(untitled) (Latin-American Journal of Computing, 2017)
In the process of oil extraction, specifically in therefinement and industrialization of hydrocarbons, as is known,multiple wastes are highly polluting for the soil, water and air.In this work, the risk level of these wastes in affected areasis estimated thanks to the application of statistical models inthe field of functional data analysis. These models have beenimplemented in a statistical software called RStudio that allowsan early measurement and evaluation of the level of risk by usingsemiquantitative and quantitative methods. This measurement iscarried out by the staff of PETROECUADOR close to the affectedplace. It was used the laser-induced fluorescence technique (LIF).The data obtained using this technique was used to adjust thefollowing models: Generalized Functional Linear Model (MLFG),which makes it possible to classify the spectrum generated intwo pollution levels: Low and High. Functional linear regressionmodel with scalar response and functional explanatory variablewith the aim of directly estimating the percentage of contaminationlevel. With these results it is verified that the shape ofthe laser fluorescence spectrum is highly related to the gasolinecontent in the sample.