ƙasa da tsire-tsire, alaƙar halittu ta ƙasa da ƙasa, lalacewar muhalli, da kimanta tasirin muhalli. Zuwa yanzu, hasashen sararin samaniya na abubuwa masu guba (PTEs) kamar Ni a cikin ƙasa ya kasance mai wahala kuma yana ɗaukar lokaci ta amfani da hanyoyin gargajiya. Zuwan taswirar ƙasa ta dijital (DSM) da nasarar da take samu a yanzu15 sun inganta taswirar ƙasa mai hasashen yanayi (PSM). A cewar Minasny da McBratney16, taswirar ƙasa mai hasashen yanayi (DSM) ta tabbatar da cewa ita ce babbar ƙungiya a fannin kimiyyar ƙasa. Lagacherie da McBratney, 2006 sun bayyana DSM a matsayin "ƙirƙirar da cike tsarin bayanai na ƙasa ta sararin samaniya ta hanyar amfani da hanyoyin lura a wuri da dakin gwaje-gwaje da tsarin ƙididdiga na ƙasa ta sararin samaniya da ba ta sararin samaniya ba".McBratney et al. 17 ya bayyana cewa DSM ko PSM na zamani ita ce hanya mafi inganci don annabta ko zana taswirar rarrabawar sarari na PTEs, nau'ikan ƙasa da halayen ƙasa. Tsarin Geostatistics da Tsarin Koyon Inji (MLA) dabarun ƙirar DSM ne waɗanda ke ƙirƙirar taswira masu dijital tare da taimakon kwamfutoci ta amfani da bayanai masu mahimmanci da ƙarancin bayanai.
Deutsch18 da Olea19 sun bayyana ilimin geostatistics a matsayin "tarin dabarun lambobi waɗanda ke magance wakilcin halayen sararin samaniya, galibi suna amfani da samfuran stochastic, kamar yadda nazarin jerin lokaci ke siffanta bayanan lokaci." Ainihin, ilimin geostatistics ya ƙunshi kimantawa na variograms, wanda ke ba da damar Quantify da ayyana dogaro da ƙimar sararin samaniya daga kowane bayanan data20. Gumiaux et al. 20 sun ƙara nuna cewa kimantawa na variograms a cikin ilimin geostatistics ya dogara ne akan ƙa'idodi uku, gami da (a) ƙididdige girman haɗin bayanai, (b) gano da ƙididdige anisotropy a cikin rashin daidaiton bayanai da (c) ban da la'akari da kuskuren da ke cikin bayanan aunawa da aka raba daga tasirin gida, ana kuma kimanta tasirin yanki. Dangane da waɗannan ra'ayoyin, ana amfani da dabarun haɗa bayanai da yawa a cikin ilimin geostatistics, gami da kriging na gabaɗaya, co-kriging, kriging na yau da kullun, kriging na Bayesian na empirical, hanyar kriging mai sauƙi da sauran sanannun dabarun haɗa bayanai don taswira ko annabta PTE, halayen ƙasa, da nau'ikan ƙasa.
Algorithms na Koyon Inji (MLA) wata sabuwar dabara ce da ke amfani da manyan azuzuwan bayanai marasa layi, waɗanda algorithms galibi ake amfani da su don haƙar bayanai, gano alamu a cikin bayanai, kuma ana amfani da su akai-akai don rarrabuwa a fannoni na kimiyya kamar kimiyyar ƙasa da ayyukan dawo da su. Takardu da yawa na bincike sun dogara da samfuran MLA don annabta PTE a cikin ƙasa, kamar Tan et al. 22 (dazuzzuka bazuwar don kimanta ƙarfe mai nauyi a cikin ƙasan noma), Sakizadeh et al. 23 (ƙirƙirar samfura ta amfani da injunan vector masu tallafi da hanyoyin sadarwa na jijiyoyi na wucin gadi) gurɓatar ƙasa). Bugu da ƙari, Vega et al. 24 (CART don yin kwaikwayon riƙe ƙarfe mai nauyi da shawa a cikin ƙasa) Sun et al. 25 (amfani da cubist shine rarraba Cd a cikin ƙasa) da sauran algorithms kamar k-maƙwabci mafi kusa, sake dawowa gabaɗaya, da sake dawowa mai ƙarfi Bishiyoyi suma sun yi amfani da MLA don annabta PTE a cikin ƙasa.
Amfani da tsarin DSM a cikin hasashen ko taswirar yana fuskantar ƙalubale da dama. Marubuta da yawa sun yi imanin cewa MLA ta fi ilimin ƙasa da akasin haka. Duk da cewa ɗaya ya fi ɗayan kyau, haɗin biyun yana inganta matakin daidaito na taswirar ko hasashen a cikin DSM15. Woodcock da Gopal26 Finke27; Pontius da Cheuk28 da Grunwald29 sun yi tsokaci kan gazawa da wasu kurakurai a cikin taswirar ƙasa da aka annabta. Masana kimiyyar ƙasa sun gwada hanyoyi daban-daban don inganta inganci, daidaito, da kuma hasashen taswirar DSM da hasashen. Haɗin rashin tabbas da tabbatarwa yana ɗaya daga cikin fannoni daban-daban da aka haɗa cikin DSM don inganta inganci da rage lahani. Duk da haka, Agyeman et al. 15 sun bayyana cewa ya kamata a tabbatar da halayen tabbatarwa da rashin tabbas da ƙirƙirar taswira da hasashen suka gabatar da kansu don inganta ingancin taswira. Iyakokin DSM sun faru ne saboda ingancin ƙasa da aka watsar a yanki, wanda ya ƙunshi ɓangaren rashin tabbas; duk da haka, rashin tabbas a cikin DSM na iya tasowa daga tushe da yawa na kuskure, wato kuskuren covariate, kuskuren samfuri, kuskuren wuri, da Kuskuren nazari 31. Rashin daidaiton ƙira da aka haifar a cikin MLA da hanyoyin geostatistical suna da alaƙa da rashin fahimta, wanda a ƙarshe ke haifar da sauƙaƙe ainihin tsari32. Ko da kuwa yanayin ƙirar, rashin daidaito ana iya danganta shi da sigogin ƙira, hasashen samfurin lissafi, ko haɗin gwiwa33. Kwanan nan, wani sabon yanayin DSM ya bayyana wanda ke haɓaka haɗakar geostatistics da MLA a cikin taswira da hasashen. Masana kimiyya da marubuta da yawa na ƙasa, kamar Sergeev et al. 34; Subbotina et al. 35; Tarasov et al. 36 da Tarasov et al. 37 sun yi amfani da ingantaccen ingancin geostatistics da koyon injina don samar da samfuran haɗin gwiwa waɗanda ke inganta ingancin hasashen da taswira. Inganci. Wasu daga cikin waɗannan samfuran algorithm masu haɗaka ko waɗanda aka haɗa sune Artificial Neural Network Kriging (ANN-RK), Multilayer Perceptron Residual Kriging (MLP-RK), Generalized Regression Neural Network Residual Kriging (GR- NNRK)36, Artificial Neural Network Kriging-Multilayer Perceptron (ANN-K-MLP)37 da Co-Kriging da Gaussian Process Regression38.
