A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input–output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.

[1]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[2]  Reza Kerachian,et al.  Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks , 2009, Environ. Model. Softw..

[3]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[4]  Patricia González,et al.  Influence of urbanization and tourist activities on the water quality of the Potrero de los Funes River (San Luis – Argentina) , 2007, Environmental monitoring and assessment.

[5]  Mohammad Reza Nikoo,et al.  Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction , 2011 .

[6]  Mark E. Borsuk,et al.  A Bayesian belief network for modelling brown trout (Salmo trutta) populations in Switzerland , 2004 .

[7]  J. Cain,et al.  Application of belief networks to water management studies , 1999 .

[8]  Wray L. Buntine A Guide to the Literature on Learning Probabilistic Networks from Data , 1996, IEEE Trans. Knowl. Data Eng..

[9]  Mohammad Reza Nikoo,et al.  Developing real time operating rules for trading discharge permits in rivers: Application of Bayesian Networks , 2009, Environ. Model. Softw..

[10]  Mohammad Karamouz,et al.  Development of a Master Plan for Water Pollution Control Using MCDM Techniques: A Case Study , 2003 .

[11]  Mohammad Karamouz,et al.  Application of Genetic Algorithms and Artificial Neural Networks in Conjunctive Use of Surface and Groundwater Resources , 2007 .

[12]  Reza Kerachian,et al.  Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN , 2010, Expert Syst. Appl..

[13]  Seockheon Lee,et al.  Application of Water Quality Indices and Dissolved Oxygen as Indicators for River Water Classification and Urban Impact Assessment , 2007, Environmental monitoring and assessment.

[14]  M. H. Kazeminezhad,et al.  Hindcasting of wave parameters using different soft computing methods , 2008 .

[15]  Reza Kerachian,et al.  Revising river water quality monitoring networks using discrete entropy theory: the Jajrood River experience , 2011, Environmental monitoring and assessment.

[16]  V. P. Semenchenko,et al.  Comparative analysis of two approaches to the assessment of water quality by biological indices: Case Study Of Dnieper tributaries , 2009 .

[17]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[18]  H. Boyacıoğlu,et al.  Utilization of the water quality index method as a classification tool , 2010, Environmental monitoring and assessment.

[19]  C. Cude OREGON WATER QUALITY INDEX A TOOL FOR EVALUATING WATER QUALITY MANAGEMENT EFFECTIVENESS 1 , 2001 .

[20]  Anne Ng,et al.  Predicting Structural Deterioration Condition of Individual Storm-Water Pipes Using Probabilistic Neural Networks and Multiple Logistic Regression Models , 2009 .

[21]  Elif Derya Übeyli Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals , 2008, Neural Networks.

[22]  Roberto Urrutia,et al.  Evaluation of Water Quality in the Chillán River (Central Chile) Using Physicochemical Parameters and a Modified Water Quality Index , 2005, Environmental monitoring and assessment.

[23]  Frederick W. Williams,et al.  Multi-criteria fire detection systems using a probabilistic neural network , 2000 .

[24]  Scott Painter,et al.  Application of a Sediment Quality Index to the Lower Laurentian Great Lakes , 2004, Environmental monitoring and assessment.

[25]  Eduardo Beamonte Córdoba,et al.  Water quality indicators: Comparison of a probabilistic index and a general quality index. The case of the Confederación Hidrográfica del Júcar (Spain) , 2010 .

[26]  William Ocampo-Duque,et al.  Assessing water quality in rivers with fuzzy inference systems: a case study. , 2006, Environment international.

[27]  Romà Tauler,et al.  Surface-water-quality indices for the analysis of data generated by automated sampling networks , 2010 .

[28]  David K. Stevens,et al.  An Innovative Index for Evaluating Water Quality in Streams , 2004, Environmental management.

[29]  Seyed Jamshid Mousavi,et al.  A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters , 2009, Eng. Appl. Artif. Intell..

[30]  Simaan M. AbouRizk,et al.  BELIEF NETWORKS FOR CONSTRUCTION PERFORMANCE DIAGNOSTICS , 1998 .

[31]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[32]  Shang-Lien Lo,et al.  A Generalized Water Quality Index for Taiwan , 2004, Environmental monitoring and assessment.