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He pilikia nui ka pollution lepo ma muli o nā hana kanaka. He ʻokoʻa ka mahele ākea o nā mea ʻona (PTEs) ma ka hapa nui o nā kūlanakauhale a me nā wahi o ke kūlanakauhale. Hoʻoholo ʻia me ka hoʻohana ʻana i ka spectrometry emission plasma inductively. ʻO ka mea hoʻololi pane ʻo Ni a ʻo nā mea wānana ʻo Ca, Mg, a me K. ʻO ka matrix correlation ma waena o ka pane pane a me ka variable predictor e hōʻike ana i kahi pilina maikaʻi ma waena o nā mea. (166.946 mg/kg) ua kiʻekiʻe ma mua o nā ʻano hana ʻē aʻe i hoʻohana ʻia. ʻO nā hiʻohiʻona hui ʻia no Empirical Bayesian Kriging-Multiple Linear Regression (EBK-MLR) hana maikaʻi ʻole, e like me ka hōʻike ʻia e nā coefficients o ka hoʻoholo ʻana ma lalo o 0.1. ʻO ka Empirical Bayesian Kriging-Support Vector Machine Regression (EBK99) haʻahaʻa (EBK-9SV maikaʻi loa). mg/kg) a me MAE (77.368 mg/kg) waiwai a me ka helu kiʻekiʻe o ka hoʻoholo (R2 = 0.637). ʻIke ʻia ka EBK-SVMR kumu hoʻohālike e hoʻohana ana i ka palapala ʻāina hoʻonohonoho ponoʻī. a ʻo SVMR kahi ʻano hana kūpono no ka wānana ʻana i ka nui o Ni ma nā ʻāina kūlanakauhale a me nā ʻāina āpau.
Manaʻo ʻia ʻo Nickel (Ni) he micronutrient no nā mea kanu no ka mea kōkua ia i ka hoʻopaʻa ʻana o ka naikokene lewa (N) a me ka metabolism urea, pono ia mau mea ʻelua no ka ulu ʻana o nā hua. nickel-based fertilizers to optimize nitrogen fixation2.Continued application of nickel-based fertilizers to enrich the soil and increase the ability of legumes to fix nitrogen in the soil hoʻonui mau i ka nickel concentration in the soil.Although nickel is micronutrient for plants, its excessive intake in the soil can do more harm of good.The hinickel i toxicity o ka lepo. ma ke ʻano he meaʻai nui no ka ulu ʻana o ka mea kanu1. Wahi a Liu3, ua ʻike ʻia ʻo Ni ka 17th mea nui e pono ai no ka ulu ʻana a me ka ulu ʻana o ka mea kanu. 'Āpana. Eia kekahi, ua hoʻohana nui ʻia nā nickel-based alloys a me nā ʻatikala electroplated i ka lumi kuke, ballroom accessories, meaʻai ʻoihana lako, uila, uea a me ke kaula, jet turbines, surgical implants, textiles, a me ka hana moku5.Ni-waiwai pae i loko o ka lepo (ʻo ia hoʻi, nā lepo ili) ua hoʻoili ʻia i nā anthropogenic a me nā kumu kumu maoli, akā ʻo ka anthropogenic a me nā kumu kumu maoli, akā ʻo ka anthropogenic a me nā kumu kumu maoli, akā ʻo ka anthropogenic a me nā kumu kumu maoli. ʻO ka nickel nā pele pele, nā mea kanu, ke ahi o ka nahele, a me nā kaʻina hana honua; nae, anthropogenic kumu i loko o ka nickel / cadmium pākuʻi i loko o ka oihana kila, electroplating, arc kuʻihao, diesel a me ka wahie aila, a me ka lewa emissions mai ka lanahu kuni a me ka lepo a me ka sludge incineration Nickel accumulation7,8. Wahi a Freedman a me Hutchinson9 a me Manyiwa et al. 10, ʻo nā kumu nui o ka pollution topsoil i loko o ke kaiapuni kokoke a pili i ka nickel-copper-based smelters a me nā mines. ʻO ka ʻāina kiʻekiʻe a puni ka Sudbury nickel-copper refinery ma Kanada ka kiʻekiʻe kiʻekiʻe o ka nickel contamination ma 26,000 mg / kg11. lepo11. Wahi a Alms et al. 12, ka nui o ka HNO3-extractable nikela i loko o ka 'āina o ka luna o ka 'āina arable (nickel hana ma Rusia) mai 6.25 a hiki i 136.88 mg / kg, e pili ana i ka mean o 30.43 mg / kg a me ka baseline kuʻina o 25 mg / kg.According i kabata 11, ka palapala noi o ka fertilizer soilsurban phosphorus. Hiki ke hoʻokomo a hoʻohaumia paha ka lepo i ka lepo i ka wā o ka wā kanu. ʻO ka hopena o ka nickel i loko o ke kanaka hiki ke alakaʻi i ka maʻi kanesa ma o ka mutagenesis, ka pōʻino chromosomal, Z-DNA generation, blocked DNA excision repair, or epigenetic process13.