A cewar Sergeev da abokan aikinsa, haɗa dabarun yin ƙira daban-daban yana da yuwuwar kawar da lahani da kuma ƙara ingancin samfurin haɗin gwiwa da aka samu maimakon haɓaka samfurinsa guda ɗaya. A cikin wannan mahallin, wannan sabon takarda ta yi jayayya cewa ya zama dole a yi amfani da haɗin algorithm na geostatistics da MLA don ƙirƙirar samfuran haɗin gwiwa mafi kyau don annabta wadatar Ni a yankunan birane da birane. Wannan binciken zai dogara ne akan Empirical Bayesian Kriging (EBK) a matsayin samfurin tushe kuma ya haɗa shi da samfuran Tallafin Vector (SVM) da Multiple Linear Regression (MLR). Ba a san haɗin EBK da kowane MLA ba. Samfuran gauraye da yawa da aka gani haɗuwa ne na yau da kullun, ragowar, regression kriging, da MLA.EBK wata hanya ce ta haɗin geostatistical wadda ke amfani da tsari mai stochastic na sarari wanda aka keɓe shi azaman filin da ba na tsayawa/tsayawa ba tare da takamaiman sigogin wurin zama a kan filin, wanda ke ba da damar bambancin sarari39. An yi amfani da EBK a cikin nau'ikan bincike daban-daban, gami da nazarin rarraba carbon na halitta a cikin ƙasa na gona40, tantance gurɓatar ƙasa41 da taswirar ƙasa kadarori42.
A gefe guda kuma, Jadawalin Tsara Kai (SeOM) wani tsari ne na ilmantarwa 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 sarari da haɗa abubuwa.Wang et al. 44 sun bayyana cewa SeOM wata dabara ce mai ƙarfi ta ilmantarwa da aka sani da ikonta na haɗa abubuwa da tunanin matsalolin da ba na layi ba. Ba kamar sauran dabarun gane tsari kamar babban nazarin sassan ba, haɗa abubuwa masu duhu, haɗa abubuwa masu tsari, da yanke shawara mai ma'ana da yawa ba, SeOM ta fi kyau wajen tsarawa da gano tsarin PTE. A cewar Wang et al. 44, SeOM na iya rarraba rarrabawar ƙwayoyin jijiyoyi masu alaƙa a sarari kuma ta samar da hangen nesa mai ƙuduri mai girma. SeOM za ta iya hango bayanan hasashen Ni don samun mafi kyawun samfurin don siffanta sakamakon don fassarar kai tsaye.
Wannan takarda tana da nufin samar da ingantaccen tsarin taswirar taswira tare da ingantaccen daidaito don annabta abubuwan da ke cikin nickel a cikin ƙasa na birane da na birane. Muna tsammanin cewa amincin tsarin gauraye ya dogara ne akan tasirin wasu samfuran da aka haɗa da samfurin tushe. Mun yarda da ƙalubalen da DSM ke fuskanta, kuma yayin da ake magance waɗannan ƙalubalen a fannoni da yawa, haɗin ci gaba a cikin ƙirar ƙasa da samfuran MLA ya zama kamar ƙarami; saboda haka, za mu yi ƙoƙarin amsa tambayoyin bincike waɗanda za su iya haifar da samfuran gauraye. Duk da haka, yaya daidaiton samfurin yake wajen annabta abin da aka nufa? Hakanan, menene matakin kimanta inganci bisa ga tantancewa da daidaito? Saboda haka, takamaiman manufofin wannan binciken shine (a) ƙirƙirar samfurin gauraye don SVMR ko MLR ta amfani da EBK azaman samfurin tushe, (b) kwatanta samfuran da suka haifar (c) gabatar da mafi kyawun samfurin gauraye don annabta yawan Ni a cikin ƙasa na birni ko na birni, da (d) aikace-aikacen SeOM don ƙirƙirar taswirar babban ƙuduri na bambancin sararin nickel.
Ana gudanar da wannan binciken a Jamhuriyar Czech, musamman a gundumar Frydek Mistek a yankin Moravia-Silesian (duba Hoto na 1). Yanayin ƙasa na yankin binciken yana da ƙarfi sosai kuma galibi ɓangare ne na yankin Moravia-Silesian Beskidy, wanda ke cikin gefen waje na Dutsen Carpathian. Yankin binciken yana tsakanin 49° 41′ 0′ N da 18° 20′ 0′ E, kuma tsayin yana tsakanin 225 da 327 m; duk da haka, tsarin rarraba Koppen don yanayin yanayi na yankin an kimanta shi azaman Cfb = yanayin teku mai zafi, Akwai ruwan sama mai yawa koda a cikin watannin bushewa. Zafin jiki ya bambanta kaɗan a cikin shekara tsakanin −5 °C da 24 °C, ba kasafai yake faɗuwa ƙasa da −14 °C ko sama da 30 °C ba, yayin da matsakaicin ruwan sama na shekara-shekara yana tsakanin 685 da 752 mm47. Yankin bincike na duk yankin shine murabba'in kilomita 1,208, tare da 39.38% na ƙasar noma da 49.36% na kewayen daji. A gefe guda kuma, yankin da aka yi amfani da shi a cikin wannan binciken shine kusan murabba'in kilomita 889.8. A cikin da kewayen Ostrava, masana'antar ƙarfe da ayyukan ƙarfe suna da ƙarfi sosai. Masana'antar ƙarfe, masana'antar ƙarfe inda ake amfani da nickel a cikin ƙarfe mai bakin ƙarfe (misali don juriya ga lalata yanayi) da ƙarfe mai ƙarfe (nickel yana ƙara ƙarfin ƙarfe yayin da yake kiyaye kyakkyawan juriya da tauri), da kuma noma mai zurfi kamar amfani da takin phosphate da samar da dabbobi sune tushen bincike na nickel. a yankin (misali, ƙara nickel ga raguna don ƙara yawan girma a cikin raguna da shanu masu ƙarancin kiwo). Sauran amfani da nickel a masana'antu a fannonin bincike sun haɗa da amfani da shi a cikin electroplating, gami da electroplating nickel da electroplating nickel plating. Ana iya bambanta halayen ƙasa cikin sauƙi daga launin ƙasa, tsari, da abun ciki na carbonate. Tsarin ƙasa matsakaici ne zuwa mai kyau, wanda aka samo daga kayan iyaye. Suna da alaƙa, alluvial ko aeolian a yanayi. Wasu yankunan ƙasa suna bayyana a cikin ƙasa da ƙasa, sau da yawa tare da siminti da bleaching. Duk da haka, cambisols da stagnosols sune nau'ikan ƙasa da aka fi sani a yankin48. Tare da tsayin daka daga 455.1 zuwa 493.5 m, cambisols sun mamaye Jamhuriyar Czech49.
