Bayesian Method for Groundwater Quality Monitoring Network Analysis

Journal of Water Resources Planning and Management

Volumn 137, Issue 1, 2010, Pages 51-61

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

^a INTERNATIONAL CENTER FOR BIOSALINE AGRICULTURE   (United Arab Emirates)

^b UTAH STATE UNIVERSITY   (United States)

^c Utah State University   (United States)

Author keywords

Bayesian analysis; Groundwater; Monitoring; Pollution; Relevance vector machine; Value of information

Indexed keywords

BAYESIAN ANALYSIS; BAYESIAN METHODS; CONCEPTUAL FRAMEWORKS; CONTAMINANT DISTRIBUTIONS; DISABILITY ADJUSTED LIFE YEARS; ENVIRONMENTAL HEALTH; EXPECTED VALUES; GROUNDWATER QUALITY MONITORING; HEALTH EFFECTS; INFORMATION CONTENTS; MONITORING INFORMATION; MONITORING NETWORK; MONITORING SITES; MONITORING SYSTEM; NITRATE CONTAMINATION; PALESTINE; PROBABILITY MAPS; RELEVANCE VECTOR MACHINE; RISK CONSEQUENCES; SAMPLE INFORMATION; VALUE OF INFORMATION;

BAYESIAN NETWORKS; ELECTRIC NETWORK ANALYSIS; GROUNDWATER; GROUNDWATER POLLUTION; HEALTH; MONITORING; POLLUTION; PROBABILITY DISTRIBUTIONS; QUALITY ASSURANCE; QUALITY CONTROL; REDUNDANCY; RISK ASSESSMENT; VECTORS; WATER QUALITY;

HEALTH RISKS;

BAYESIAN ANALYSIS; CONCEPTUAL FRAMEWORK; ESTIMATION METHOD; GROUNDWATER; HEALTH RISK; MONITORING SYSTEM; NITRATE; RISK ASSESSMENT; WATER POLLUTION; WATER QUALITY;

OCCUPIED TERRITORIES; WEST BANK;

EID: 78650274493     PISSN: 07339496     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)WR.1943-5452.0000043     Document Type: Article

References (23)

1. Aller L. Bennett T. Lehr J.H. Petty R.J. DRASTIC: A standardized system for evaluating groundwater pollution potential using hydrogeologic settings. 1987, and Rep. No.EPA/600/2-85/0108, U.S. EPA, Robert S. Kerr Environmental Research Laboratory, Ada, Okla.
2. Ammar A.K. A Bayesian method for groundwater quality monitoring network analysis. 2007, Dissertation, Utah State Univ., Salt Lake City, Utah.
3. Ammar K. Khalil A. McKee M. Kaluarachchi J. Bayesian deduction for redundancy detection in groundwater quality monitoring networks. Water Resour. Res. 2008, 44:W08412. WRERAQ, 0043-1397, 10.1029/2006WR005616, and.
4. Bates S. Cullen A. Raftery A. Bayesian uncertainty assessment in multicompartment deterministic simulation models for environmental risk assessment. Environmetrics 2003, 14:355-371. ENVCEE, 1180-4009, 10.1002/env.590, and.
5. Aquifer modeling. Water resources program phase III 2001, CH2M HILL, USAID, Washington, D.C.
6. Fewtrell L. Drinking-water nitrate, methemoglobinemia, and global burden of disease: A discussion. Environ. Health Perspect. 2004, 112(14):1371-1374. EVHPAZ, 0091-6765, 10.1289/ehp.7216.
7. Harmancioglu N.B. Fistikoglu O. Ozkul S.D. Singh V.P. Alpaslan M.N. Water science and technology library: Water quality monitoring network design 1999, and Kluwer Academic, Dordrecht, The Netherlands.
8. Khadam I. Kaluarachchi J. Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated groundwater. Environ. Impact. Asses. Rev. 2003, 23:683-721. EIARDK, 0195-9255, 10.1016/S0195-9255(03)00117-3, and.
9. Khalil A. Almasri M. McKee M. Kaluarachchi J. Applicability of statistical learning algorithms in groundwater quality modeling. Water Resour. Res. 2005a, 41:W05010. WRERAQ, 0043-1397, 10.1029/2004WR003608, and.
10. Khalil A. McKee M. Kemblowski M. Asefa T. Sparse Bayesian learning machine for real-time management of reservoir releases. Water Resour. Res. 2005b, 41(11):W11401. WRERAQ, 0043-1397, 10.1029/2004WR003891, and.
11. Khalil A. McKee M. Kemblowski M. Asefa T. Bastidas L. Multiobjective analysis of chaotic dynamic systems with sparse learning machines. Adv. Water Resour. 2006, 29(1):72-88. AWREDI, 0309-1708, 10.1016/j.advwatres.2005.05.011, and.
12. Kim J. Bridges T. Risk, uncertainty, and decision analysis applied to the management of aquatic nuisance species. 2006, and ERDC/TN ANSRP-06-1, Aquatic Nuisance Species Research Program, Washington, D.C.
13. Lopez A. Mathers C. Ezzati M. Jamison D. Murray C. Global burden of disease and risk factors 2006, 10.1596/978-0-8213-6262-4, and The International Bank for Reconstruction and Development/World Bank, Washington, D.C.
14. Results of population census of December 1997 in the West Bank and Gaza Strip, 1998 2002, Palestinian Center Bureau of Statistics (PCBS), Ramallah, Palestine.
15. Tappenden P. Chilcott J. Eggington S. Oakley J. MaCabe C. Methods for expected value of information analysis in complex health economic models: Developments on the health economics of interferon-β and glatiramer acetate for multiple sclerosis. Health Technol. Assess 2004, 8(27):1-92. ZZZZZZ, 1366-5278, and.
16. Thayananthan A. Navaratnam R. Stenger B. Torr P. Cilpolla R. Multivariate relevance vector machines for tracking. Proc., European Conf. on Computer Vision 2006, and Dublin, Ireland.
17. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 2001, 1:211-244. ZZZZZZ, 1532-4435, 10.1162/15324430152748236.
18. Tipping M.E. Faul A.C. Fast marginal likelihood maximisation for sparse Bayesian models. Proc., 9th Int. Workshop on Artificial Intelligence and Statistic 2003, C. M. Bishop, B. J. Frey, and and eds., Key West, Fla.
19. Tripathi S. Govindaraju R.S. On selection of kernel parameters in relevance vector machines for hydrologic applications. Stochastic Environ. Res. Risk Assess. 2006, 21(6):747-764. 1436-3240, and.
20. Risk assessment guidance for superfund: Volume III-Part A, Process for conducting probabilistic risk assessment. 2001, U.S. EPA, EPA 540-R-02-002 OSWER 9285.7-45 PB2002 963302, Washington, D.C.
21. Health criteria and other supporting information. Guidelines for drinking water quality 1996, World Health Organization (WHO), 2nd. Ed. Vol. 2, Geneva, Switzerland.
22. Yokota F. Thompson K. Value of information analysis in environmental health risk management decisions: Past, present, and future. Risk Anal. 2004, 24(3):635-650. RIANDF, 0272-4332, 10.1111/j.0272-4332.2004.00464.x, and.
23. Zhang H. Malik J. Selecting shape features using multi-class relevance vector machines. 2005, and Technical Rep. No.UCB/EECS-2005-6, EECS, University of California, Berkeley, Berkeley, Calif.