Bayesian deduction for redundancy detection in groundwater quality monitoring networks

Water Resources Research

Volumn 44, Issue 8, 2008, Pages

  Ammar, Khalil ^a   Khalil, Abedalrazq ^b   McKee, Mac ^c   Kaluarachchi, Jagath ^a  

^a Utah State University   (United States)

^b Columbia University ^*  (United States)

^c UTAH STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GROUNDWATER; HYDROGEOLOGY; MATHEMATICAL MODELS; NETWORK PROTOCOLS; NITRATES; QUALITY ASSURANCE; REDUNDANCY; RELIABILITY; SENSOR NETWORKS; UNDERGROUND RESERVOIRS; WATER QUALITY;

BAYESIAN; CASE STUDIES; GROUND WATER QUALITY; GROUNDWATER QUALITY MONITORING; INFORMATION CONTENTS; MODEL PARAMETERS; NETWORK CONFIGURATIONS; PROBABILISTIC PREDICTIONS; RELEVANCE VECTOR MACHINE; SPARSE BAYESIAN LEARNING;

BAYESIAN NETWORKS;

ACCURACY ASSESSMENT; AQUIFER; BAYESIAN ANALYSIS; NITRATE; UNCERTAINTY ANALYSIS; WATER QUALITY;

EID: 53849085165     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/2006WR005616     Document Type: Article

References (19)

1. Asefa, T., M. Kemblowski, G. Urroz, M. McKee, and A. Khalil (2004), Support vectors-based groundwater head observation networks design, Water Resour. Res., 40, W11509, doi:10.1029/2004WR003304.
2. Asefa, T., M. Kemblowski, G. Urroz, and M. McKee (2005), Support vector machines (SVMs) for monitoring network design, Ground Water, 43, 413-422, doi:10.1111/j.1745-6584.2005.0050.x.
3. CH2MHILL (2001), Aquifer modeling, in Water Resources Program Phase III, Task 2, pp. 31-68, U.S. Agency for Int. Dev., Washington, D. C.
4. Gill, M. K., Y. H. Kaheil, A. Khalil, M. McKee, and L. Bastidas (2006), Multiobjective particle swarm optimization for parameter estimation in hydrology, Water Resour. Res., 42, W07417, doi:10.1029/2005WR004528.
5. Harmancioglu, N. B., O. Fistikoglu, S. D. Ozkul, V. P. Singh, and M. N. Alpaslan (1999), Water Quality Monitoring Network Design, Water Sci. Technol. Libr., vol. 33, 304 pp., Kluwer Acad., Dordrecht, Netherlands.
6. Khalil, A., M. N. Almasri, M. McKee, and J. J. Kaluarachchi (2005a), Applicability of statistical learning algorithms in groundwater quality modeling, Water Resour. Res., 41, W05010, doi:10.1029/2004WR003608.
7. Khalil, A., M. McKee, M. Kemblowski, and T. Asefa (2005b), Sparse Bayesian learning machine for real-time management of reservoir releases, Water Resour. Res., 41, W11401, doi:10.1029/2004WR003891.
8. Khalil, A., M. McKee, M. Kemblowski, T. Asefa, and L. Bastidas (2006), Multiobjective analysis of chaotic dynamic systems with sparse learning machines, Adv. Water Resour., 29(1), 72-88, doi:10.1016/j.advwatres.2005. 05.011.
9. Legates, D. R., and G. J. McCabe Jr. (1999), Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35(1), 233-241, doi:10.1029/1998WR900018.
10. Loaiciga, H., R. J. Charbeneau, L. G. Everett, G. E. Fogg, B. F. Hobbs, and S. Rouhani (1992), Review of ground-water quality monitoring network design, J. Hydraul. Eng., 118(1), 11-37, doi:10.1061/(ASCE)0733-9429(1992)118: 1(11).
11. Mogheir, Y., and V. P. Singh (2002), Application of information theory to groundwater quality monitoring networks, Water Resour. Manage., 16, 37-49, doi:10.1023/A:1015511811686.
12. Nunes, L. M., M. C. Cunha, and L. Ribeiro (2004), Groundwater monitoring network optimization with redundancy reduction, J. Water Resour. Plann. Manage., 130(1), 33-43.
13. Palestinian Central Bureau of Statistics (1997), Results of population census of December 1997 in the West Bank and Gaza Strip, Ramallah, West Bank.
14. Reed, P. M., and B. S. Minsker (2004), Striking the balance: Long-term groundwater monitoring design for conflicting objectives, J. Water Resour. Plann. Manage., 130(2), 140-149, doi:10.1061/(ASCE)0733-9496(2004)130:2(140) .
15. Reed, P., B. Minsker, and A. J. Valocchi (2000), Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation, Water Resour. Res., 36(12), 3731-3741, doi:10.1029/2000WR900232.
16. Reed, P. M., B. S. Minsker, and D. E. Goldberg (2001), A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data, J. Hydroinf., 3(2), 71-89.
17. Shawe-Tylor, J., and N. Cristiani (2004), Kernel Methods for Pattern Analysis, 476 pp., Cambridge Univ. Press, Cambridge, U. K.
18. Tipping, M. E. (2001), Sparse Bayesian learning and the relevance vector machine, J. Mach. Learning Res., 1, 211-244, doi:10.1162/ 15324430152748236.
19. Yan, S., and B. Minsker (2006), Optimal groundwater remediation design using an Adaptive Neural Network Genetic Algorithm, Water Resour. Res., 42, W05407, doi:10.1029/2005WR004303.