Taswirar yankin karatu [An ƙirƙiri taswirar yankin karatu ta amfani da ArcGIS Desktop (ESRI, Inc, sigar 10.7, URL: https://desktop.arcgis.com).]
An samo jimillar samfuran ƙasa 115 daga ƙasan birni da na birni a gundumar Frydek Mistek. Tsarin samfurin da aka yi amfani da shi shine grid na yau da kullun tare da samfuran ƙasa a nisan kilomita 2 × 2, kuma an auna ƙasan a zurfin santimita 0 zuwa 20 ta amfani da na'urar GPS mai riƙe da hannu (Leica Zeno 5 GPS). Ana naɗe samfuran a cikin jakunkunan Ziploc, an yi musu lakabi da kyau, sannan aka aika su zuwa dakin gwaje-gwaje. An busar da samfuran ta hanyar iska don samar da samfuran da aka niƙa, an niƙa su ta hanyar injina (Fritsch disc niƙa), sannan aka tace su (girman sieve 2 mm). A sanya gram 1 na samfuran ƙasa busasshe, an yi musu tsari iri ɗaya kuma an tace su a cikin kwalaben teflon masu lakabi. A cikin kowace tukunyar Teflon, a zuba 7 ml na HCl 35% da 3 ml na 65% HNO3 (ta amfani da na'urar rarrabawa ta atomatik - ɗaya ga kowane acid), a rufe su da sauƙi kuma a bar samfuran su tsaya na dare don amsawar (shirin aqua regia). A sanya ruwan sama a kan farantin ƙarfe mai zafi (zafin jiki: 100 W da 160 °C) na tsawon awanni 2 don sauƙaƙe tsarin narkewar samfuran, sannan a sanyaya. A mayar da ruwan da ke cikin kwalbar zuwa kwalbar volumetric mai girman 50 ml sannan a narkar da shi zuwa 50 ml da ruwan da aka narkar da shi. Bayan haka, a tace ruwan da aka narkar da shi zuwa bututun PVC mai girman 50 ml da ruwan da aka narkar da shi. Bugu da ƙari, an narkar da ruwan da aka narkar da shi da 9 ml na ruwan da aka narkar da shi sannan a tace shi a cikin bututun 12 ml da aka shirya don PTE pseudo-concentration. An ƙayyade yawan PTEs (As, Cd, Cr, Cu, Mn, Ni, Pb, Zn, Ca, Mg, K) ta hanyar ICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy) (Thermo Fisher Scientific, Amurka) bisa ga hanyoyin da aka saba da yarjejeniya. Tabbatar da Inganci da Kulawa (QA/QC) hanyoyin (SRM NIST 2711a Montana II Soil). An cire PTEs tare da iyakokin ganowa ƙasa da rabi daga wannan binciken. Iyakar gano PTE da aka yi amfani da shi a cikin wannan binciken ya kasance 0.0004.(ku). Bugu da ƙari, ana tabbatar da tsarin kula da inganci da tabbatar da inganci ga kowane bincike ta hanyar nazarin ƙa'idodin tunani. Don tabbatar da cewa an rage kurakurai, an yi bincike sau biyu.
Empirical Bayesian Kriging (EBK) yana ɗaya daga cikin dabarun haɗa bayanai na geostatistical da ake amfani da su wajen yin samfuri a fannoni daban-daban kamar kimiyyar ƙasa. Ba kamar sauran dabarun haɗa bayanai na kriging ba, EBK ya bambanta da hanyoyin haɗa bayanai na gargajiya ta hanyar la'akari da kuskuren da aka kiyasta ta hanyar samfurin semivariogram. A cikin haɗa bayanai na EBK, ana ƙididdige samfuran semivariogram da yawa yayin haɗa bayanai, maimakon semivariogram guda ɗaya. Dabaru na haɗa bayanai sun ba da hanya ga rashin tabbas da shirye-shirye da ke da alaƙa da wannan zane na semivariogram wanda ya ƙunshi wani ɓangare mai rikitarwa na hanyar kriging mai isa. Tsarin haɗa bayanai na EBK yana bin sharuɗɗa uku da Krivoruchko50 ya gabatar, (a) samfurin yana kimanta semivariogram daga bayanan shigarwa (b) sabon ƙimar da aka annabta don kowane wurin bayanan shigarwa bisa ga semivariogram da aka samar da kuma (c) an ƙididdige samfurin A na ƙarshe daga bayanan da aka kwaikwayi. An ba da ƙa'idar daidaiton Bayesian a matsayin baya
Inda \(Prob\left(A\right)\) ke wakiltar abin da ya gabata, \(Prob\left(B\right)\) ana yin watsi da yiwuwar gefen dama a mafi yawan lokuta, \(Prob (B,A)\) . Lissafin semivariogram ya dogara ne akan ƙa'idar Bayes, wanda ke nuna yuwuwar bayanan lura da za a iya ƙirƙira daga semivariograms. Sannan ana tantance ƙimar semivariogram ta amfani da ƙa'idar Bayes, wanda ke bayyana yadda ake iya ƙirƙirar bayanan lura daga semivariogram.
