Arima as a forecasting tool for water quality time series measured with UV-Vis spectrometers in a constructed wetland

Autores/as

  • Nathalie Hernández Pontificia Universidad Javeriana
  • Julio Camargo Pontificia Universidad Javeriana
  • Fredy Moreno Pontificia Universidad Javeriana
  • Andrés Torres Pontificia Universidad Javeriana
  • Leonardo Plazas Nossa Universidad Distrital Francisco José de Caldas

DOI:

https://doi.org/10.24850/j-tyca-2017-05-09

Palabras clave:

Forecasting methods, time series analysis, UVVis spectrometry, water quality, wetland

Resumen

Hernández, N., Camargo, J., Moreno, F., Plazas-Nossa, L., & Torres, A. (September-October, 2017). Arima as a tool to predict water quality using time series recorded with UV-Vis spectrometers in a constructed wetland. Water Technology and Sciences (in Spanish), 8(5), 127-139.

The prediction of water quality plays a crucial role in discussions about urban drainage systems, given that the integrated management of this resource is required in order to meet human needs. The present paper uses Arima (Autoregressive Integrated Moving Average) to predict influent and effluent water quality in a constructed wetland, as well as its pollutant removal efficiency. The wetland is located on the campus of the Pontificia Universidad Javeriana in Bogotá, Colombia. Arima prediction values were based on time series obtained with UV-Vis spectrometry probes. These predictions were found to be adequate for the first 12 hours of the water quality time series for the three data sets analyzed: influent, effluent, and efficiency. Overall, none of the data had prediction errors over 15%. In separate analyses of the relative predictive errors in influent and effluent values, they were found to be less significant for UV wavelengths than for the visible range (Vis). In addition, the variability in this type of error was less for the UV range than for the Vis range, which indicates that Arima is a suitable prediction method for analyzing pollutants that fall in the UV range.

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Publicado

2017-10-02