PMID- 34434831
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210827
IS  - 2215-0161 (Print)
IS  - 2215-0161 (Electronic)
IS  - 2215-0161 (Linking)
VI  - 8
DP  - 2021
TI  - Method for lake eutrophication levels evaluation: TOPSIS-MCS.
PG  - 101311
LID - 10.1016/j.mex.2021.101311 [doi]
LID - 101311
AB  - Monte Carlo simulation (MCS) is applied in the engineering with great fuzziness 
      and uncertainty. Technique for order preference by similarity to an ideal 
      solution (TOPSIS) method is used to deal with multi-criteria decision-making 
      issue. Membership function is used to determine the membership degree of 
      evaluated index. This paper presents the method for lake eutrophication level 
      evaluation. The developed approach merges MCS method, TOPSIS method and 
      membership function. The evaluated results are consistent with real 
      eutrophication level in Lake Erhai, China. Global sensitivity analysis (GSA) is 
      conducted. Results show that potassium permanganate index (COD(Mn)) displays the 
      highest negative correlation with the evaluated results and Secchi disc (SD) 
      performs the highest positive correlation under different errors in measured 
      data. The novelty of this work are: (1) the application of TOPSIS considers 
      Surface water environmental quality standards and measured data. Besides, the 
      Monte Carlo simulation method is applied to generate a normal distributed dataset 
      to overcome the errors caused by human and equipment in data collection. The 
      approach is utilized in the article, titled "Approach based on TOPSIS and Monte 
      Carlo simulation methods to evaluate lake eutrophication levels" (Lin et al., 
      2020) [1].*Developed approach merges TOPSIS and MCS method.*It can increase the 
      reliability of evaluated result.
CI  - (c) 2021 The Author(s). Published by Elsevier B.V.
FAU - Lin, Song-Shun
AU  - Lin SS
AD  - Department of Civil Engineering, School of Naval Architecture, Ocean, and Civil 
      Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai, 
      Minhang District 200240, China.
FAU - Shen, Shui-Long
AU  - Shen SL
AD  - MOE Key Laboratory of Intelligent Manufacturing Technology, College of 
      Engineering, Shantou University, Shantou, Guangdong 515063, China.
AD  - Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne 
      Institute of Technology (RMIT), Victoria 3001, Australia.
AD  - Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, 
      School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong 
      University, Shanghai 200240, China.
FAU - Zhang, Ning
AU  - Zhang N
AD  - MOE Key Laboratory of Intelligent Manufacturing Technology, College of 
      Engineering, Shantou University, Shantou, Guangdong 515063, China.
FAU - Zhou, Annan
AU  - Zhou A
AD  - Discipline of Civil and Infrastructure, School of Engineering, Royal Melbourne 
      Institute of Technology (RMIT), Victoria 3001, Australia.
LA  - eng
PT  - Journal Article
DEP - 20210318
PL  - Netherlands
TA  - MethodsX
JT  - MethodsX
JID - 101639829
PMC - PMC8374272
OTO - NOTNLM
OT  - Lake eutrophication evaluation
OT  - Membership function
OT  - Monte Carlo simulation
OT  - TOPSIS method
COIS- The authors declare that they have no known competing financial interests or 
      personal relatio The nships that could have appeared to influence the work 
      reported in this paper.
EDAT- 2021/08/27 06:00
MHDA- 2021/08/27 06:01
PMCR- 2021/03/18
CRDT- 2021/08/26 06:16
PHST- 2020/10/08 00:00 [received]
PHST- 2021/03/12 00:00 [accepted]
PHST- 2021/08/26 06:16 [entrez]
PHST- 2021/08/27 06:00 [pubmed]
PHST- 2021/08/27 06:01 [medline]
PHST- 2021/03/18 00:00 [pmc-release]
AID - S2215-0161(21)00104-7 [pii]
AID - 101311 [pii]
AID - 10.1016/j.mex.2021.101311 [doi]
PST - epublish
SO  - MethodsX. 2021 Mar 18;8:101311. doi: 10.1016/j.mex.2021.101311. eCollection 2021.