Injin tallafi na vector algorithm ne na koyon injin wanda ke samar da ingantaccen tsarin rabawa don bambance azuzuwan da ba su da alaƙa da layi.Vapnik51 ya ƙirƙiri tsarin rarraba 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 rarrabawa kuma an yi amfani da shi a fannoni daban-daban. An yi amfani da ɓangaren komawa-baya na SVM (Tallafin Injin Tallafin Vector - SVMR) a cikin wannan binciken. Cherkassky da Mulier53 sun fara SVMR a matsayin koma-baya bisa tushen kernel, wanda aka yi lissafinsa ta amfani da samfurin komawa-baya na layi tare da ayyukan sarari na ƙasashe da yawa. John et al54 sun ba da rahoton cewa ƙirar SVMR tana amfani da koma-baya na layi na hyperplane, wanda ke ƙirƙirar alaƙa mara layi kuma yana ba da damar ayyukan sarari. A cewar Vohland et al. 55, epsilon (ε)-SVMR yana amfani da bayanan da aka horar don samun samfurin wakilci azaman aikin da ba shi da hankali na epsilon wanda aka yi amfani da shi don tsara bayanan daban-daban tare da mafi kyawun son zuciya na epsilon daga horo akan bayanai masu alaƙa. Ana yin watsi da kuskuren nisan da aka saita daga ainihin ƙimar, kuma idan kuskuren ya fi girma fiye da ε(ε), halayen ƙasa suna rama shi. Tsarin kuma yana rage sarkakiyar bayanan horo zuwa wani babban ɓangaren vectors na tallafi. An nuna daidaiton da Vapnik51 ya gabatar a ƙasa.
inda b ke wakiltar matakin scalar, \(K\left({x}_{,}{ x}_{k}\right)\) yana wakiltar aikin kernel, \(\alpha\) yana wakiltar mai ninka Lagrange, N Yana wakiltar bayanan lambobi, \({x}_{k}\) yana wakiltar shigar bayanai, kuma \(y\) shine fitarwar bayanai. Ɗaya daga cikin maɓallan kernel da aka yi amfani da su shine aikin SVMR, wanda shine aikin tushen radial na Gaussian (RBF). Ana amfani da kernel na RBF don tantance mafi kyawun samfurin SVMR, wanda yake da mahimmanci don samun mafi ƙarancin hukuncin saitin C da gamma na kernel (γ) don bayanan horon PTE. Da farko, mun kimanta saitin horo sannan muka gwada aikin samfurin akan saitin tabbatarwa. Sigar tuƙi da aka yi amfani da ita shine sigma kuma ƙimar hanyar ita ce svmRadial.
Tsarin komawa ga layi mai yawa (MLR) samfurin komawa ga layi ne wanda ke wakiltar alaƙar da ke tsakanin canjin amsawa da adadin masu hasashen yanayi ta hanyar amfani da sigogin da aka haɗa masu layi ta hanyar amfani da hanyar mafi ƙarancin murabba'i. A cikin MLR, samfurin mafi ƙarancin murabba'i aiki ne na hasashen halayen ƙasa bayan zaɓar masu canjin bayani. Ya zama dole a yi amfani da martanin don kafa alaƙar layi ta amfani da masu canjin bayani. An yi amfani da PTE azaman mai canjin martani don kafa alaƙar layi tare da masu canjin bayani. Daidaiton MLR shine
inda y shine canjin amsawa, \(a\) shine tsangwama, n shine adadin masu hasashen, \({b}_{1}\) shine raguwar ɓangaren ma'auni, \({x}_{i}\) yana wakiltar canjin mai hasashen ko bayani, kuma \({\varepsilon }_{i}\) yana wakiltar kuskuren da ke cikin samfurin, wanda kuma aka sani da ragowar.
An samo samfuran gauraye ta hanyar haɗa EBK da SVMR da MLR. Ana yin wannan ta hanyar cire ƙimar da aka annabta daga haɗin EBK. Ana samun ƙimar da aka annabta da aka samu daga haɗin Ca, K, da Mg ta hanyar haɗin gwiwa don samun sabbin masu canji, kamar CaK, CaMg, da KMg. Sannan ana haɗa abubuwan Ca, K da Mg don samun mai canji na huɗu, CaKMg. Gabaɗaya, masu canji da aka samu sune Ca, K, Mg, CaK, CaMg, KMg da CaKMg. Waɗannan masu canji sun zama masu hasashenmu, suna taimakawa wajen hasashen yawan nickel a cikin ƙasa na birni da na birni. An yi tsarin SVMR akan masu hasashen don samun samfurin gauraye Injin Vector na Empirical Bayesian Kriging-Support (EBK_SVM). Hakazalika, ana haɗa masu canji ta hanyar tsarin MLR don samun samfurin gauraye Empirical Bayesian Kriging-Multiple Linear Regression (EBK_MLR). Yawanci, masu canji Ca, K, Ana amfani da Mg, CaK, CaMg, KMg, da CaKMg a matsayin masu hasashen yawan Ni a cikin ƙasa ta birane da kuma ta birane. Za a iya ganin samfurin da aka fi yarda da shi (EBK_SVM ko EBK_MLR) ta amfani da jadawalin tsara kai. An nuna tsarin aikin wannan binciken a cikin Hoto na 2.
Amfani da SeOM ya zama kayan aiki mai shahara don tsarawa, kimantawa, da hasashen bayanai a fannin kuɗi, kiwon lafiya, masana'antu, ƙididdiga, kimiyyar ƙasa, da ƙari. An ƙirƙiri SeOM ta amfani da hanyoyin sadarwa na jijiyoyi na wucin gadi da hanyoyin koyo marasa kulawa don tsarawa, kimantawa, da hasashe. A cikin wannan binciken, an yi amfani da SeOM don hango yawan Ni bisa ga mafi kyawun samfurin don annabta Ni a cikin ƙasa na birni da na birni. Ana amfani da bayanan da aka sarrafa a cikin kimantawar SeOM azaman masu canjin vector na n shigarwa43,56.Melssen et al. 57 ya bayyana haɗin vector mai shigarwa zuwa hanyar sadarwa ta jijiyoyi ta hanyar layin shigarwa guda ɗaya zuwa vector mai fitarwa tare da vector mai nauyi ɗaya. Fitowar da SeOM ta samar taswira ce mai girma biyu wacce ta ƙunshi jijiyoyi daban-daban ko ƙusoshi da aka saka a cikin taswirorin hexagonal, da'ira, ko murabba'i gwargwadon kusancinsu. Idan aka kwatanta girman taswira bisa ga ma'auni, kuskuren ƙididdigewa (QE) da kuskuren yanayin ƙasa (TE), an zaɓi samfurin SeOM tare da 0.086 da 0.904, bi da bi, wanda shine naúrar taswira 55 (5 × 11). Tsarin neuron an ƙaddara shi bisa ga adadin ƙusoshin a cikin lissafin gwaji.
