Water is a complicated
environment system; traditional methods cannot meet the demands of water
environment protection. As the frontrunner of complex nonlinear science and
artificial intelligence, artificial neural network has begun to be applied in
the field of water quality evaluation and estimation. In view of the deficiency
of the traditional methods, artificial intelligence techniques, such as neural
networks modeling tools, can be applied to assess water quality parameters.
This study is conducted to evaluate factors regulating groundwater quality in
and around Erode District, Tamil Nadu, India. This investigation is focused on
the determination of physico-chemical parameters such as pH, EC, TDS, Ca, Mg,
TH, Na, K, HCO3, SO4？and
Cl. Groundwater suitability for drinking, domestic and agricultural purposes is
examined with WHO standards. Dominant factors controlling the
hydro-geochemistry of groundwater in the study area is indicated by Principal
Component Analysis. Classification methods are used to classify the water
quality regulating factors. Cluster analysis is supporting for the grouping on
the basis of contamination characteristics of groundwater quality. This study
also reveals that multivariate statistical analyses are used to improve the
understanding of groundwater condition and appraisal of groundwater quality.
References
[1]
Davis, S.N. and Wiest, D. (1996) Hydrogeology. John Wiley & Sons, New York.
[2]
Brydon, D.A. and Frodsham, D.A. (2001) A Model-Based Approach to Predicting BOD5 in Settled Sewage. Water Science and Technology, 44, 9-15.
[3]
Chandramouli, V., Brion, G., Neelakantan, T.R. and Lingireddy, S. (2007) Backfilling Missing Microbial Concentrations in a Riverine Database Using Artificial Neural Networks. Water Research, 41, 217-227. http://dx.doi.org/10.1016/j.watres.2006.08.022
[4]
Cho, J.H., Sung, K.S. and Ha, S.R. (2004) A River Water Quality Management Model for Optimising Regional Wastewater Treatment Using a Genetic Algorithm. Journal of Environmental Management, 73, 229-242. http://dx.doi.org/10.1016/j.jenvman.2004.07.004
[5]
Han, J., Kamber, M. and Pei, J. (2011) Data Mining: Concepts and Techniques. 3rd Edition, Morgan Kaufmann Publishers, Burlington.
[6]
Icaga, Y. (2005) Genetic Algorithm Usage in Water Quality Monitoring Networks Optimization in Gediz (Turkey) River Basin. Environmental Monitoring and Assessment, 108, 261-277. http://dx.doi.org/10.1007/s10661-005-4328-z
[7]
Ip, W.C., Hu, B.Q., Wong, H. and Xia, J. (2009) Applications of Grey Relational Method to River Environment Quality Evaluation in China. Journal of Hydrology, 379, 284-290. http://dx.doi.org/10.1016/j.jhydrol.2009.10.013
[8]
O’Connor, E., Smeaton, A.F., O’Connor, N.E. and Regan, F. (2012) A neural Network Approach to Smarter Sensor Networks for Water Quality Monitoring. Sensors, 12, 4605-4632. http://dx.doi.org/10.3390/s120404605
[9]
?enkal, O., Y?ld?z, B.Y., ?ahin, M. and Pestemalcl, V. (2012) Precipitable Water Modelling Using Artificial Neural Network in Cukurova Region. Environmental Monitoring and Assessment, 184, 141-147. http://dx.doi.org/10.1007/s10661-011-1953-6
[10]
Smeti, E.M., Thanasoulias, N.C., Lytras, E.S., Tzoumerkas, P.C. and Golfinopoulos, S.K. (2009) Treated Water Quality Assurance and Description of Distribution Networks by Multivariate Chemometrics. Water Research, 43, 4676- 4684. http://dx.doi.org/10.1016/j.watres.2009.07.023
[11]
Zou, R. and Lung, W.S. (2004) Robust Water Quality Model Calibration Using an Alternating Fitness Genetic Algorithm. Journal of Water Resources Planning and Management, 130, 471-479. http://dx.doi.org/10.1061/(ASCE)0733-9496(2004)130:6(471)
[12]
Chen, T. and Chen, H. (1995) Approximation Capability to Functions of Several Variables, Nonlinear Functions and Operator by Radial Basis Function Neural Network. IEEE Transactions on Neural Networks, 6, 904-910. http://dx.doi.org/10.1109/72.392252
[13]
Hagan, M.T., Demuth, H.B. and Beale, M.H. (2004) Neural Network Design. China Machine Press, Beijing.
[14]
Zhao, L.M., Hu, H.Y., Wei, D.H. and Wang, S.Q. (1999) Multilayer forward Artificial Neural Network. Yellow River Conservancy Press, Zhengzhou.
[15]
Luo, D.G., Guo, Q. and Wang, X.J. (2003) Neural Network Model Design of Surface Water Environmental Quality Assessment. Geography and Geo-Information Science, 19, 77-81 (in Chinese).
[16]
Fu, Y.F., Zhang, J. and Luo, G.M. (2004) Application of BP Network to Groundwater Quality Evaluation. Journal of Northwest Sci-Tech University of Agriculture and Forestry, 32, 129-132.
