Supervised committee machine with artificial intelligence for prediction of fluoride concentration

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.

[1]  Jasna Radulović,et al.  Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system , 2012 .

[2]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[3]  S Zietsman.  Spatial variation of fluorosis and fluoride content of water in an endemic area in Bophuthatswana. , 1991, The Journal of the Dental Association of South Africa = Die Tydskrif van die Tandheelkundige Vereniging van Suid-Afrika.

[4]  Mohammad N. Almasri,et al.  Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data , 2005, Environ. Model. Softw..

[5]  Chang-Hsu Chen,et al.  A committee machine with empirical formulas for permeability prediction , 2006, Comput. Geosci..

[6]  K. P. Singh,et al.  Controls on the genesis of some high-fluoride groundwaters in India , 2005 .

[7]  K Bjorvatn,et al.  Variability in fluoride content of subsurface water reservoirs. , 1996, Acta odontologica Scandinavica.

[8]  Paul F Hudak,et al.  Spatial Patterns of Nitrate, Chloride, Sulfate, and Fluoride Concentrations in the Woodbine Aquifer of North-Central Texas , 2003, Environmental monitoring and assessment.

[9]  B. K. Handa,et al.  Geochemistry and Genesis of Fluoride‐Containing Ground Waters in India , 1975 .

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

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

[12]  Mohammad Teshnehlab,et al.  Using adaptive neuro-fuzzy inference system for hydrological time series prediction , 2008, Appl. Soft Comput..

[13]  Abdulazeez Abdulraheem,et al.  Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization , 2011 .

[14]  Inmaculada Pulido-Calvo,et al.  Improved irrigation water demand forecasting using a soft-computing hybrid model , 2009 .

[15]  K. Dragon,et al.  Application of factor analysis to study contamination of a semi-confined aquifer (Wielkopolska Buried Valley aquifer, Poland) , 2006 .

[16]  Berhanu Gizaw,et al.  The origin of high bicarbonate and fluoride concentrations in waters of the Main Ethiopian Rift Valley, East African Rift system , 1996 .

[17]  Ole Mark,et al.  Application of hydroinformatics tools for water quality modeling and management: case study of Vientiane, Lao P.D.R , 2010 .

[18]  H. Kaiser.  The varimax criterion for analytic rotation in factor analysis , 1958 .

[19]  Bülent Tütmez,et al.  Comparing two data driven interpolation methods for modeling nitrate distribution in aquifer , 2010, Ecol. Informatics.

[20]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[21]  E. H. Mamdani,et al.  Advances in the linguistic synthesis of fuzzy controllers , 1976 .

[22]  David L. Ozsvath,et al.  Fluoride concentrations in a crystalline bedrock aquifer Marathon County, Wisconsin , 2006 .

[23]  F. Tsai,et al.  Hydrogeochemical analysis for Tasuj plain aquifer, Iran , 2013, Journal of Earth System Science.

[24]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[25]  Mohammadreza Rezaee,et al.  A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: An example from Kangan and Dalan reservoirs in South Pars Gas Field, Iran , 2009, Comput. Geosci..

[26]  Jong-Se Lim,et al.  Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea , 2005 .

[27]  Vijay P. Singh,et al.  ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff , 2006 .

[28]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[29]  B. P. McGrail.  Inverse reactive transport simulator (INVERTS): an inverse model for contaminant transport with nonlinear adsorption and source terms , 2001, Environ. Model. Softw..

[30]  Luis A. Garcia,et al.  Using neural networks for parameter estimation in ground water , 2006 .

[31]  Barnali M. Dixon,et al.  Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis , 2005 .

[32]  Vahid Nourani,et al.  Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes , 2012, Adv. Eng. Softw..

[33]  Shakeel Ahmed,et al.  Inferring the chemical parameters for the dissolution of fluoride in groundwater , 2003 .

[34]  E. H. Mandami.  Application of Fuzzy Logic to Approximate Reasoning using Linguistic Synthesis , 1977 .

[35]  Karim Salahshoor,et al.  Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin , 2010 .

[36]  Vahid Nourani,et al.  Hybrid Wavelet-Genetic Programming Approach to Optimize ANN Modeling of Rainfall-Runoff Process , 2012 .

[37]  Elham Fijani,et al.  Distribution of fluoride in groundwater of Maku area, northwest of Iran , 2008 .

[38]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[39]  Sunanda Mitra,et al.  Adaptive fuzzy leader clustering of complex data sets in pattern recognition , 1992, IEEE Trans. Neural Networks.

[40]  J. A. Grande,et al.  Fuzzy Modeling of the Spatial Evolution of the Chemistry in the Tinto River (SW Spain) , 2010 .

[41]  Nima Chitsazan,et al.  Bayesian Artificial Intelligence Model Averaging for Hydraulic Conductivity Estimation , 2014 .

[42]  Simon X. Yang,et al.  Neural networks for predicting nitrate-nitrogen in drainage water , 2003 .

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

[44]  C. B. Dissanayake,et al.  The fluoride problem in the ground water of Sri Lanka — environmental management and health , 1991 .

[45]  Vahid Nourani,et al.  An ANN‐based model for spatiotemporal groundwater level forecasting , 2008 .

[46]  Poonam Kharb,et al.  Fluoride ingestion in excess and its effect on organic and certain inorganic constituents of soft tissues , 1994 .

[47]  Jose Antonio Grande,et al.  Application of Factor Analysis to the Study of Contamination in the Aquifer System of Ayamonte‐Huelva (Spain) , 1996 .

[48]  Shakeel Ahmed,et al.  Dissolution of fluoride in groundwater: a water-rock interaction study , 2001 .

[49]  S. Upchurch,et al.  Interpretation of Hydrochemical Facies by Factor Analysis , 1978 .

[50]  Inmaculada Pulido-Calvo,et al.  Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.) intensive rearing system , 2004 .

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

[52]  Mu-Song Chen,et al.  Fuzzy clustering analysis for optimizing fuzzy membership functions , 1999, Fuzzy Sets Syst..

[53]  Meetu Agarwal,et al.  Origin of high fluoride in groundwater in the North Gujarat-Cambay region, India , 2005 .