PMID- 31203453
OWN - NLM
STAT- MEDLINE
DCOM- 20190822
LR  - 20200225
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Linking)
VI  - 191
IP  - 7
DP  - 2019 Jun 16
TI  - Integrating fuzzy logic with Pearson correlation to optimize contaminant 
      detection in water distribution system with uncertainty analyses.
PG  - 441
LID - 10.1007/s10661-019-7533-x [doi]
AB  - An effective detection algorithm, supervising an online water system, is expected 
      to monitor changes in water quality due to any contamination. However, 
      contemporary event detection methods are often criticized for their high false 
      detection rates as well as for their low true detection rates. This study 
      proposes two new event detection methods for contamination that use 
      multi-objective optimization by investigating the correlation between multiple 
      types of conventional water quality sensors. While the first method incorporates 
      non-dominated sorting genetic algorithm II (NSGA-II) with the Pearson correlation 
      Euclidean distance (PE) method in order to maximize the probability of detection 
      (PD) and to minimize the false alarm rate (FAR), the second method introduces 
      fuzzy logic in order to establish a degree of correlations ranking that replaces 
      the correlation relationship indicator threshold. Optimization is performed by 
      using NSGA-II in the second method. The results of this study show that the 
      incorporation of fuzzy logic with NSGA-II in event detection method have produced 
      better results in event detection. The results also show that both methods detect 
      all true events without producing any false alarm rates. Moreover, an uncertainty 
      analysis on input sensor signals is performed to test the robustness of the fuzzy 
      logic-based event detection method by employing the widely used Monte Carlo 
      simulation (MCS) technique. Four different scenarios of uncertainty are analyzed, 
      in particular, and the findings suggest that the proposed method is very 
      effective in minimizing false alarm rates and maximizing true events detection, 
      and hence, it can be regarded as one of the novel approaches to demonstrate its 
      application in the development of an event detection algorithm.
FAU - Osmani, Shabbir Ahmed
AU  - Osmani SA
AD  - Department of Civil Engineering, Leading University, Ragib Nagar, South Surma, 
      Sylhet, Bangladesh.
FAU - Banik, Bijit Kumar
AU  - Banik BK
AUID- ORCID: 0000-0002-3966-7430
AD  - Department of Civil and Environmental Engineering, Shahjalal University of 
      Science and Technology, Sylhet, Bangladesh. bijit-cee@sust.edu.
FAU - Ali, Hazrat
AU  - Ali H
AD  - Department of Civil Engineering, Chittagong University of Engineering & 
      Technology, Chattogram, 4349, Bangladesh.
LA  - eng
PT  - Journal Article
DEP - 20190616
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
RN  - 0 (Water Pollutants, Chemical)
MH  - Algorithms
MH  - Environmental Monitoring/*methods
MH  - Fuzzy Logic
MH  - *Models, Theoretical
MH  - Monte Carlo Method
MH  - Probability
MH  - Uncertainty
MH  - Water Pollutants, Chemical/*analysis
MH  - Water Quality/*standards
MH  - Water Supply/*standards
OTO - NOTNLM
OT  - Contamination event
OT  - Fuzzy logic
OT  - Monte Carlo simulation
OT  - NSGA-II
OT  - Optimization
OT  - Pearson correlation
EDAT- 2019/06/17 06:00
MHDA- 2019/08/23 06:00
CRDT- 2019/06/17 06:00
PHST- 2018/11/13 00:00 [received]
PHST- 2019/05/10 00:00 [accepted]
PHST- 2019/06/17 06:00 [entrez]
PHST- 2019/06/17 06:00 [pubmed]
PHST- 2019/08/23 06:00 [medline]
AID - 10.1007/s10661-019-7533-x [pii]
AID - 10.1007/s10661-019-7533-x [doi]
PST - epublish
SO  - Environ Monit Assess. 2019 Jun 16;191(7):441. doi: 10.1007/s10661-019-7533-x.