@article {553,
	title = {A systematic mapping of water quality prediction using computational intelligence techniques},
	journal = {Revista Ingenier{\'\i}as Universidad de Medell{\'\i}n},
	volume = {15},
	year = {2016},
	chapter = {35-52},
	abstract = {<p class="rtejustify"><span style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 12.61px; text-align: start;">Due to the renewable nature of water, this resource has been treated and managed as if it were unlimited; however, increase the indiscriminate use has brought with it a rapid deterioration in quality; so as predicting water quality has a very important role for many socio-economic sectors that depend on the use of the precious liquid. In this study, a systematic literature mapping was performed about water quality prediction using computational intelligence techniques, including those used to calibrate predictive models in order to improve accuracy. Based on research questions formulated in the systematic mapping, a gap is identified oriented to creation of an adaptive mechanism for predicting water quality that can be applied in different water uses without raised the accuracy of the predictions is affected.</span></p>
},
	keywords = {complex adaptive systems, computational intelligence, forecasting, water quality},
	issn = {1692-3324},
	doi = {http://dx.doi.org/10.22395/rium.v15n28a2 },
	url = {http://www.scielo.org.co/scielo.php?script=sci_arttext\&pid=S1692-33242016000100003},
	author = {L{\'o}pez, I. D. and Figueroa, A. and Corrales, J. C.}
}
