Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater

Environmental Monitoring and Assessment

Volumn 186, Issue 6, 2014, Pages 3685-3699

  Hosseini, Seiyed Mossa ^a   Mahjouri, Najmeh ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial neural network; Fuzzy set theory; Genetic algorithm; Groundwater quality; Nitrate concentration; Support vector regression

Indexed keywords

AQUIFERS; BENCHMARKING; FUZZY NEURAL NETWORKS; FUZZY SET THEORY; GROUNDWATER RESOURCES; NEURAL NETWORKS; REGRESSION ANALYSIS; WATER QUALITY;

ASYMMETRIC TRIANGULAR FUZZY; BETTER PERFORMANCE; GAUSSIAN KERNEL FUNCTIONS; GROUND-WATER QUALITIES; MULTI LAYER PERCEPTRON; NITRATE CONCENTRATION; SUPPORT VECTOR REGRESSION (SVR); SUPPORT VECTOR REGRESSION MODELS;

GENETIC ALGORITHMS;

GROUND WATER; NITRATE;

AQUIFER; ARTIFICIAL NEURAL NETWORK; BENCHMARKING; CONCENTRATION (COMPOSITION); FUZZY MATHEMATICS; GENETIC ALGORITHM; NITRATE; OPTIMIZATION; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CODING; CONCENTRATION (PARAMETERS); FUZZY NEURAL NETWORK BASED SUPPORT VECTOR REGRESSION; GENETIC ALGORITHM; IRAN; KERNEL METHOD; LINEAR SYSTEM; MACHINE LEARNING; PERCEPTRON; PROGRAM EFFICACY; SAMPLING BIAS; WEIGHT;

IRAN;

ALGORITHMS; ENVIRONMENTAL MONITORING; FUZZY LOGIC; GROUNDWATER; IRAN; NEURAL NETWORKS (COMPUTER); NITRATES; WATER POLLUTANTS, CHEMICAL; WATER POLLUTION, CHEMICAL;

EID: 84900823209     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-014-3650-8     Document Type: Article

References (73)