Adadin bayanan da aka yi amfani da su a cikin wannan binciken samfura 115 ne. An yi amfani da wata hanya ta bazuwar don raba bayanan zuwa bayanan gwaji (25% don tabbatarwa) da kuma saitin bayanan horo (75% don daidaitawa). Ana amfani da bayanan horo don samar da samfurin komawa baya (daidaitawa), kuma ana amfani da bayanan gwaji don tabbatar da ikon gabaɗaya58. An yi wannan ne don tantance dacewa da samfura daban-daban don annabta abubuwan da ke cikin nickel a cikin ƙasa. Duk samfuran da aka yi amfani da su sun wuce tsarin tabbatarwa sau goma, an maimaita sau biyar. Ana amfani da masu canjin da EBK ya samar azaman masu hasashen ko masu canji masu bayani don annabta canjin manufa (PTE). Ana sarrafa ƙirar a cikin RStudio ta amfani da ɗakin karatu na fakiti (Kohonen), ɗakin karatu (caret), ɗakin karatu (modelr), ɗakin karatu (“e1071″), ɗakin karatu (“plyr”), ɗakin karatu (“caTools”), ɗakin karatu (“prospecter”) da ɗakunan karatu (“Metrics”).
An yi amfani da sigogi daban-daban na tabbatarwa don tantance mafi kyawun samfurin da ya dace da hasashen yawan nickel a cikin ƙasa da kuma kimanta daidaiton samfurin da ingancinsa. An kimanta samfuran haɗaka ta amfani da matsakaicin kuskuren cikakke (MAE), kuskuren tushen matsakaicin murabba'i (RMSE), da kuma ƙaddarar R-squared ko coefficient (R2). R2 yana bayyana bambancin rabo a cikin amsar, wanda samfurin komawa baya ya wakilta. Girman RMSE da bambancin a cikin ma'auni masu zaman kansu suna bayyana ƙarfin hasashen samfurin, yayin da MAE ke tantance ainihin ƙimar adadi. Dole ne ƙimar R2 ta kasance mai girma don kimanta mafi kyawun samfurin cakuda ta amfani da sigogin tabbatarwa, kusan ƙimar zuwa 1, mafi girman daidaito. A cewar Li et al. 59, ana ɗaukar ƙimar ma'aunin R2 na 0.75 ko sama da haka a matsayin kyakkyawan hasashen; daga 0.5 zuwa 0.75 aikin samfuri ne mai karɓuwa, kuma ƙasa da 0.5 aikin samfuri ne mara karɓuwa. Lokacin zaɓar samfuri ta amfani da hanyoyin kimanta ka'idojin tabbatarwa na RMSE da MAE, ƙananan ƙimar da aka samu sun isa kuma an ɗauke su mafi kyawun zaɓi. Lissafi mai zuwa yana bayyana hanyar tabbatarwa.
inda n ke wakiltar girman ƙimar da aka lura\({Y}_{i}\) yana wakiltar amsar da aka auna, kuma \({\widehat{Y}}_{i}\) shi ma yana wakiltar ƙimar amsawar da aka annabta, saboda haka, don lura da i na farko.
An gabatar da bayanin kididdiga na masu hasashen da masu amsawa a cikin Jadawali na 1, yana nuna matsakaicin karkacewa na yau da kullun (SD), ma'aunin bambancin (CV), mafi ƙaranci, matsakaicin, kurtosis, da karkacewa. Mafi ƙarancin ƙima da matsakaicin ƙima na abubuwan suna cikin tsarin raguwa na Mg < Ca < K < Ni da Ca < Mg < K < Ni, bi da bi. Yawan ma'aunin amsawa (Ni) da aka samo daga yankin binciken ya kasance daga 4.86 zuwa 42.39 mg/kg. Kwatanta Ni da matsakaicin duniya (29 mg/kg) da matsakaicin Turai (37 mg/kg) ya nuna cewa jimlar ma'aunin lissafi na yankin binciken yana cikin kewayon da za a iya jurewa. Duk da haka, kamar yadda Kabata-Pendias11 ya nuna, kwatanta matsakaicin yawan nickel (Ni) a cikin binciken da ake yi yanzu da ƙasar noma a Sweden ya nuna cewa matsakaicin yawan nickel na yanzu ya fi girma. Haka kuma, matsakaicin yawan Frydek Mistek a cikin ƙasa ta birni da ta birni a cikin binciken da ake yi yanzu (Ni 16.15 mg/kg) ya fi wanda aka yarda da shi. iyaka ta 60 (10.2 mg/kg) don Ni a cikin ƙasan birane na Poland da Różański et al suka ruwaito. Bugu da ƙari, Bretzel da Calderisi61 sun sami ƙarancin matsakaicin yawan Ni (1.78 mg/kg) a cikin ƙasan birane a Tuscany idan aka kwatanta da binciken da ake yi a yanzu. Jim62 sun kuma sami ƙarancin yawan nickel (12.34 mg/kg) a cikin ƙasan birane na Hong Kong, wanda ya yi ƙasa da yawan nickel na yanzu a cikin wannan binciken. Birke et al63 sun ba da rahoton matsakaicin yawan Ni na 17.6 mg/kg a cikin wani tsohon yanki na hakar ma'adinai da masana'antar birane a Saxony-Anhalt, Jamus, wanda ya fi 1.45 mg/kg girma fiye da matsakaicin yawan Ni a yankin (16.15 mg/kg). Binciken da ake yi a yanzu. Yawan sinadarin nickel a cikin ƙasa a wasu yankunan birane da kewaye na yankin binciken ana iya danganta shi da masana'antar ƙarfe da ƙarfe da masana'antar ƙarfe. Wannan ya yi daidai da binciken da Khodoust et al. 64 cewa masana'antar ƙarfe da aikin ƙarfe sune manyan tushen gurɓatar nickel a cikin ƙasa. Duk da haka, masu hasashen sun kuma kasance daga 538.70 mg/kg zuwa 69,161.80 mg/kg ga Ca, 497.51 mg/kg zuwa 3535.68 mg/kg ga K, da kuma 685.68 mg/kg zuwa 5970.05 mg/kg ga Mg.Jakovljevic et al. 65 sun binciki jimillar adadin Mg da K na ƙasa a tsakiyar Serbia. Sun gano cewa jimillar yawan (410 mg/kg da 400 mg/kg, bi da bi) ya yi ƙasa da yawan Mg da K na binciken da ake yi a yanzu. Ba za a iya bambancewa ba, a gabashin Poland, Orzechowski da Smolczynski66 sun kimanta jimillar adadin Ca, Mg da K kuma sun nuna matsakaicin yawan Ca (1100 mg/kg), Mg (590 mg/kg) da K (810 mg/kg). Abubuwan da ke cikin ƙasa a saman ƙasa sun yi ƙasa da kashi ɗaya cikin uku a cikin wannan binciken. Wani bincike na baya-bayan nan da Pongrac et al. 67 suka yi ya nuna cewa jimillar adadin Ca da aka yi nazari a cikin ƙasa 3 daban-daban a Scotland, UK (ƙasar Mylnefield, ƙasa Balruddery da ƙasan Hartwood) sun nuna mafi girman adadin Ca a cikin wannan binciken.