Ua ulu aʻe nā loiloi hoʻohaumia lepo i kēia mau manawa ma muli o ka nui o nā pilikia e pili ana i ke olakino e kū mai ana mai ka pilina lepo-mea kanu, ka lepo a me ka pilina olaola lepo, ka hoʻohaʻahaʻa ʻana i ka kaiaola, a me ka loiloi hopena o ke kaiapuni. ʻO ka palapala ʻāina wānana (PSM). Wahi a Minasny lāua ʻo McBratney16, ua hōʻike ʻia ka palapala ʻāina wānana (DSM) he subdiscipline koʻikoʻi o ka ʻepekema lepo. pūnaewele".McBratney et al. 17 ka wehewehe ʻana ʻo ka DSM a i ʻole PSM o kēia wā ʻo ia ka ʻenehana maikaʻi loa no ka wānana a i ʻole ka palapala ʻāina i ka māhele ākea o nā PTE, nā ʻano lepo a me nā waiwai lepo. ʻO Geostatistics a me Machine Learning Algorithms (MLA) nā ʻano hana hoʻohālike DSM e hana ana i nā palapala ʻāina i helu ʻia me ke kōkua o nā kamepiula e hoʻohana ana i ka ʻikepili koʻikoʻi a liʻiliʻi.
Ua wehewehe ʻo Deutsch18 a me Olea19 i ka geostatistics "ʻo ka hōʻiliʻili o nā ʻenehana helu e pili ana i ka hōʻike ʻana i nā ʻano kikoʻī, e hoʻohana nui ana i nā hiʻohiʻona stochastic, e like me ke ʻano o ka hōʻike ʻana i ka ʻikepili manawa." ʻO ka mea nui, pili ka geostatistics i ka loiloi o nā variograms, e ʻae ai i ka helu ʻana a wehewehe i nā hilinaʻi o nā waiwai spatial mai kēlā me kēia dataset20.Gumiaux et al. 20 hōʻike hou aʻe i ka loiloi o variograms ma geostatistics ma luna o ekolu kumu, me (a) helu ana i ka unahi o ka ikepili correlation, (b) ike a me ka helu anisotropy i ka dataset disparity a me (c) ma waho aʻe o ka lawe 'ana i ka inherent hewa o ke ana ikepili i hookaawaleia mai ka local effects, the areaBuilding effects are also used on these effects. geostatistics, me ka kriging general, co-kriging, ordinary kriging, empirical Bayesian kriging, simple kriging method and other well-known interpolation techniques to map or predict PTE, soil soil, and soil type.
Machine Learning Algorithms (MLA) he ʻano hana hou e hoʻohana ana i nā papa ʻikepili nui ʻole linear, hoʻohana ʻia e nā algorithms i hoʻohana mua ʻia no ka ʻimi ʻana i ka ʻikepili, ka ʻike ʻana i nā ʻano o ka ʻikepili, a hoʻohana pinepine ʻia i ka hoʻokaʻawale ʻana i nā kula ʻepekema e like me ka ʻepekema lepo a me nā hana hoʻihoʻi. 22 (nā ululāʻau maʻamau no ka manaʻo metala kaumaha ma nā ʻāina mahiʻai), Sakizadeh et al. 23 (hoʻohālike me ka hoʻohana ʻana i nā mīkini vector kākoʻo a me nā pūnaewele neural artificial) haumia lepo ). Eia kekahi, ʻo Vega et al. 24 (CART no ka hoʻohālike ʻana i ka paʻa metala kaumaha a me ka adsorption i ka lepo) Sun et al. 25 (ʻo ka hoʻohana ʻana i ka cubist ka māhele ʻana o Cd i loko o ka lepo) a me nā algorithm ʻē aʻe e like me k-kokoke loa, hoʻonui i ka hoʻihoʻi hou ʻana, a me ka hoʻoulu hou ʻana Ua hoʻohana pū ʻia nā lāʻau iā MLA e wānana i ka PTE i ka lepo.
ʻO ka hoʻohanaʻana i nā algorithms DSM i ka wānana a iʻole ka palapala'āina e kū ana i nā pilikia. Manaʻo nā mea kākau heʻoi aku ka maikaʻi o ka MLA ma mua o ka geostatistics a me ka hope. Manaʻo ʻo Pontius a me Cheuk28 a me Grunwald29 e pili ana i nā hemahema a me kekahi mau hewa i ka wānana ʻana i ka palapala ʻāina. 15 e wehewehe pono e hoʻopaʻa kūʻokoʻa ʻia ka ʻano hōʻoia a me ka maopopo ʻole i hoʻokomo ʻia e ka hana ʻana i ka palapala ʻāina a me ka wānana i mea e hoʻomaikaʻi ai i ka maikaʻi o ka palapala ʻāina. akā naʻe, hiki mai ka hemahema o ka maopopo i ka DSM mai nā kumu he nui o ka hewa, ʻo ia hoʻi ka hewa covariate, ka hewa kumu hoʻohālike, ka hewa wahi, a me ka analytical Error 31. ʻO ka hewa o ka hoʻohālike ʻana i hoʻokomo ʻia i ka MLA a me nā kaʻina geostatistical e pili ana me ka ʻike ʻole, ma hope o ke alakaʻi ʻana i ka oversimplification o ke kaʻina maoli32. No ke ʻano o ke ʻano o ka hoʻohālikelike ʻana i ke ʻano hoʻohālike, hiki ke hoʻohālikelike ʻia i ke ʻano o ka hoʻohālikelike ʻana. wānana, a i ʻole interpolation33. I kēia mau lā, ua puka mai kahi ʻano DSM hou e paipai ana i ka hoʻohui ʻana o nā geostatistics a me MLA i ka palapala ʻāina a me ka wānana. 34; Subbotina et al. 35; Tarasov et al. 36 a me Tarasov et al. Ua hoʻohana ʻo 37 i ka maikaʻi kūpono o ka geostatistics a me ke aʻo ʻana i ka mīkini e hana i nā hiʻohiʻona hybrid e hoʻomaikaʻi i ka pono o ka wānana a me ka palapala ʻāina. maikaʻi. ʻO kekahi o kēia mau hiʻohiʻona algorithm hybrid a i hui pū ʻia ʻo 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 a me ka Couperation Regression Co.
