info:eu-repo/semantics/bookPart
Prediction of biochemical oxygen demand in mexican surface waters using machine learning
Fecha
2022-08-04Autor
Guzmán Fernández, Maximiliano
Zambrano de la Torre, Misael
Sifuentes Gallardo, Claudia
Cruz Dominguez, Oscar
Bautista Capetillo, Carlos
Badillo de Loera, Juan
González Ramírez, Efrén
Durán Muñoz, Héctor
Institución
Resumen
The monitoring of surface water quality is insufficient in Mexico due to the limited water monitoring
stations. The main monitoring parameter to evaluate surface water quality is the biochemical oxygen
demand. This parameter estimates the biodegradable organic matter present in the water.
Concentrations above 30 mg/l indicates a high level of contamination by domestic and industrial
waste. Therefore, the aim of this work to provide a reference to the conventional process of
determining biochemical oxygen demand using machine learning. The database used was collected
by the National Water Commission (CONAGUA). Pearson’s correlation and Forward Selection
techniques were applied to identify the parameters with the most important contribution to prediction
of biochemical oxygen demand. Two groups were formed and used as input to four machine learning
algorithms. Random forest algorithm obtained the best performance. Group 1 and 2 of parameters
obtained a 0.76 and 0.75 coefficient of determination respectively. This allows choosing an adequate
group of parameters that can be determined with the chemical analysis instruments available in the
study area.