Saboda bambancin yawan abubuwan da aka auna, rarrabawar bayanai na abubuwan suna nuna karkacewa daban-daban. Karkacewa da kurtosis na abubuwan sun kasance daga 1.53 zuwa 7.24 da 2.49 zuwa 54.16, bi da bi. Duk abubuwan da aka lissafa suna da karkacewa da matakan kurtosis sama da +1, don haka yana nuna cewa rarrabawar bayanai ba ta da tsari, an karkace ta daidai kuma ta kai kololuwa. Kiyasin CV na abubuwan kuma sun nuna cewa K, Mg, da Ni suna nuna matsakaicin bambanci, yayin da Ca yana da babban bambanci. CV na K, Ni da Mg sun bayyana rarrabawarsu iri ɗaya. Bugu da ƙari, rarraba Ca ba iri ɗaya ba ne kuma tushen waje na iya shafar matakin wadatarsa.
Daidaiton masu canjin hasashen da abubuwan amsawa ya nuna alaƙa mai gamsarwa tsakanin abubuwan (duba Hoto na 3). Daidaiton ya nuna cewa CaK ya nuna alaƙar matsakaici tare da ƙimar r = 0.53, kamar yadda CaNi ya yi. Kodayake Ca da K suna nuna alaƙa mai sauƙi da juna, masu bincike kamar Kingston et al. 68 da Santo69 sun nuna cewa matakan su a cikin ƙasa suna da daidaiton daidaito. Duk da haka, Ca da Mg suna adawa da K, amma CaK suna da alaƙa mai kyau. Wannan yana iya faruwa ne saboda amfani da takin zamani kamar potassium carbonate, wanda ya fi 56% girma a cikin potassium. Potassium yana da alaƙa mai kyau da magnesium (KM r = 0.63). A cikin masana'antar takin zamani, waɗannan abubuwa biyu suna da alaƙa mai kyau saboda ana amfani da potassium magnesium sulfate, potassium magnesium nitrate, da potassium a cikin ƙasa don ƙara matakan ƙarancin su. Nickel yana da alaƙa mai kyau da Ca, K da Mg tare da ƙimar r = 0.52, 0.63 da 0.55, bi da bi. Alaƙar da ke tattare da calcium, magnesium, da PTEs kamar nickel suna da rikitarwa, amma duk da haka, magnesium yana hana shan calcium, calcium yana rage tasirin magnesium mai yawa, kuma magnesium da calcium suna rage tasirin guba na nickel a cikin ƙasa.
Ma'aunin daidaitawa ga abubuwan da ke nuna alaƙar da ke tsakanin masu hasashen da martani (Lura: wannan adadi ya haɗa da taswirar warwatse tsakanin abubuwa, matakan mahimmanci sun dogara ne akan p < 0,001).
Hoto na 4 yana nuna rarrabawar abubuwa a sarari. A cewar Burgos et al70, amfani da rarrabawar sarari wata dabara ce da ake amfani da ita don ƙididdigewa da kuma haskaka wuraren zafi a wuraren da aka gurbata. Matakan wadatarwa na Ca a Hoto na 4 ana iya gani a ɓangaren arewa maso yamma na taswirar rarraba sarari. Hoton yana nuna wuraren wadatarwa na Ca matsakaici zuwa babban. Ingantawar calcium a arewa maso yamma na taswirar wataƙila saboda amfani da quicklime (calcium oxide) don rage yawan acidity na ƙasa da kuma amfani da shi a cikin injinan ƙarfe a matsayin iskar alkaline a cikin tsarin yin ƙarfe. A gefe guda kuma, wasu manoma sun fi son amfani da calcium hydroxide a cikin ƙasa mai acidic don rage pH, wanda kuma yana ƙara yawan sinadarin calcium na ƙasa71. Potassium kuma yana nuna wuraren zafi a arewa maso yamma da gabashin taswirar. Arewa maso Yamma babbar al'umma ce ta noma, kuma yanayin potassium matsakaici zuwa babba na iya zama saboda aikace-aikacen NPK da potash. Wannan ya yi daidai da sauran bincike, kamar Madaras da Lipavský72, Madaras et al.73, Pulkrabová et al.74, Asare et al.75, waɗanda suka lura cewa Daidaita ƙasa da kuma magance ta da KCl da NPK ya haifar da yawan sinadarin K a cikin ƙasa. Ƙara yawan sinadarin Potassium a arewa maso yammacin taswirar rarrabawa na iya faruwa ne saboda amfani da takin zamani da aka yi da potassium kamar potassium chloride, potassium sulfate, potassium nitrate, potash, da potassium don ƙara yawan sinadarin potassium a cikin ƙasa mara kyau. Zádorová et al. 76 da Tlustoš et al. 77 ya bayyana cewa amfani da takin K ya ƙara yawan sinadarin K a cikin ƙasa kuma zai ƙara yawan sinadarin gina jiki a cikin ƙasa a cikin dogon lokaci, musamman K da Mg suna nuna wuri mai zafi a cikin ƙasa. Matsakaici wurare masu zafi a arewa maso yammacin taswirar da kudu maso gabashin taswirar. Daidaitawar colloidal a cikin ƙasa yana rage yawan sinadarin magnesium a cikin ƙasa. Rashinsa a cikin ƙasa yana sa tsire-tsire su nuna launin rawaya na chlorosis. Takin da ke tushen Magnesium, kamar potassium magnesium sulfate, magnesium sulfate, da Kieserite, suna magance ƙarancin (shuke-shuke suna kama da shunayya, ja, ko launin ruwan kasa, suna nuna ƙarancin magnesium) a cikin ƙasa mai matsakaicin pH6. Tarin nickel a saman ƙasa na birni da na birni na iya zama saboda ayyukan ɗan adam kamar noma da mahimmancin nickel a cikin samar da ƙarfe na bakin ƙarfe78.
