Data/hora: 12/06/2024 11:59:27

Provedor de dados: 	65	
País: 	Brazil	
Título: 	Comparative analysis of decision tree algorithms on quality of water contaminated with soil	
Autores: 	Dota,Mara Andrea; Cugnasca,Carlos Eduardo; Barbosa,Domingos Sávio.	
Data: 	2015-02-01	
Ano: 	2015	
Palavras-chave: 	Environmentalcontrol; Runoff; Wireless sensor networks; Machine learning; Data mining.	
Resumo: 	Agriculture, roads, animal farms and other land uses may modify the water quality from rivers, dams and other surface freshwaters. In the control of the ecological process and for environmental management, it is necessary to quickly and accurately identify surface water contamination (in areas such as rivers and dams) with contaminated runoff waters coming, for example, from cultivation and urban areas. This paper presents a comparative analysis of different classification algorithms applied to the data collected from a sample of soil-contaminated water aiming to identify if the water quality classification proposed in this research agrees with reality. The sample was part of a laboratory experiment, which began with a sample of treated water added with increasing fractions of soil. The results show that the proposed classification for water quality in this scenario is coherent, because different algorithms indicated a strong statistic relationship between the classes and their instances, that is, in the classes that qualify the water sample and the values which describe each class. The proposed water classification varies from excelling to very awful (12 classes)	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782015000200267	
Editor: 	Universidade Federal de Santa Maria	
Relação: 	10.1590/0103-8478cr20140147	
Formato: 	text/html	
Fonte: 	Ciência Rural  v.45 n.2 2015	
Fonte: 	info:eu-repo/semantics/openAccess