Use of neurofuzzy networks to improve wastewater flow-rate forecasting

  • Authors:

  • F. J. Fernandez;A. Seco;J. Ferrer;M. A. Rodrigo

  • Affiliations:

  • Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad de Castilla-La Mancha, Campus Universitario s/n. 13071 Ciudad Real, Spain;Departamento de Ingeniería Química, Escuela Técnica Superior de Ingeniería, Universitat de Valencia, Dr. Moliner n 50, 46100 Burjassot, Valencia, Spain;Departamento de Ingeniería Hidráulica y Medio Ambiente, E.T.S.I.C.C.P, Universidad Politécnica de Valencia, Camino de Vera s/n. 46017 Valencia, Spain;Departamento de Ingeniería Química, Facultad de Ciencias Químicas, Universidad de Castilla-La Mancha, Campus Universitario s/n. 13071 Ciudad Real, Spain

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

A neurofuzzy wastewater flow-rate forecasting model (NFWFFM) has been developed and tested with actual data measured at the input of two wastewater treatment facilities which treat the wastewater corresponding to 150,000 and 1,250,000p.e., respectively. Good agreements between forecasted and actual flow-rates were obtained. The artificial intelligence algorithm uses only two input variables (day of the week and average daily flow-rate of day before) and one output variable (predicted average daily flow-rate). Using three months data for training the network, a long-term forecast (one month) is made with average errors below 10%. Results were compared with those obtained by applying the Census Method II (a commonly used decomposition/recomposition time series method) observing that forecast made by the NFWFFM is more accurate than the one made by this commonly used statistical method.