1. Alvisi, S., & Franchini, M. (2011). Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environmental Modelling & Software, 26, 523-537.
2. Aqil, M., Kita, I., Yano, A., & Nishiyama, S. (2007). A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. Journal of Hydrology, 337, 22-34. (Pubitemid 46483469)
3. Asefa, T., & Kemblowski, M. W. (2002). Support vector machines approximation of flow and transportmodels in initial groundwater contamination network design. EOS. Transactions of the American Geophysical Union, Fall Meeting 2002, abstract #H72D-0882.
4. Asefa, T., Kemblowski, M. W., Urroz, G., McKee, M., & Khalil, A. (2004). Support vectors-based groundwater head observation networks design. Water Resources Research. doi:10.1029/2004WR003304.
5. Asefa, T., Kemblowski, M. W., Lall, U., & Urroz, G. (2005). Support vectormachines for nonlinear state space reconstruction: application to the Great Salt Lake time series. Water Resources Research. doi:10.1029/2004WR003785.
6. Asefa, T., Kemblowski, M., McKee, M., & Khalil, A. (2006). Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology, 318, 7-16. (Pubitemid 43120784)
7. Babovic, V., Keijzer, M., & Bundzel, M. (2000). From global to local modelling: a case study in error correction of deterministic models, hydroinformatics. USA: Iowa Institute of Hydraulic Research, Iowa.
8. Behzad, M., Asghari, K., & Coppola, E. A. (2010). Comparative study of SVMs and ANNs in aquifer water level prediction. Journal of Computing in Civil Engineering ASCE, 24, 408-413.
9. Cawley, G. C., & Talbot, N. L. C. (2004). Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks, 17(10), 1467-1475. (Pubitemid 39487142)
10. Chang, F. J., & Chen, L. (2004). Real-coded genetic algorithm for rule-based flood control reservoir management. Water Resources Management, 12(3), 185-198.
11. Cherkassky, V., & Yunqian, M. Y. (2002). Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks, 17, 113 - 126. doi:10.1016/S0893-6080(03)00169-2.
12. Chuang, C. C. (2008). Extended support vector interval regression networks for interval input - output data. Information Sciences, 178, 871-891.
13. Dahiy, S., Singh, B., Gaur, S., Garg, V. K., & Kushwaha, H. S. (2007). Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials, 147, 938-946. (Pubitemid 47126721)
14. Dibike, Y. B. (2000). Machine learning paradigms for rainfall-runoff modeling, hydroinformatics. USA: Iowa Institute of Hydraulic Research, Iowa.
15. Dibike, Y. B., Velickov, S., Solomatine, D., & Abbott, M. B. (2001). Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering, 15(3), 208-216. (Pubitemid 32583199)
16. Gill, M. K., Asefa, T., Kemblowski, M. W., & McKee, M. (2006). Soil moisture prediction using support vector machines. Journal of the American Water Resources Association, 42(4), 1033-1046. (Pubitemid 44870396)
17. Gill, M. K., Asefa, T., Kaheil, Y., & McKee, M. (2007). Effect of missing data on performance of learning algorithms for hydrologic predictions: implications to an imputation technique. Water Resources Research. doi:10.1029/2006WR005298.
18. Goldberg, D. E. (1989). A comparative analysis of selection scheme used in genetic algorithms. Foundations of genetic algorithms (pp. 69-93). San Mateo: Morgan Kaufman.
19. Han, D., Chan, L., & Zhu, N. (2007). Flood forecasting using support vector machines. Journal of Hydroinformatics, 9(4), 267-276.
20. Hao, P. Y., & Chiang, J. H. (2007). A fuzzy model of support vector regression machine. International Journal of Fuzzy Systems, 9(1), 45-50.
21. Hao, P. Y., & Chiang, J. H. (2008). Fuzzy regression analysis by support vector learning approach. IEEE Transactions on Fuzzy Systems, 16(2), 428-441.
22. Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66-72.
23. Hong, D. H., & Hwang, C. (2003a). Support vector fuzzy regression machines. Fuzzy Sets and Systems, 138(2), 271-281.
24. Hong, D. H., & Hwang, C. (2003b). Support vector fuzzy regression machines. Fuzzy Sets and Systems, 138, 271-281.
25. Hua, Z. S., & Zhang, B. (2006). A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Applied Mathematics and Computation, 181, 1035-1048.
26. Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics (p. 561). Oxford: Oxford University Press.
27. Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence. USA, NJ: Prentice Hall.
28. Jeng, J.T., & Lee, T.T. (1999). Support vector machines for the fuzzy neural networks, In: Proc. IEEE International Conference on System, Man and Cybernetics (IEEE SMC'99), 115-120.
29. Jeng, J. T., Chuang, C. C., & Su, S. F. (2003). Support vector interval regression networks for interval regression analysis. Fuzzy Sets and Systems, 138, 283-300.
30. Jiang, B. T., & Zhao, F. Y. (2013). Combination of support vector regression and artificial neural networks for prediction of critical heat flux. International Journal of Heat and Mass Transfer, 62, 481-494.
31. Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. San Francisco: New York Academic Press.
32. Juang, C. F., & Hsieh, C. D. (2009). TS-fuzzy system-based support vector regression. Fuzzy Sets and Systems, 160, 2486-2504.
33. Kacprzyk, J., & Fedrizzi, M. (1992). Fuzzy regression analysis. Heidelberg: Physics-Verlag.
34. Kalteh, A. M. (2013). Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Computers & Geosciences, 54, 1-8.
35. Karatzoglou, A., Meyer, D., & Hornik, K. (2006). Support vector machine in R. Journal of Statistical Software, 15(9), 1-28.
36. Kazemi, S. M., & Hosseini, S. M. (2011). Comparison of spatial interpolation methods for estimating heavy metals in sediments of Caspian Sea. Expert Systems with Applications, 38, 1632-1649.
37. Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks and fuzzy logic models. Cambridge, MA: The MIT Press.