Wahi a Sergeev et al., ʻo ka hui pū ʻana i nā ʻano hana hoʻohālike like ʻole e hiki ke hoʻopau i nā hemahema a hoʻonui i ka pono o ka hopena hybrid ma mua o ka hoʻomohala ʻana i kāna kumu hoʻohālike hoʻokahi. me ke kākoʻo Vector Machine (SVM) a me Multiple Linear Regression (MLR). ʻae ʻia ka hoʻololi ākea39.Ua hoʻohana ʻia ʻo EBK i nā haʻawina like ʻole, me ka nānā ʻana i ka hāʻawi ʻana o ke kalapona organik i nā ʻāina mahiʻai40, ka loiloi ʻana i ka pollution lepo41 a me ka palapala ʻana i nā waiwai lepo42.
Ma kekahi ʻaoʻao, ʻo Self-Organizing Graph (SeOM) kahi algorithm aʻo i hoʻohana ʻia ma nā ʻatikala like ʻole e like me Li et al. 43, Wang et al. 44, Hossain Bhuiyan et al. 45 a me Kebonye et al.46 E hoʻoholo i nā ʻano kikoʻī a me ka hui pū ʻana o nā mea.Wang et al. 44 outline ʻo SeOM he ʻenehana hoʻonaʻauao ikaika i ʻike ʻia no kona hiki ke hui a noʻonoʻo i nā pilikia laina ʻole. 44, hiki iā SeOM ke hoʻohui i ka mahele o nā neurons pili a hāʻawi i ka ʻike ʻike kiʻekiʻe.
Ke manaʻo nei kēia pepa e hana i kahi kŘkohu palapala ʻāina ikaika me ka pololei loa no ka wānana ʻana i ka ʻike nickel ma nā ʻāina kaona a me nā ʻāina āpau. Manaʻo mākou e hilinaʻi nui ʻia ka hilinaʻi o ke kumu hoʻohālike i ka mana o nā kumu hoʻohālike ʻē aʻe i hoʻopili ʻia i ke kumu hoʻohālike. hoʻonui; No laila, e ho'āʻo mākou e pane i nā nīnau noiʻi e hāʻawi mai i nā ʻano like ʻole. Akā naʻe, pehea ka pololei o ke kumu hoʻohālike i ka wānana ʻana i ka mea i manaʻo ʻia? Eia kekahi, he aha ke kiʻekiʻe o ka loiloi maikaʻi ma muli o ka hōʻoia a me ka loiloi pololei? ka hoʻopaʻa ʻana i nā ʻāina kūlanakauhale a i ʻole ke kaona, a (d) ka noi ʻana o SeOM e hana i kahi palapala ʻāina hoʻonā kiʻekiʻe o ka hoʻololi ʻana i ka nickel spatial variation.
Ke hanaʻia nei ke aʻoʻana ma Czech Republic, ma ka mokuʻo Frydek Mistek ma ka'āpana Moravia-Silesian (e nānā i ke kiʻi 1). ʻO ka palapala honua o ka wahi aʻoʻana heʻeleʻele loa a he hapa nui ia o ka'āpana Moravia-Silesian Beskidy,ʻo ia kekahi hapa o ka palena waho o nā mauna Carpathian. Aia ka wahi aʻo ma waena o 49 ′ 0 ′ ′ ′ ′ ′ ′ ′ 4 ′ ′ ′ ′ ′ ′ ′. E, a aia ke kiʻekiʻe ma waena o 225 a me 327 m; Eia naʻe, ua helu ʻia ka ʻōnaehana hoʻohālikelike Koppen no ke kūlana climatic o ka ʻāina ʻo Cfb = temperate oceanic climate, He nui ka ua i loko o nā mahina maloʻo. Ua ʻano liʻiliʻi nā mahana ma waena o ka makahiki ma waena o −5 °C a me 24 °C, ʻaʻole hiki ke hāʻule ma lalo o −14 °C a i ʻole ma luna o 30 °C, ʻoiai ʻo ka awelika o ka makahiki he 5285 mm. 'āpana o ka wahi holoʻokoʻa he 1,208 square kilometres, me 39.38% o ka ʻāina mahiʻai a me 49.36% o ka uhi ʻana o ka ululāʻau. Ma kekahi ʻaoʻao, ʻo ka wahi i hoʻohana ʻia i kēia haʻawina ma kahi o 889.8 square kilomika. Ma a puni ʻo Ostrava, ʻo ka ʻoihana kila a me nā hana metala he ikaika loa. a me nā kila kila (e hoʻonui ka nickel i ka ikaika o ka huila me ka mālama ʻana i kona ductility maikaʻi a me ka paʻakikī), a me ka mahiʻai ikaika e like me ka noi phosphate fertilizer a me ka hana holoholona ʻo ia nā kumu noiʻi kūpono o ka nickel ma ka ʻāina (e like me ka hoʻohui ʻana i ka nickel i nā keiki hipa e hoʻonui i ka ulu ʻana o nā hipa a me nā pipi hānai haʻahaʻa). nickel plating kaʻina. He mea maʻalahi ka ʻike ʻana o ka lepo mai ka waihoʻoluʻu o ka lepo, ka hale, a me ka carbonate content. ʻO ke ʻano o ka lepo ma waena o ka maikaʻi, i loaʻa mai ka makua. ʻO nā kiʻekiʻe mai 455.1 a 493.5 m, ʻo nā cambisol ka mea i lanakila ma ka Czech Republic49.
