Using geostatistics and machine learning models to analyze the influence of soil nutrients and terrain attributes on lead prediction in forest soils

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Title	Using geostatistics and machine learning models to analyze the influence of soil nutrients and terrain attributes on lead prediction in forest soils
Author info	Samuel Kudjo Ahado ... [et al.]
Author	Ahado Samuel Kudjo 1984- (50%) UMBFP09 - Katedra biológie a environmentálnych štúdií
Co-authors	Agyeman Prince Chapman (15%) Borůvka Luboš (15%) Kanianska Radoslava 1967- (15%) UMBFP09 - Katedra biológie a environmentálnych štúdií Nwaogu Chukwudi (5%)
Source document	Modeling earth systems and environment. Vol. 10, no. 2 (2024), pp. 2099-2112. - Cham : Springer Nature Switzerland AG, 2024
Keywords	olovo - lead lesné pôdy pôdne živiny - soil nutrients geoštatistika
Form. Descr.	články - journal articles
Language	English
Country	Germany
URL	Link na plný text
Public work category	ADM
No. of Archival Copy	54322
Repercussion category	KWAYISI, Daniel - KAZAPOE, Raymond Webrah - ALIDU, Seidu - SAGOE, Samuel Dzidefo - UMARU, Aliyu Ohiani - AMUAH, Ebenezer Ebo Yahans - KPIEBAYA, Prosper. Exploring soil pollution patterns in Ghana's 's northeastern mining zone using machine learning models. In Journal of hazardous materials advances. ISSN 2772-4166, 2024, vol. 16, art. no. 100480, pp. 1-13. DOI: https://doi.org/10.1016/j.hazadv.2024.100480
Catal.org.	BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici
Database	xpca - PUBLIKAČNÁ ČINNOSŤ
References	PERIODIKÁ-Súborný záznam periodika

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Number of the records: 1