Application of the Artificial Neural Network and Neuro‐fuzzy System for Assessment of Groundwater Quality

One of the most important aspects of the evaluation of any aquatic system is the simulation of water quality parameters. Recently, artificial intelligence methods have been broadly applied to simulate hydrological processes. This study evaluates the potential of applying the neuro-fuzzy system and neural network to simulate total dissolved solid and electrical conductivity levels, by employing the values of other existing water quality parameters. Consideration of these results will be important for implementing and adopting a water quality prediction model which is able to provide a useful tool for the management of water resources. In this study, water quality data were analyzed from five sampling stations over six years from 2008 to 2013, in the Langat Basin, Malaysia. An assessment of the model's performance was carried out through the correlation coefficient and mean squared error obtained from the model computation and measurement values of the dependent variables. Consequently, a close agreement between these values and their respective measured values in the quality of the groundwater were found. Accordingly, artificial intelligence approaches and adaptive neuro-fuzzy inference system models in particular are capable of interpreting the behavior of water quality parameters.

[1]  K. P. Sudheer,et al.  Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India , 2010 .

[2]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[3]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[4]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[5]  Reza Kerachian,et al.  Developing operating rules for reservoirs considering the water quality issues: Application of ANFIS-based surrogate models , 2010, Expert Syst. Appl..

[6]  R. K. Tiwari,et al.  Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks , 2013, Environmental Monitoring and Assessment.

[7]  Shie-Yui Liong,et al.  An ANN application for water quality forecasting. , 2008, Marine pollution bulletin.

[8]  Kourosh Mohammadi,et al.  Groundwater Table Estimation Using MODFLOW and Artificial Neural Networks , 2009 .

[9]  Ying Zhao,et al.  Water quality forecast through application of BP neural network at Yuqiao reservoir , 2007 .

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

[11]  A. Khataee,et al.  Modeling of Biological Water and Wastewater Treatment Processes Using Artificial Neural Networks , 2011 .

[12]  Sharad K. Jain,et al.  Optimal Operation of a Multi-Purpose Reservoir Using Neuro-Fuzzy Technique , 2009 .

[13]  M. Castellano-Méndez,et al.  Modelling of the monthly and daily behaviour of the runoff of the Xallas river using Box-Jenkins and neural networks methods , 2004 .

[14]  Sun Tao.  Application of artificial neural network model to groundwaterquality assessment and classification , 2004 .

[15]  L. Shu,et al.  Assessment of Sustainable Yield of Karst Water in Huaibei, China , 2011 .

[16]  Paulin Coulibaly,et al.  Groundwater level forecasting using artificial neural networks , 2005 .

[17]  Soichi Nishiyama,et al.  Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool. , 2007, Journal of environmental management.

[18]  Ioannis K. Nikolos,et al.  Optimal selection of artificial neural network parameters for the prediction of a karstic aquifer's response , 2009 .

[19]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[20]  Mustafa M. Aral,et al.  Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm , 2007 .

[21]  Bikash Mohanty,et al.  Modeling of the Removal of Arsenic Species from Simulated Groundwater Containing As, Fe, and Mn: A Neural Network Based Approach , 2012 .

[22]  Faming Liang,et al.  Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting , 2011 .

[23]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .

[24]  Holger R. Maier,et al.  Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study , 1998 .

[25]  Vasil Simeonov,et al.  Comparative use of artificial neural networks for the quality assessment of the water reservoirs of Athens , 2013 .

[26]  François Anctil,et al.  Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models , 2004, Environ. Model. Softw..