Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation

被引:128
作者
Reed, P [1 ]
Minsker, B [1 ]
Valocchi, AJ [1 ]
机构
[1] Univ Illinois, Dept Civil & Environm Engn, Urbana, IL 61801 USA
关键词
D O I
10.1029/2000WR900232
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
new methodology for sampling plan design has been developed to reduce the costs associated with long-term monitoring of sites with groundwater contamination. The method combines a fate-and-transport model, plume interpolation, and a genetic algorithm to identify cost-effective sampling plans that accurately quantify the total mass of dissolved contaminant. The plume interpolation methods considered were inverse-distance weighting, ordinary kriging, and a hybrid method that combines the two approaches. Application of the methodology to Hill Air Force Base indicated that sampling costs could be reduced by as much as 60% without significant loss in accuracy of the global mass estimates. Inverse-distance weighting was shown to be most effective as a screening tool for evaluating whether more comprehensive geostatistical modeling is warranted. The hybrid method was effective for implementing such a tiered approach, reducing computational time by more than 60% relative to kriging alone.
引用
收藏
页码:3731 / 3741
页数:11
相关论文
共 43 条
[1]   Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems [J].
Aly, AH ;
Peralta, PC .
WATER RESOURCES RESEARCH, 1999, 35 (08) :2415-2425
[2]  
[Anonymous], 1989, GENETIC ALGORITHM SE
[3]  
[Anonymous], 1984, Mathematical Geology
[4]  
ASCE Hydraulics Division, 1990, Journal of Hydraulic Engineering (New York), V116, P633, DOI 10.1061/(ASCE)0733-9429(1990)116:5(633)
[5]  
ASCE Hydraulics Division, 1990, Journal of Hydraulic Engineering (New York), V116, P612, DOI 10.1061/(ASCE)0733-9429(1990)116:5(612)
[6]  
ASTM (American Society for Testing and Materials), 1995, E173995 ASTM
[7]  
Chiles J.P., 1999, WILEY SER PROBAB STA
[8]   A MULTIPLE-OBJECTIVE OPTIMAL EXPLORATION STRATEGY [J].
CHRISTAKOS, G ;
OLEA, RA .
MATHEMATICAL AND COMPUTER MODELLING, 1988, 11 :413-418
[9]   USING GENETIC ALGORITHMS TO SOLVE A MULTIOBJECTIVE GROUNDWATER MONITORING PROBLEM [J].
CIENIAWSKI, SE ;
EHEART, JW ;
RANJITHAN, S .
WATER RESOURCES RESEARCH, 1995, 31 (02) :399-409
[10]   Modeling multispecies reactive transport in ground water [J].
Clement, TP ;
Sun, Y ;
Hooker, BS ;
Petersen, JN .
GROUND WATER MONITORING AND REMEDIATION, 1998, 18 (02) :79-92