[17]
Cheng, C.T., Chau, K.W., Sun, Y.G. and Lin, J.Y. (2005) Long-Term Prediction of Discharges in Manwan Reservoir Using Artificial Neural Network Models. In: Wang, J., Liao, X.-F. and Yi, Z., Eds., Advances in Neural Networks— ISNN 2005, Springer, Berlin, Heidelberg, 1040-1045. http://dx.doi.org/10.1007/11427469_165
[18]
Liu, W.C. and Chung, C.E. (2014) Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System. Water, 6, 1642-1661. http://dx.doi.org/10.3390/w6061642
[19]
Chen, W.B. and Liu, W.C. (2015) Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models. Advances in Artificial Neural Systems, 2015, Article ID: 521721. http://dx.doi.org/10.1155/2015/521721
[20]
Dalal, S.G., Shirodkar, P.V., Jagtap, T.G., Naik, B.G. and Rao, G.S. (2010) Evaluation of Significant Sources Influencing the Variation of Water Quality of Kandla Creek, Gulf of Katchchh, Using PCA. Environmental Monitoring and Assessment, 163, 49-56. http://dx.doi.org/10.1007/s10661-009-0815-y
[21]
Olsen, R.L., Chappell, R.W. and Loftis, J.C. (2012) Water Quality Sample Collection, Data Treatment and Results Presentation for Principal Components Analysis—Literature Review and Illinois River Watershed Case Study. Water Research, 46, 3110-3122. http://dx.doi.org/10.1016/j.watres.2012.03.028
[22]
CGWB (2008) Report Groundwater Information of Erode District. Tamil Nadu Central Ground Water Board, Chennai.
[23]
WHO (1993) Guideline of Drinking Quality. World Health Organization, Geneva.
[24]
APHA (1998) Standard Methods for the Examination of Water and Wastewater. American Public Health Association, Washington DC.
[25]
BIS (1998) Standards IS: 10500. Bureau of Indian Standards, Manak Bhavan, New Delhi.
[26]
Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J.M. and Fernandez, L. (2000) Temporal Evolution of Groundwater Composition in Alluvial Aquifer (Pisuerga River, Spain) by Principal Component Analysis. Water Research, 49, 359-372. http://dx.doi.org/10.1016/s0043-1354(99)00225-0
[27]
Kim, R.H., Yum, B.W. and Chang, H.W. (2002) Hydrogeochemical and Isotopic Characteristics for Salinization of a Shallow Groundwater in Coastal Area, Youngkwang, Korea. 17th Salt Water Intrusion Meeting, Delft, 6-10 May 2002, 227-237.
[28]
Holland, S.M. (2006) Cluster Analysis. Department of Geology, University of Georgia, Athens.
[29]
Harmon, H. (1976) Modern Factor Analysis. 3rd Edition, University of Chicago Press, Chicago.
[30]
Han, H.G., Chen, Q. and Qiao, J.F. (2011) An Efficient Self-Organizing RBF Neural Network for Water Quality Prediction. Neural Networks, 24, 717-725. http://dx.doi.org/10.1016/j.neunet.2011.04.006
[31]
Witten, I.H., Frank, E. and Hall, M.A. (2011) Data Mining: Practical Machine Learning Tools and Techniques. 3rd Edition, Morgan Kaufmann Publishers, Burlington.
[32]
Petre, R. (2015) Enhancing Forecasting Performance of Naive-Bayes Classifiers with Discretization Techniques. Database Systems Journal, 6, 24-30.
[33]
Benne, R.G., Dhandra, B.V. and Hangarge, M. (2009) Tri-Scripts Handwritten Numeral Recognition: A Novel Approach. Advances in Computational Research, 1, 47-51.
[34]
Dhandra, B.V., Benne, R.G. and Hangarge, M. (2011) Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach. International Journal of Computer Applications, 26, 11-16. http://dx.doi.org/10.5120/3134-4319
[35]
Zhang, G.P. (2000) Neural Networks for Classification: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30, 451-462. http://dx.doi.org/10.1109/5326.897072
[36]
Cao, L., Liu, X., Wang, Z.P. and Zhang, Z. (2013) The Spatial Outlier Mining Algorithm Based on the KNN Graph. Journal of Software, 8, 3158-3165. http://dx.doi.org/10.4304/jsw.8.12.3158-3165
[37]
Sudha, M. and Valarmathi, B. (2014) Rainfall Forecast Analysis Using Rough Set Attribute Reduction and Data Mining Methods. Agris On-Line Papers in Economics and Informatics, 6, 145-154.
[38]
Venkatramanan, S., Chung, S.Y., Ramkumar, T., Gnanachandrasamy, G. and Vasudevan, S. (2013) A Multivariate Statistical Approaches on Physicochemical Characteristics of Ground Water in and around Nagapattinam District, Cauvery Deltaic Region of Tamil Nadu, India. Earth Sciences Research Journal, 17, 97-103.