Palapala ʻāina aʻo [Ua hana ʻia ka palapala ʻāina aʻo me ka ArcGIS Desktop (ESRI, Inc, version 10.7, URL: https://desktop.arcgis.com).]
Ua loaʻa mai he 115 mau laʻana topsoil mai ke kūlanakauhale a me nā ʻāina āpau ma ka moku ʻo Frydek Mistek. ʻO ke kumu hoʻohālike i hoʻohana ʻia he grid maʻamau me nā laʻana lepo i hoʻokaʻawale ʻia 2 × 2 km kaʻawale, a ua ana ʻia ka topsoil ma kahi hohonu o 0 a 20 cm me ka hoʻohana ʻana i kahi mea GPS paʻa lima (Leica Zeno 5 GPS). laboratory.The samples were air-dry to produce pulverized samples, pulverized by a mechanical system (Fritsch disc mill), and sieved (sieve size 2 mm) Place 1 gram of dryed, homogenized and sieved soil samples in clear labeled teflon bottles.In kēlā me kēia moku Teflon, e hoʻokaʻawale i 7 ml o 35% ml NO3 dispense (35% ml HC6) maʻamau. hoʻokahi no kēlā me kēiaʻakika), uhi māmā aʻae i nā laʻana e kū i ka pō no ka hopena (aqua regia program). supernatant i loko o ka 50 ml PVC paipu me ka wai deionized. Eia kekahi, 1 ml o ka dilution solution ua hoʻoheheʻeʻia me 9 ml o ka wai deionized a kānana i loko o ka 12 ml paipu i hoʻomākaukauʻia no ka PTE pseudo-concentration. ʻO nā manaʻo o nā PTE (As, Cd, Cr, Cu, Mn, Ni, Pb, Zn, Caduct, Mg, ICP-O) Hoʻopiliʻia ka Plasma Optical Emission Spectroscopy) (Thermo Fisher Scientific, USA) e like me nāʻano maʻamau a me kaʻaelike.Ensure Quality Assurance and Control (QA/QC) procedures (SRM NIST 2711a Montana II Soil).PTEs me nā palenaʻike ma lalo o ka hapalua ua kāpaeʻia mai kēia noiʻi. a e hōʻoiaʻiʻo ʻia ke kaʻina hana hōʻoia maikaʻi no kēlā me kēia kānana ma ka nānā ʻana i nā kūlana kuhikuhi.
ʻO ka Empirical Bayesian Kriging (EBK) kekahi o nā ʻenehana interpolation geostatistical i hoʻohana ʻia i ka hoʻohālikelike ʻana i nā ʻano ʻano like ʻole e like me ka ʻepekema lepo. ʻO ke kaʻina hana interpolation o EBK e hahai i nā koina ʻekolu i manaʻo ʻia e Krivoruchko50. Hāʻawi ʻia ka lula hoʻohālikelike Bayesian ma ke ʻano he hope
Ma kahi o ka \(Prob\left(A\right)\) e hoike ana i ka mua, \(Prob\left(B\right)\) i malama ole ia ka probability marginal i ka hapanui o na hihia, \(Prob (B,A)\ ) . Hoʻokumu ʻia ka helu semivariogram ma luna o ka lula Bayes, e hōʻike ana i ka propensity o ka nānā ʻana i nā ʻikepili i hiki ke hana ʻia mai ka semivariograms mokuʻāina ʻo ia ka lula. pehea paha ka hana ʻana i kahi ʻikepili o ka nānā ʻana mai ka semivariogram.
