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               <dc:title>A Software system for the microbial source tracking problem</dc:title>
               <dc:creator>Sánchez-Mendoza, David</dc:creator>
               <dc:subject>Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic</dc:subject>
               <dc:subject>Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Desenvolupament humà::Aigua i sanejament</dc:subject>
               <dc:subject>Machine learning</dc:subject>
               <dc:subject>Water pollution</dc:subject>
               <dc:subject>Sewage - Microbiology</dc:subject>
               <dc:subject>Aprenentatge automàtic</dc:subject>
               <dc:subject>Aigua--Contaminació</dc:subject>
               <dc:subject>Aigües residuals--Microbiologia</dc:subject>
               <dc:description>The Microbial Source Tracking problem (MST) has to do with the determination of&#xd;
the fecal pollution origin in waters by the use of microbial and chemical indicators.&#xd;
This document introduces a methodology for solving MST problem from the machine&#xd;
learning point of view reporting both the arising specifi c problems and challenges and&#xd;
how they have been addressed. The simplest instance of the MST problem has already&#xd;
been solved to satisfaction using machine learning techniques on recently and&#xd;
heavily polluted waters, however, our methodology accepts examples showing di fferent&#xd;
concentration levels and using indicators (variables) with diff erent environmental&#xd;
persistence. The theoretical methodology is supported by a software which implements&#xd;
it and has been validated using two real datasets with real data from diff erent&#xd;
geographical and climatic areas.</dc:description>
               <dc:date>2012-06-20</dc:date>
               <dc:type>Master thesis</dc:type>
               <dc:rights>Open Access</dc:rights>
               <dc:publisher>Universitat Politècnica de Catalunya</dc:publisher>
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