Evaluation of WWTP discharges into a Mediterranean river using KSOM neural networks and mass balance modelling

The water quality of the Tet River, referred to nutrients compounds, is lower than the expected. Its management must be largely improved. The present work takes part in a global effort of development and evaluation of reliable and robust tools, with the aim of allowing the control and supervision of its lowland area (at the south Mediterranean coast of France). A simplified model, based on mass balances, has been developed to estimate nitrogen and organic matter concentrations in the stream and to describe the river water quality. Kohonen self-organizing maps (KSOMs) were used to deal with missing data. This kind of neural networks proved to be very useful to predict missing components and to complete the available database, describing the chemical quality of the river and the WasteWater Treatment Plant (WWTP) outflows. The simulation model also proved to be a good tool for the system evaluated. The results it provided reveal the high impact of the WWTPs located along the studied area, due to malfunction and tourism activities.

[1]  Monique Polit,et al.  KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process , 2006 .

[2]  J. M. Zaldívar,et al.  Modelling water discharges and nitrogen inputs into a Mediterranean lagoon: Impact on the primary production , 2006 .

[3]  B. C. Patten,et al.  Systems Analysis and Simulation in Ecology , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[5]  Federico Marini,et al.  Class-modeling using Kohonen artificial neural networks , 2005 .

[6]  B. Crabtree,et al.  A case study of regional catchment water quality modelling to identify pollution control requirements. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[7]  Nikola Kasabov,et al.  Neuro-Fuzzy Techniques for Intelligent Information Systems , 1999 .

[8]  Paul L. Younger,et al.  A numerical modelling and neural network approach to estimate the impact of groundwater abstractions on river flows , 2007 .

[9]  Patrick Rousset,et al.  Cartes auto-organisées pour l'analyse exploratoire de données et la visualisation , 2006, ArXiv.

[10]  Yoon-Seok Timothy Hong,et al.  Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis. , 2003, Water research.

[11]  David G. Angeler,et al.  Mediterranean limnology: current status, gaps and the future , 2005 .

[12]  Monique Polit,et al.  Neural networks for estimating the efficiency of a WWTP biologic treatment , 2005, CCIA.

[13]  G. Marsily,et al.  A statistical method for source apportionment of riverine nitrogen loads , 2003 .

[14]  Olli Simula,et al.  Analysis of Complex Systems Using the Self-Organizing Map , 1997, ICONIP.

[15]  Olli Simula,et al.  Analysis and Modeling of Complex Systems Using the Self-Organizing Map , 1999 .

[16]  R. Howarth,et al.  Human acceleration of the nitrogen cycle: drivers, consequences, and steps toward solutions. , 2004, Water science and technology : a journal of the International Association on Water Pollution Research.

[17]  Manel Poch,et al.  Knowledge acquisition in the STREAMES project: the key process in the Environmental Decision Support System development , 2003, AI Commun..

[18]  Manel Poch,et al.  Nutrient retention efficiency in streams receiving inputs from wastewater treatment plants. , 2004, Journal of environmental quality.

[19]  Michael Matthies,et al.  System analysis of water quality management for the Elbe river basin , 2006, Environ. Model. Softw..

[20]  Wolfgang Ludwig,et al.  Evaluating the impact of the recent temperature increase on the hydrology of the Têt River (Southern France) , 2004 .

[21]  P. Vanrolleghem,et al.  Simplifying Dynamic River Water Quality Modelling: A Case Study of Inorganic Nitrogen Dynamics in the Crocodile River (South Africa) , 2001 .

[22]  Laurent Cadilhac Le système d'évaluation de la qualité des eaux souterraines « SEQ - Eaux souterraines » , 2003 .

[23]  S. Bartell,et al.  Dynamics of Lotic Ecosystems. , 1984 .

[24]  L. Mays Water Resources Handbook , 1996 .

[25]  W. Gujer,et al.  Activated sludge model No. 3 , 1995 .

[26]  F H Schulze,et al.  Applications of Artificial Neural Networks in integrated water management: fiction or future? , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[27]  Esther Llorens i Ribes Desenvolupament d'un sistema expert com a eina per a una millor gestió de la qualitat de les aigües fluvials , 2004 .

[28]  Robert H. Boling,et al.  9 – Ecosystem Modeling for Small Woodland Streams* , 1975 .

[29]  D. Harper,et al.  Floodplain Forests and River Restoration , 1997 .

[30]  Earle B. Phelps,et al.  A Study of the Pollution and Natural Purification of the Ohio River , 1958 .

[31]  Monique Polit,et al.  Prediction of parameters characterizing the state of a pollution removal biologic process , 2005, Eng. Appl. Artif. Intell..