ʻO ka mīkini vector kākoʻo he algorithm aʻo mīkini e hoʻohua ai i kahi hyperplane hoʻokaʻawale maikaʻi loa e ʻike i nā papa kūʻokoʻa like ʻole akā ʻaʻole linearly. - SVMR) ua hoʻohana ʻia ma kēia loiloi. ʻO Cherkassky lāua ʻo Mulier53 i paionia SVMR ma ke ʻano he kernel-based regression, ka helu ʻana o ia mea i hana ʻia me ka hoʻohana ʻana i kahi ʻano hoʻohālikelike linear me nā hana spatial lehulehu. al. 55, epsilon (ε)-SVMR ke hoʻohana nei i ka ʻikepili i hoʻomaʻamaʻa ʻia no ka loaʻa ʻana o kahi kumu hoʻohālike e like me ka hana epsilon-insensitive i hoʻopili ʻia e palapala i ka ʻikepili me ka maikaʻi epsilon bias mai ka hoʻomaʻamaʻa ʻana i ka ʻikepili i hoʻopili ʻia. vector.
kahi e hōʻike ai ka b i ka paepae scalar, \(K\hema({x}_{,}{ x}_{k}\ʻākau)\) hōʻike i ka hana kernel, \(\alpha\) hōʻike i ka Lagrange multiplier, N Hōʻike i kahi waihona helu helu, \({x}_{k}\) hōʻike i ka hoʻokomo ʻikepili, a ʻo \(y\) ka hana ʻikepili i hoʻohana ʻia. hana (RBF). Hoʻohana ʻia ka kernel RBF e hoʻoholo i ke kumu hoʻohālike SVMR maikaʻi loa, he mea koʻikoʻi ia e loaʻa ai ka helu hoʻopaʻi maʻalahi loa C a me ka kernel parameter gamma (γ) no ka ʻikepili aʻo PTE.
ʻO ke kumu hoʻohālikelike laina nui (MLR) he ʻano hoʻohālikelike e hōʻike ana i ka pilina ma waena o ka mea hoʻololi pane a me ka helu o nā mea wānana ma o ka hoʻohana ʻana i nā ʻāpana linear pooled i helu ʻia me ka hoʻohana ʻana i ke ʻano ʻāpana liʻiliʻi loa. nā mea hoʻololi. ʻO ka hoʻohālikelike MLR
ma kahi o y ka hoololi pane, \(a\) ka hoololi, n ka helu o na mea wanana, \({b}_{1}\) ka hoopau hapa o na coefficients, \({x}_{ i}\) hoike mai i ka mea wanana a wehewehe wehewehe paha, a o \({\varepsilon }_{i}\) ka hewa i ke koena.
Ua loaʻa nā ʻano hoʻohālike i hui ʻia e ka sandwiching EBK me SVMR a me MLR. Hana ʻia kēia ma ka unuhi ʻana i nā waiwai wānana mai ka interpolation EBK. ʻO nā waiwai wānana i loaʻa mai ka interpolated Ca, K, a me Mg e loaʻa ma o ke kaʻina hui e loaʻa ai nā ʻano hou, e like me CaK, CaMg, a me KMg. ʻO nā mea hoʻololi i loaʻa ʻo Ca, K, Mg, CaK, CaMg, KMg a me CaKMg. Ua lilo kēia mau mea hoʻololi i kā mākou wānana, e kōkua ana i ka wānana i ka nickel concentrations ma ke kūlanakauhale a me nā ʻāina peri-urban. ʻO Bayesian Kriging-Multiple Linear Regression (EBK_MLR). ʻO ka mea maʻamau, ua hoʻohana ʻia nā mea hoʻololi Ca, K, Mg, CaK, CaMg, KMg, a me CaKMg ma ke ʻano he covariates ma ke ʻano he wānana o ka ʻike Ni i loko o ke kūlanakauhale a me nā ʻāina pili. Kiʻi 2.
Ua lilo ka hoʻohana ʻana iā SeOM i mea hana kaulana no ka hoʻonohonoho ʻana, loiloi, a me ka wānana ʻana i ka ʻikepili i ka ʻāpana kālā, mālama ola kino, ʻoihana, ʻikepili, ʻepekema lepo, a me nā mea hou aku. hoʻokomo-dimensional vector variables43,56.Melssen et al. 57 wehewehe i ka pili ana o ka vector hookomo i loko o ka pūnaewele neural ma o ka papa komo hoʻokahi i ka mea hoʻopuka vector me kahi vector kaumaha hoʻokahi. ʻO ka huahana i hana ʻia e SeOM he palapala ʻelua-dimensional i loaʻa i nā neurons ʻokoʻa a i ʻole nodes i ulana ʻia i loko o nā palapala hexagonal, circular, a square topological paha e like me ko lākou kokoke. Ua koho ʻia ʻo 0.904, ʻo ia hoʻi, he 55-map unit (5 × 11). Hoʻoholo ʻia ka ʻōnaehana neuron e like me ka helu o nā nodes i ka empirical equation.
ʻO ka helu o nā ʻikepili i hoʻohana ʻia ma kēia noiʻi ʻana he 115 mau hōʻailona. Hoʻohana ʻia nā ʻano hoʻololi i hana ʻia e ka interpolation EBK ma ke ʻano he mea wānana a wehewehe wehewehe paha e wānana i ka mea hoʻololi i manaʻo ʻia (PTE). Hana ʻia ka hoʻohālike ʻana ma RStudio me ka hoʻohana ʻana i ka waihona waihona (Kohonen), hale waihona (caret), hale waihona (modelr), hale waihona ("e1071″), hale waihona puke ("plyr"), hale waihona puke ("plyr"), hale waihona puke ("plyr") (“Metrics”).