Rarraba sarari na abubuwa [an ƙirƙiri taswirar rarraba sarari ta amfani da ArcGIS Desktop (ESRI, Inc, Sigar 10.7, URL: https://desktop.arcgis.com).]
An nuna sakamakon ma'aunin aikin samfurin abubuwan da aka yi amfani da su a cikin wannan binciken a cikin Jadawali na 2. A gefe guda kuma, RMSE da MAE na Ni duka suna kusa da sifili (0.86 RMSE, -0.08 MAE). A gefe guda kuma, ƙimar RMSE da MAE na K an yarda da su. Sakamakon RMSE da MAE sun fi girma ga calcium da magnesium. Sakamakon Ca da K MAE da RMSE sun fi girma saboda bayanai daban-daban. An gano cewa RMSE da MAE na wannan binciken ta amfani da EBK don annabta Ni sun fi sakamakon John et al. 54 ta amfani da kriging na synergistic don annabta yawan S a cikin ƙasa ta amfani da bayanan da aka tattara iri ɗaya. Sakamakon EBK da muka yi nazari ya 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 da aka yi amfani da su wajen hasashen abubuwan da ke cikin nickel a cikin ƙasan birane da kuma yankunan birane ta amfani da aikin samfuran (Tebur 3). Tabbatar da ingancin samfuri da kimantawa daidai sun tabbatar da cewa an haɗa Ca_Mg_K da samfurin EBK SVMR sun samar da mafi kyawun aiki. Tsarin daidaitawa Ca_Mg_K-EBK_SVMR samfurin R2, kuskuren tushen matsakaicin murabba'i (RMSE) da matsakaicin kuskuren cikakke (MAE) sun kasance 0.637 (R2), 95.479 mg/kg (RMSE) da 77.368 mg/kg (MAE) Ca_Mg_K-SVMR ya kasance 0.663 (R2), 235.974 mg/kg (RMSE) da 166.946 mg/kg (MAE). Duk da haka, an sami kyawawan ƙimar R2 don Ca_Mg_K-SVMR (0.663 mg/kg R2) da Ca_Mg-EBK_SVMR (0.643 =) R2); sakamakon RMSE da MAE ɗinsu ya fi na Ca_Mg_K-EBK_SVMR (R2 0.637) (duba Tebur 3). Bugu da ƙari, RMSE da MAE na samfurin Ca_Mg-EBK_SVMR (RMSE = 1664.64 da MAE = 1031.49) sune 17.5 da 13.4, bi da bi, waɗanda suka fi na Ca_Mg_K-EBK_SVMR girma. Haka kuma, RMSE da MAE na samfurin Ca_Mg-K SVMR (RMSE = 235.974 da MAE = 166.946) sun fi na Ca_Mg_K-EBK_SVMR RMSE da MAE girma da 2.5 da 2.2, bi da bi. Sakamakon RMSE da aka ƙididdige ya nuna yadda saitin bayanai ya kasance tare da layin mafi dacewa. An lura da mafi girman RSME da MAE. Bisa ga Kebonye da abokan aikinsa 46 da john da abokan aikinsa 54, kusantar RMSE da MAE zuwa sifili, zai fi kyau sakamakon ya fi kyau.SVMR da EBK_SVMR suna da ƙimar RSME da MAE mafi girma. An lura cewa kiyasin RSME sun fi ƙimar MAE yawa, suna nuna kasancewar waɗanda ba su da alaƙa. A cewar Legates da McCabe83, ana ba da shawarar yadda RMSE ya wuce matsakaicin kuskuren cikakken (MAE) a matsayin alamar kasancewar waɗanda ba su da alaƙa. Wannan yana nufin cewa yawan bayanai daban-daban, mafi girman ƙimar MAE da RMSE. Daidaiton kimantawa tsakanin-inganci na samfurin Ca_Mg_K-EBK_SVMR gauraye don annabta abubuwan da ke cikin Ni a cikin ƙasa na birane da na birni shine 63.70%. A cewar Li da abokan aikinsa 59, wannan matakin daidaito ƙimar aikin samfurin ne mai karɓuwa. An kwatanta sakamakon yanzu da wani bincike da Tarasov da abokan aikinsa suka yi a baya. 36 wanda samfurinsa na haɗaka ya ƙirƙiri MLPRK (Multilayer Perceptron Residual Kriging), wanda ya shafi ma'aunin kimanta daidaito na EBK_SVMR da aka ruwaito a cikin binciken na yanzu, RMSE (210) da The MAE (167.5) ya fi sakamakonmu a cikin binciken na yanzu (RMSE 95.479, MAE 77.368). Duk da haka, lokacin da aka kwatanta R2 na binciken na yanzu (0.637) da na Tarasov et al. 36 (0.544), a bayyane yake cewa ma'aunin tantancewa (R2) ya fi girma a cikin wannan samfurin gauraye. Gefen kuskure (RMSE da MAE) (EBK SVMR) na samfurin gauraye ya ninka sau biyu ƙasa. Haka nan, Sergeev et al.34 sun rubuta 0.28 (R2) don samfurin gauraye da aka haɓaka (Multilayer Perceptron Residual Kriging), yayin da Ni a cikin binciken na yanzu ya rubuta 0.637 (R2). Matsayin daidaiton hasashen wannan samfurin (EBK SVMR) shine 63.7%, yayin da daidaiton hasashen da Sergeev et al. 34 suka samu shine 28%. Taswirar ƙarshe (Hoto na 5) da aka ƙirƙira ta amfani da samfurin EBK_SVMR da Ca_Mg_K a matsayin mai hasashen yana nuna hasashen wuraren zafi da matsakaici zuwa nickel a kan dukkan yankin binciken. Wannan yana nufin cewa yawan nickel a yankin binciken galibi matsakaici ne, tare da yawan taro a wasu takamaiman yankuna.
Taswirar hasashen ƙarshe an wakilta ta amfani da samfurin EBK_SVMR mai haɗaka kuma ana amfani da Ca_Mg_K a matsayin mai hasashen.[An ƙirƙiri taswirar rarraba sarari ta amfani da RStudio (sigar 1.4.1717: https://www.rstudio.com/).]
