Probabilistic Model for Identifying Groundwater Contamination Sources

2003 ◽  
Author(s):  
Ranhao Lin ◽  
Roseanna M. Neupauer
Keyword(s):  
Groundwater Contamination ◽  
Probabilistic Model ◽  
Contamination Sources

A Bayesian-based integrated approach for identifying groundwater contamination sources

2019 ◽  
Vol 579 ◽  
pp. 124160 ◽  
Author(s):  
Xueman Yan ◽  
Weihong Dong ◽  
Yongkai An ◽  
Wenxi Lu
Keyword(s):  
Groundwater Contamination ◽  
Integrated Approach ◽  
Contamination Sources

Combined analyses of chemometrics and kriging for identifying groundwater contamination sources and origins at the Masan coastal area in Korea

2012 ◽  
Vol 67 (5) ◽  
pp. 1373-1388 ◽  
Author(s):  
Tae Hyung Kim ◽  
Sang Yong Chung ◽  
Namsik Park ◽  
Se-Yeong Hamm ◽  
Seung Yeop Lee ◽  
...  
Keyword(s):  
Groundwater Contamination ◽  
Coastal Area ◽  
Contamination Sources

Evaluation of microbial indicators for the determination of bacterial groundwater contamination sources

2005 ◽  
Vol 168 (1-4) ◽  
pp. 157-169 ◽  
Author(s):  
M. Cimenti ◽  
N. Biswas ◽  
J. K. Bewtra ◽  
A. Hubberstey
Keyword(s):  
Groundwater Contamination ◽  
Microbial Indicators ◽  
Contamination Sources

scholarly journals Simultaneous Identification of Number, Location, and Release History of Groundwater Contamination Sources

2021 ◽  
Author(s):  
Jiuhui Li ◽  
Wenxi Lu ◽  
Zhengfang Wu ◽  
Hongshi He
Keyword(s):  
Simulation Model ◽  
Groundwater Contamination ◽  
Surrogate Model ◽  
Solution Process ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Integer Variable ◽  
Contamination Sources ◽  
History Of ◽  
Learning Machine

Abstract In previous studies, a 0-1 mixed integer nonlinear programming optimization model (0-1MINLPOM) could only identify the location and release intensity for groundwater contamination sources (GCSs), and the location of each GCS was regarded as a 0-1 integer variable, selected from several locations determined in advance. However, in actual situations, the locations usually cannot be accurately isolated to a few GCSs and the number of GCSs is often unknown, so 0-1MINLPOM was improved in this study. Based on the estimation that there is a maximum of three GCSs in the study area, an improved 0-1 MINLPOM was established to simultaneously identify the number of GCSs (treated as 0-1 integer variable), the location (treated as integer variable) and release history of GCS (treated as continuous variables). The simulation model was constructed as an equality constraint embedded improved 0-1 MINLPOM. In the improved 0-1 MINLPOM solution process, repeatedly calling the simulation model would have incurred a massive computational load and taken a long time. Thus, a surrogate model based on kriging and extreme learning machine (ELM) was established respectively for the simulation model to avoid this shortcoming. The results show that the accuracy of the kriging surrogate model (Krig-SM) was higher compared with the ELM surrogate model (ELM-SM). The improved 0-1 MINLPOM could identify the number, location, and release history of GCSs simultaneously. The accuracy of identifying the number of GCSs was 100%, and the accuracies of identifying the locations and release history were above 91.67% and 90.14%, respectively.

Sign in / Sign up

Export Citation Format

Share Document