Ua hoʻohana ʻia nā ʻāpana hōʻoia like ʻole no ka hoʻoholo ʻana i ke kumu hoʻohālike maikaʻi loa i kūpono no ka wānana ʻana i ka neʻe ʻana o ka nickel i ka lepo a no ka loiloi ʻana i ka pololei o ke kumu hoʻohālike a me kona hōʻoia. ʻO ka nui ma nā ana kūʻokoʻa e wehewehe i ka mana wānana o ke kŘkohu, aʻo ka MAE e hoʻoholo i ka waiwai quantitative maoli. Pono ke kiʻekiʻe o ka waiwai R2 no ka loiloiʻana i ke kumu hoʻohālike maikaʻi loa me ka hoʻohanaʻana i nā palena hōʻoia,ʻo ka pili kokoke i ka 1,ʻo ka kiʻekiʻe o ka pololei. Wahi a Li et al. 59, ua manaʻo ʻia ka waiwai hōʻailona R2 o 0.75 a ʻoi aku paha he wānana maikaʻi; Mai ka 0.5 a hiki i ka 0.75 ka hana hoʻohālike i ʻae ʻia, a ma lalo o 0.5 ʻaʻole i ʻae ʻia ka hana hoʻohālikelike.
kahi n e hōʻike ana i ka nui o ka waiwai i ʻike ʻia \({Y}_{i}\) i ka pane i ana ʻia, a ʻo \({\widehat{Y}}_{i}\) pū kekahi i ka waiwai pane wānana, no laila, no nā ʻike mua i.
Hōʻike ʻia nā wehewehe ʻikepili o ka wānana a me nā ʻano pane pane i ka Papa 1, e hōʻike ana i ka mean, deviation maʻamau (SD), coefficient of variation (CV), liʻiliʻi, maximum, kurtosis, a me skewness. ʻO ka palena liʻiliʻi a me ka nui o nā mea i ka hoʻohaʻahaʻa ʻana o Mg < Ca < K < Ni a me Ca < Mg < K Ma muli o ke ana ʻokoʻa o nā ʻano mea i hoʻohālikelike ʻia, ua hōʻike ʻia ka skewness ʻokoʻa o ka hoʻonohonoho ʻikepili o nā mea. ʻO nā CV i manaʻo ʻia o nā mea e hōʻike pū ana ʻo K, Mg, a me Ni e hōʻike ana i ka ʻokoʻa haʻahaʻa, ʻoiai ʻo Ca he kiʻekiʻe loa ke ʻano.
ʻO ka pilina o nā mea wānana me nā mea pane i hōʻike i kahi pilina kūpono ma waena o nā mea (e nānā i ka Figure 3). Manaʻo ʻo 68 a me Santo69 i ka like ʻole o ko lākou pae i ka lepo. Akā naʻe, he kūʻē ʻo Ca a me Mg iā K, akā pili maikaʻi ʻo CaK. Ma muli paha o ka hoʻohana ʻana i nā mea kanu e like me ka potassium carbonate, ʻo ia ka 56% kiʻekiʻe i ka potassium. Hoʻopili ʻia ka nikela me Ca, K a me Mg me nā waiwai r = 0.52, 0.63 a me 0.55. ʻO nā pilina e pili ana i ka calcium, magnesium, a me nā PTE e like me ka nickel he paʻakikī, akā naʻe, hoʻemi ka magnesiump i ka calcium abscess a me ka magnesium. nā hopena ʻawaʻawa o ka nickel i ka lepo.
ʻO ka matrix correlation no nā mea e hōʻike ana i ka pilina ma waena o nā mea wānana a me nā pane (E hoʻomaopopo: aia kēia kiʻi i kahi scatterplot ma waena o nā mea, nā pae koʻikoʻi e pili ana i ka p <0,001).
Hōʻike ka Figure 4 i ka māhele ākea o nā mea. Wahi a Burgos et al70, ʻo ka hoʻohana ʻana i ka māhele spatial he ʻenehana i hoʻohana ʻia e helu a hōʻike i nā wahi wela ma nā wahi haumia. Hiki ke ʻike ʻia nā pae hoʻonui o Ca ma Fig. ma muli paha o ka hoʻohana ʻana i ka lime (calcium oxide) e hoʻemi i ka acidity o ka lepo a me kona hoʻohana ʻana i nā wili kila e like me ka oxygen alkaline i ke kaʻina hana kila. ma muli o ka NPK a me ka potash applications.This ua kūlike me nā haʻawina ʻē aʻe, e like me Madaras a me Lipavský72, Madaras et al.73, Pulkrabová et al.74, Asare et al.75, nāna i ʻike i ka hoʻopaʻa ʻana o ka lepo a me ka mālama ʻana me KCl a me NPK i loaʻa i ka ʻike K kiʻekiʻe i ka lepo. ʻO ka hoʻonui ʻia ʻana o ka Potassium Spatial ma ke komohana ʻākau o ka palapala hoʻohele ma muli o ka hoʻohana ʻana i nā mea hoʻomoʻa pālolo e like me ka potassium chloride, potassium sulfate, potassium nitrate, potash, a me ka potash e hoʻonui ai i ka pāpaʻi o nā lepo ʻilihune.Zádorová et al. 76 a me Tlustoš et al. Ua wehewehe ʻo 77 i ka hoʻohana ʻana i nā mea kanu K i hoʻonui i ka K i loko o ka lepo a e hoʻonui nui i ka ʻai o ka lepo i ka wā lōʻihi, ʻoi aku ka nui o K a me Mg e hōʻike ana i kahi wela o ka lepo. chlorosis. ʻO nā mea hoʻomomona i hoʻokumu ʻia e ka magnesium, e like me ka potassium magnesium sulfate, magnesium sulfate, a me Kieserite, mālama i nā hemahema (ke ʻike ʻia nā mea kanu i ka poni, ʻulaʻula, a ʻeleʻele paha, e hōʻike ana i ka hemahema o ka magnesium) i nā lepo me kahi pae pH maʻamau6. ʻO ka hōʻiliʻili ʻana o ka nickel ma ke kūlanakauhale a me ke kaona o ka lepo ma muli paha o nā hana anthropogenic e like me ke koʻikoʻi o ka mahiʻai kila7nickel8.
