Using Artificial Neural Network Models for Eutrophication Prediction

Abstract Artificial neural network (ANN), a data driven modeling approach, is proposed to predict the water quality indicators of Lake Fuxian, the deepest lake of southwest China. To determine the non-linear relationships between the water quality factors and the eutrophication indicators, several ANN models was chosen for the investigation. A commonly used back-propagation neural network model was used to relate the key factors that influence a number of water quality indicators such as dissolved oxygen (DO), total phosphorus (TP), chlorophyll-a (Chl-a), and secchi disk depth (SD) in Lake Fuxian. The measured data were fed to the input layer, representing forcing functions to control the in- lake bio-chemical processes. Eutrophication indicators such as DO, TN, Chl-a and SD were represented in the output layers. The results indicated that the back-propagation neural network model performs good in ten months prediction and the neural network is able to predict these indicators with reasonable accuracy. This study also suggested that the neural network is a valuable tool for lake management.

[1]  F. Recknagel,et al.  Modeling Of Algal Blooms In FreshwatersUsing Artificial Neural Networks , 1970 .

[2]  Weihao Zhang,et al.  Phosphorus removal from water of eutrophic Lake Donghu by five submerged macrophytes. , 2009 .

[3]  Yan Huang,et al.  Neural network modelling of coastal algal blooms , 2003 .

[4]  Hongzhu Wang,et al.  Macrozoobenthic community of Fuxian Lake, the deepest lake of southwest China , 2008 .

[5]  Holger R. Maier,et al.  Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters , 2004, Environ. Model. Softw..

[6]  Qing Zhang,et al.  Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling , 1997 .

[7]  Gary R. Weckman,et al.  Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network , 2011, Environ. Model. Softw..

[8]  Jan-Tai Kuo,et al.  USING ARTIFICIAL NEURAL NETWORK FOR RESERVOIR EUTROPHICATION PREDICTION , 2007 .

[9]  A. Malik,et al.  Artificial neural network modeling of the river water quality—A case study , 2009 .

[10]  W. Zeng,et al.  Research on ANN-based Pre-warning Water Bloom Model of LiuHai Lake in Beijing , 2010 .

[11]  I. Dimopoulos,et al.  Application of neural networks to modelling nonlinear relationships in ecology , 1996 .

[12]  Adebayo J. Adeloye.  The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs , 2009, Environ. Model. Softw..

[13]  S. Soyupak,et al.  Case studies on the use of neural networks in eutrophication modeling , 2000 .

[14]  Ralf Wieland,et al.  Adaptive fuzzy modeling versus artificial neural networks , 2008, Environ. Model. Softw..

[15]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[16]  Holger R. Maier,et al.  Use of artificial neural networks for modelling cyanobacteria Anabaena spp. in the River Murray, South Australia , 1998 .

[17]  Ahmet Kurunç,et al.  Performance of two stochastic approaches for forecasting water quality and streamflow data from Yesilrmak River, Turkey , 2005, Environ. Model. Softw..

[18]  Emilio Fernández,et al.  Modelling primary production in a coastal embayment affected by upwelling using dynamic ecosystem models and artificial neural networks , 1999 .