An gabatar a Hoto na 6 cewa yawan PTE a matsayin tsarin hadewa wanda ya kunshi kwayoyin halitta daban-daban. Babu daya daga cikin jiragen da aka zana da ya nuna irin tsarin launi kamar yadda aka nuna. Duk da haka, adadin kwayoyin halitta da ya dace a kowace taswirar da aka zana shine 55. Ana samar da SeOM ta amfani da launuka daban-daban, kuma mafi kama da tsarin launi, haka nan halayen samfuran suka fi kama. Dangane da ma'aunin launi nasu, abubuwan da ke cikinsa (Ca, K, da Mg) sun nuna nau'ikan launi iri ɗaya ga manyan kwayoyin halitta guda ɗaya da mafi yawan ƙananan ƙwayoyin halitta. Don haka, CaK da CaMg suna da wasu kamanceceniya da ƙananan ƙwayoyin halitta masu tsari da kuma ƙananan launuka masu launi. Dukansu samfuran suna annabta yawan Ni a cikin ƙasa ta hanyar nuna launuka masu matsakaici zuwa manyan launuka kamar ja, lemu da rawaya. Samfurin KMg yana nuna nau'ikan launuka masu yawa bisa ga daidaiton rabo da ƙananan zuwa matsakaicin faci. A kan madaidaicin sikelin launi daga ƙasa zuwa sama, tsarin rarraba planar na abubuwan da ke cikin samfurin ya nuna babban tsarin launi wanda ke nuna yuwuwar yawan nickel a cikin ƙasa (duba Hoto na 4). Tsarin kayan samfurin CakMg yana nuna nau'in launi daban-daban daga ƙasa zuwa sama bisa ga daidaiton launi. sikelin. Bugu da ƙari, hasashen samfurin game da abun ciki na nickel (CakMg) yayi kama da rarrabawar sarari na nickel da aka nuna a Hoto na 5. Duk zane-zanen suna nuna babban, matsakaici da ƙarancin yawan nickel a cikin ƙasan birni da na birni. Hoto na 7 yana nuna hanyar daidaitawa a cikin rukunin k-ma'ana akan taswira, an raba shi zuwa ƙungiyoyi uku bisa ga ƙimar da aka annabta a cikin kowane samfuri. Hanyar daidaitawa tana wakiltar mafi kyawun adadin gungu. Daga cikin samfuran ƙasa 115 da aka tattara, rukuni na 1 ya sami mafi yawan samfuran ƙasa, 74. Rukunin 2 ya sami samfura 33, yayin da rukuni na 3 ya sami samfura 8. An sauƙaƙe haɗin hasashen planar mai sassa bakwai don ba da damar fassarar gungu daidai. Saboda yawancin hanyoyin da ke shafar halittar ɗan adam da na halitta, yana da wuya a sami bambance-bambancen tsarin gungu yadda ya kamata a cikin taswirar SeOM da aka rarraba78.
Fitowar jirgin sama na sassa ta kowace na'urar Empirical Bayesian Kriging Support Vector Machine (EBK_SVM_SeOM) mai canzawa. [An ƙirƙiri taswirar SeOM ta amfani da RStudio (sigar 1.4.1717: https://www.rstudio.com/).]
Abubuwan rarrabuwa daban-daban na rukuni [An ƙirƙiri taswirar SeOM ta amfani da RStudio (sigar 1.4.1717: https://www.rstudio.com/).]
Binciken da ake yi yanzu ya nuna dabarun yin ƙira a sarari don yawan nickel a cikin ƙasan birane da kuma na birane. Binciken ya gwada dabarun yin ƙira daban-daban, yana haɗa abubuwa da dabarun yin ƙira, don samun hanya mafi kyau don yin hasashen yawan nickel a cikin ƙasa. Siffofin sararin samaniya na SeOM na dabarar yin ƙira sun nuna babban tsarin launi daga ƙasa zuwa sama akan madaidaicin sikelin launi, yana nuna yawan Ni a cikin ƙasa. Duk da haka, taswirar rarraba sarari ta tabbatar da rarraba sararin samaniya na abubuwan da EBK_SVMR ya nuna (duba Hoto na 5). Sakamakon ya nuna cewa samfurin regression na injin tallafi (Ca Mg K-SVMR) yana annabta yawan Ni a cikin ƙasa a matsayin samfuri ɗaya, amma sigogin tantancewa da daidaito suna nuna kurakurai masu yawa dangane da RMSE da MAE. A gefe guda kuma, dabarar yin ƙira da aka yi amfani da ita tare da samfurin EBK_MLR ita ma tana da matsala saboda ƙarancin ƙimar ma'aunin ƙuduri (R2). An sami sakamako mai kyau ta amfani da EBK SVMR da abubuwan haɗin gwiwa (CaKMg) tare da ƙananan kurakuran RMSE da MAE tare da daidaito na 63.7%. Ya bayyana cewa haɗa EBK algorithm tare da algorithm na koyon injin zai iya samar da algorithm na haɗin gwiwa wanda zai iya hasashen yawan PTEs a cikin ƙasa. Sakamakon ya nuna cewa amfani da Ca Mg K a matsayin masu hasashen don hasashen yawan Ni a yankin binciken na iya inganta hasashen Ni a cikin ƙasa. Wannan yana nufin cewa ci gaba da amfani da takin zamani da aka yi da nickel da gurɓatar masana'antu na ƙasa ta masana'antar ƙarfe yana da halin ƙara yawan nickel a cikin ƙasa. Wannan binciken ya nuna cewa samfurin EBK zai iya rage matakin kuskure da inganta daidaiton samfurin rarraba sararin ƙasa a cikin ƙasa na birane ko na birane. Gabaɗaya, muna ba da shawarar amfani da samfurin EBK-SVMR don tantancewa da hasashen PTE a cikin ƙasa; ban da haka, muna ba da shawarar amfani da EBK don haɗakar da nau'ikan algorithms na koyon injina daban-daban. An annabta yawan Ni ta amfani da abubuwa a matsayin covariates; duk da haka, amfani da ƙarin covariates zai inganta aikin samfurin sosai, wanda za'a iya ɗaukarsa a matsayin iyakance aikin da ake yi a yanzu. Wani iyakancewar wannan binciken shine cewa adadin bayanai shine 115. Saboda haka, idan aka samar da ƙarin bayanai, ana iya inganta aikin hanyar haɗakar da aka tsara.
PlantProbs.net.Nickel a cikin Tsire-tsire da Ƙasa https://plantprobs.net/plant/nutrientImbalances/sodium.html (An shiga 28 Afrilu 2021).
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