ʻO ka hoʻohele ākea o nā mea [ua hoʻokumu ʻia ka palapala ʻāina hoʻohele ma ka hoʻohana ʻana i ka ArcGIS Desktop (ESRI, Inc, Version 10.7, URL: https://desktop.arcgis.com).]
Hōʻike ʻia nā hualoaʻa hōʻike kumu hoʻohālike no nā mea i hoʻohana ʻia i loko o kēia haʻawina ma ka Papa 2. Ma kekahi ʻaoʻao, ua kokoke ka RMSE a me MAE o Ni i ka ʻole (0.86 RMSE, -0.08 MAE). Ma kekahi ʻaoʻao, ʻae ʻia nā waiwai ʻelua o RMSE a me MAE o K. Ua ʻike ʻia ʻo EBK e wānana iā Ni ma mua o nā hopena o John et al. 54 me ka hoʻohana ʻana i ka synergistic kriging e wānana i nā manaʻo S ma ka lepo me ka hoʻohana ʻana i ka ʻikepili i hōʻiliʻili like. 41, Yan et al. 79, Beguin et al. 80, Adhikary et al. 81 a me Ioane et al. 82, oi aku o K a me Ni.
Ua loiloiʻia ka hana o nāʻano hoʻokahi no ka wānanaʻana i ka nickel i loko o nā kūlanakauhale a me nā'āina āpau e hoʻohana i ka hana o nā hiʻohiʻona (Table 3). Hōʻoia ka hōʻoiaʻana o ka hōʻailona a me ka loiloi pololei i ka mea wānana Ca_Mg_K i hui pūʻia me ka EBK SVMR kumu hoʻohālike i ka hana maikaʻi loa. he 0.637 (R2), 95.479 mg/kg (RMSE) a me 77.368 mg/kg (MAE) Ca_Mg_K-SVMR he 0.663 (R2), 235.974 mg/kg (RMSE) a me 166.946 mg/kg (MAE). (0.663 mg / kg R2) a me Ca_Mg-EBK_SVMR (0.643 = R2); ʻoi aku ka kiʻekiʻe o kā lākou RMSE a me MAE hopena ma mua o nā Ca_Mg_K-EBK_SVMR (R2 0.637) (e nānā i ka Papa 3). Eia kekahi, ʻo ka RMSE a me ka MAE o ka Ca_Mg-EBK_SVMR (RMSE = 1664.64 a me MAE = 1031.49) he 17.4 a me ka 13. Ca_Mg_K-EBK_SVMR.Pēlā hoʻi, ʻo ka RMSE a me ka MAE o ka Ca_Mg-K SVMR (RMSE = 235.974 a me MAE = 166.946) kŘkohu he 2.5 a me 2.2 ka nui ma mua o ko ka Ca_Mg_K-EBK_SVMR RMSE a me ka MAE i hoʻonohonoho ʻia i nā hualoaʻa. kūpono. Ua nānā ʻia ʻo RSME kiʻekiʻe a me MAE. Wahi a Kebonye et al. 46 a me Ioane et al. 54, ʻoi aku ka pili o ka RMSE a me ka MAE i ka ʻole, ʻoi aku ka maikaʻi o nā hopena. SVMR a me EBK_SVMR ua kiʻekiʻe aʻe nā helu RSME a me MAE. outliers. 'O ia ho'i, 'o ka 'oi aku ka heterogeneous o ka dataset, 'o ia ke ki'eki'e o ka MAE a me ka RMSE waiwai. 'O ka pololei o ka loiloi cross-validation o ka Ca_Mg_K-EBK_SVMR kŘkohu huikau no ka wanana ana o Ni ma loko o ke kaona a me ka lepo o ka aina he 63.70%. E like me Li et al. 59, ʻo kēia pae o ka pololei he helu hana hoʻohālike e ʻae ʻia. Hoʻohālikelike ʻia nā hopena o kēia manawa me kahi noiʻi mua e Tarasov et al 36 nona ka hybrid kŘkohu i hana MLPRK (Multilayer Perceptron Residual Kriging), pili i ka EBK_SVMR pololei helu helu helu helu helu 'ia i loko o ka haʻawina o kēia manawa, RMSE (210) a me ka MAE (167.5) ua oi aku mamua o ko kakou mau hualoaʻa ma ka haʻawina o kēia manawa (RMSE 95.479, MAE 77.368). Tarasov et al. 36 (0.544), ua akaka ka nui o ka coefficient of determination (R2) i loko o kēia kŘkohu huikau. ʻO ka palena o ka hewa (RMSE a me MAE) (EBK SVMR) no ka mea hoʻohālike i hui pūʻia heʻelua manawa haʻahaʻa. Likelike, Sergeev et al.34 i hoʻopaʻa i ka 0.28 (R2) no ka hoʻomohalaʻana i keʻano hybrid (Multilayer Kriging) oiaiʻo Residuyal Perceptron i kēia manawa. (R2) .ʻO 63.7% ka wānana pololei o kēia kükohu (EBK SVMR), aʻo ka pololei o ka wānana i loaʻa e Sergeev et al. ʻO 34 ka 28%. ʻO ka palapala 'āina hope (Fig. 5) i hana ʻia me ka hoʻohana ʻana i ke kumu hoʻohālike EBK_SVMR a me Ca_Mg_K ma ke ʻano he wānana e hōʻike ana i nā wanana o nā wahi wela a me ka haʻahaʻa a hiki i ka nickel ma luna o ka wahi haʻawina holoʻokoʻa.