38. Khan, M. S., & Coulibaly, P. (2006). Application of support vector machine in lake water level prediction. Journal of Hydrologic Engineering, 11, 199-205.
39. Kholghi, M., & Hosseini, S. M. (2009). Comparison of groundwater level estimation using neuro-fuzzy and ordinary Kriging. Environmental Modeling and Assessment. doi:10. 1007/s10666-008-9174-2.
40. Krishna, B., Satyaji, R. Y. R., & Vijaya, T. (2008). Modelling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrological Processes, 22, 1180 -1188.
41. Krzysztofowicz, R. (1999). Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research, 35(9), 2739-2750.
42. Kumar V., & Schuhmacher M. (2005). Fuzzy uncertainty analysis in system modeling. European Symposium on Computer Aided Process Engineering, 391-396.
43. Kumar, M., & Yadav, N. (2011). Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: a survey. Computers and Mathematics with Applications, 62, 3796-3811.
44. Lin, K. P., & Pai, P. F. (2010). A fuzzy support vector regression model for business cycle predictions. Expert Systems with Applications, 37, 5430-5435.
45. Liong, S. Y., & Sivapragasam, C. (2002). Flood stage forecasting with support vectormachines. Journal of the American Water Resources Association, 38(1), 173-196.
46. Ma, T. S., Sophocleous, M., & Yu, Y. S. (1999). Geostatistical applications in ground-water modeling in south-central Kansas. Journal of Hydrologic Engineering, 4(1), 57-64. (Pubitemid 29154375)
47. Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modeling and Software, 101-124. (Pubitemid 30018318)
48. Morshed, J., & Kaluarachchi, J. J. (2012). Parameter estimation using artificial neural network and genetic algorithm for free-product migration and recovery. Water Resources Research, 34(5), 1101-1113.
49. Nikoo, M. R. & Mahjouri, N. (2013) Water quality zoning using probabilistic support vector machines: two case studies, Water Resources Management, Springer, 27, 2577-2594
50. Ovaska, S. J. (2005). Computationally intelligent hybrid systems. Piscataway, New Jersey: Wiley-IEEE Press.
51. Rogers, D. (1996). Some theory and examples of genetic function approximation with comparison to evolutionary techniques. In J. Devillers (Ed.), Genetic algorithms in molecular modeling (pp. 87-107). London: Academic Press.
52. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: MIT Press. 626.
53. Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., & Murthy, K. R. K. (2000). Improvements to the SMO algorithm for SVM regression. IEEE Trans on Neural Netw, 11, 1188-1193.
54. Shrestha, D. L., & Solomatine, D. P. (2006). Experiments with AdaBoost. RT, an improved boosting scheme for regression. Neural Computation, 18, 1678-1710. (Pubitemid 44024734)
55. Sivapragasam, C., Liong, S. Y., & Pasha, M. F. K. (2001). Rainfall and runoff forecasting with SSA-SVM approach. Journal of Hydroinformatics, 3(3), 141-152.
56. Szidarovszky, F., Coppola, E. A., Long, J., Hall, A. D., & Poulton, M. M. (2007). A hybrid artificial neural network-numerical model for ground water problems. Ground Water, 45(5), 590-600. (Pubitemid 47301552)
57. Tanaka, H., Uejima, S., & Asai, K. (1982). Linear regression analysis with fuzzy model. IEEE Transactions on Systems Man and Cybernetics, 12(6), 903-907. (Pubitemid 13493493)
58. Task Committee, A. S. C. E. (2000). Artificial neural networks in hydrology. II: hydrologic applications. Journal of Hydrologic Engineering, ASCE, 5(2), 124-137.
59. Tutmez, B., Hatipoglu, Z., & Kaymak, U. (2006). Modelling electrical conductivity of groundwater using an adaptive neurofuzzy inference system. Computers & Geosciences, 32, 421-433. (Pubitemid 43380877)
60. Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer-Verlag.
61. Vapnik, V. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, l0(5), 988-999.
62. Varma, M., & Babu, B. R. (2009). More generality in efficient multiple kernel learning. In proceedings of the 26th annual international conference on machine learning (ICML) (pp. 1065-1072). New York: ACM.
63. Ward, M. H., deKok, T. M., Levallois, P., Brender, J., Gulis, G., Nolan, B. T., et al. (2005). Workgroup report: drinking-water nitrate and health -recent findings and research needs. Environmental Health Perspectives, 113(11), 1607-1614. (Pubitemid 41614919)
64. Winneberger, J.H. (1982). Nitrogen, Public health and the environment. Ann Arbor, Michigan: Ann Arbor Science publishers, Inc.
65. Winston, W. L., & Venkataramanan, M. (2003). Introduction to Mathematical Programming. Pacific Grove: Brooks/Cole.
66. Wu, Q. (2011). The complex fuzzy system forecasting model based on triangular fuzzy robust wavelet m-support vector machine. Expert Systems with Applications, 38, 14478-14489.
67. Wu, C. L., & Chau, K. W. (2006). A flood forecasting neural network model with genetic algorithm. International Journal of Environment and Pollution, 28(3/4), 261-272.
68. Yao, C. C., & Yu, P. T. (2006). Fuzzy regression based on asymmetric support vector machines. Applied Mathematics and Computation, 182, 175-193.
69. Yen, K. K., Goshray, S., & Roig, G. (1999). A linear regression model using triangular fuzzy number coefficients. Fuzzy Sets and Systems, 106, 166-177.
70. Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., & Lee, K. K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology, 396, 128-138.
71. Yu, X. Y., & Liong, S. Y. (2007). Forecasting of hydrology time series with ridge regression in feature space. Journal of Hydrology, 332(3-4), 290-302.
72. Yu, P. S., Chen, S. T., & Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3-4), 704-716.
73. Zahraie, B., & Hosseini, S. M. (2009). Development of reservoir operation policies considering variable agricultural water demands. Expert Systems with Applications, 36, 4980-4987.