PMID- 28363342
OWN - NLM
STAT- MEDLINE
DCOM- 20170814
LR  - 20181202
IS  - 1873-6009 (Electronic)
IS  - 0169-7722 (Linking)
VI  - 200
DP  - 2017 May
TI  - Chance-constrained multi-objective optimization of groundwater remediation design 
      at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.
PG  - 15-23
LID - S0169-7722(16)30084-5 [pii]
LID - 10.1016/j.jconhyd.2017.03.004 [doi]
AB  - In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) 
      method is proposed as a multi-objective optimization solver. It was implemented 
      in the multi-objective optimization of a groundwater remediation design at sites 
      contaminated by dense non-aqueous phase liquids. In this study, there were two 
      objectives: minimization of the total remediation cost, and minimization of the 
      remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was 
      adopted to compare with the proposed method. For efficiency, the time-consuming 
      surfactant-enhanced aquifer remediation simulation model was replaced by a 
      surrogate model constructed by a multi-gene genetic programming (MGGP) technique. 
      Similarly, two other surrogate modeling methods-support vector regression (SVR) 
      and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the 
      surrogate-modeling uncertainty was incorporated in the optimization model by 
      chance-constrained programming (CCP). The results showed that, for the problem 
      considered in this study, (1) the solutions obtained by AMALGAM incurred less 
      remediation cost and required less time than those of NSGA-II, indicating that 
      AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP 
      surrogate model was more accurate than SVR and KRG; and (3) the remediation cost 
      and time increased with the confidence level, which can enable decision makers to 
      make a suitable choice by considering the given budget, remediation time, and 
      reliability.
CI  - Copyright (c) 2017 Elsevier B.V. All rights reserved.
FAU - Ouyang, Qi
AU  - Ouyang Q
AD  - Key Laboratory of Groundwater Resources and Environment, Ministry of Education, 
      Jilin University, Changchun 130021, PR China; College of Environment and 
      Resources, Jilin University, Changchun 130021, PR China.
FAU - Lu, Wenxi
AU  - Lu W
AD  - Key Laboratory of Groundwater Resources and Environment, Ministry of Education, 
      Jilin University, Changchun 130021, PR China; College of Environment and 
      Resources, Jilin University, Changchun 130021, PR China. Electronic address: 
      luwenxi@jlu.edu.cn.
FAU - Hou, Zeyu
AU  - Hou Z
AD  - Key Laboratory of Groundwater Resources and Environment, Ministry of Education, 
      Jilin University, Changchun 130021, PR China; College of Environment and 
      Resources, Jilin University, Changchun 130021, PR China.
FAU - Zhang, Yu
AU  - Zhang Y
AD  - Institute of Water Environmental Sciences of Songliao, Fujin Road 11-16, 
      Changchun 130000, PR China.
FAU - Li, Shuai
AU  - Li S
AD  - School of Architecture and Civil Engineering, Xi'an University of Science and 
      Technology, Xi'an, 710054, PR China.
FAU - Luo, Jiannan
AU  - Luo J
AD  - Key Laboratory of Groundwater Resources and Environment, Ministry of Education, 
      Jilin University, Changchun 130021, PR China; College of Environment and 
      Resources, Jilin University, Changchun 130021, PR China.
LA  - eng
PT  - Journal Article
DEP - 20170314
PL  - Netherlands
TA  - J Contam Hydrol
JT  - Journal of contaminant hydrology
JID - 8805644
RN  - 0 (Surface-Active Agents)
RN  - 0 (Water Pollutants, Chemical)
SB  - IM
MH  - *Algorithms
MH  - Artificial Intelligence
MH  - Computer Simulation
MH  - Environmental Restoration and Remediation/*methods
MH  - *Groundwater
MH  - Hydrology/*methods
MH  - Models, Theoretical
MH  - Regression Analysis
MH  - Reproducibility of Results
MH  - Surface-Active Agents
MH  - *Water Pollutants, Chemical
OTO - NOTNLM
OT  - Chance-constrained programming
OT  - Groundwater remediation
OT  - Multi-algorithm method
OT  - Multi-gene genetic programming
OT  - Multi-objective optimization
OT  - Surrogate model
EDAT- 2017/04/02 06:00
MHDA- 2017/08/15 06:00
CRDT- 2017/04/02 06:00
PHST- 2016/06/06 00:00 [received]
PHST- 2017/02/26 00:00 [revised]
PHST- 2017/03/13 00:00 [accepted]
PHST- 2017/04/02 06:00 [pubmed]
PHST- 2017/08/15 06:00 [medline]
PHST- 2017/04/02 06:00 [entrez]
AID - S0169-7722(16)30084-5 [pii]
AID - 10.1016/j.jconhyd.2017.03.004 [doi]
PST - ppublish
SO  - J Contam Hydrol. 2017 May;200:15-23. doi: 10.1016/j.jconhyd.2017.03.004. Epub 
      2017 Mar 14.