Hōʻike ʻia ka palapala wānana hope loa me ka hoʻohana ʻana i ke kumu hoʻohālike hybrid EBK_SVMR a me ka hoʻohana ʻana iā Ca_Mg_K ma ke ʻano he wānana.
Hōʻike ʻia ma ka Figure 6 he mau PTE i hoʻokumu ʻia ma ke ʻano he mau neurons. ʻO nā hiʻohiʻona i hoʻokahi neurons kiʻekiʻe a me nā neurons haʻahaʻa loa. No laila, kaʻana like ʻo CaK a me CaMg i nā mea like me nā neurons kiʻekiʻe loa a me nā ʻano kala haʻahaʻa haʻahaʻa. ua hōʻike ʻia ke ʻano hoʻohele planar o nā ʻāpana o ke kŘkohu i ke kala kiʻekiʻe e hōʻike ana i ka manaʻo o ka nickel i loko o ka lepo (e ʻike i ke kiʻi 4). Hōʻike ka mokulele ʻāpana kumu hoʻohālike CakMg i ke ʻano kala like ʻole mai ka haʻahaʻa a i ke kiʻekiʻe e like me ke kala pololei. ka huina o ka nickel concentrations ma ke kaona a me ke kaona. samples.The seven-component planar predictor combined to allow for correct cluster interpretation.Ma muli o ka nui anthropogenic a me ka hana kūlohelohe e pili ana i ka hoʻokumu ʻana i ka lepo, he mea paʻakikī ke hoʻokaʻawale pono i nā kumulāʻau puʻupuʻu i ka mahele SeOM map78.
Hoʻopuka ʻia nā mea mokulele e kēlā me kēia Empirical Bayesian Kriging Support Vector Machine (EBK_SVM_SeOM).
ʻO nā ʻāpana hoʻohālikelike hui like ʻole [Ua hana ʻia nā palapala SeOM me ka hoʻohana ʻana iā RStudio (version 1.4.1717: https://www.rstudio.com/).]
Hōʻike maopopo ka haʻawina i kēia manawa i nā ʻano hana hoʻohālike no ka neʻe ʻana o ka nickel i nā kūlanakauhale a me nā ʻāina āpau. hōʻoia i ka planar spatial mahele o nā mea i hōʻike ʻia e EBK_SVMR (e nānā i ke kiʻi 5). Hōʻike nā hopena i ke kumu hoʻohālike o ka mīkini hoʻihoʻi vector kākoʻo (Ca Mg K-SVMR) wānana i ka neʻe ʻana o Ni i ka lepo ma ke ʻano hoʻokahi kumu hoʻohālike, akā ʻo ka hōʻoia ʻana a me ka pololei o ka loiloi loiloi e hōʻike ana i nā hewa kiʻekiʻe loa ma ke ʻano o RMSE a me MAE. ka coefficient of determination (R2). Loaʻa nā hopena maikaʻi me ka hoʻohana ʻana i ka EBK SVMR a me nā mea i hui pū ʻia (CaKMg) me nā hewa haʻahaʻa RMSE a me MAE me ka pololei o 63,7%. ʻO ke ʻano o ka hoʻohana mau ʻana i nā mea kanu nickel-based a me ka hoʻohaumia ʻana o ka lepo e ka ʻoihana kila e hoʻonui i ka neʻe ʻana o ka nickel i ka lepo. Eia kekahi, ke manaʻo nei mākou e hoʻohana i ka EBK e hoʻohui me nā algorithm aʻo mīkini like ʻole. akā naʻe, ʻo ka hoʻohana ʻana i nā covariates hou e hoʻomaikaʻi nui i ka hana o ke kumu hoʻohālike, hiki ke manaʻo ʻia he palena o ka hana o kēia manawa.
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