PMID- 23583753
OWN - NLM
STAT- MEDLINE
DCOM- 20131024
LR  - 20191210
IS  - 1095-8630 (Electronic)
IS  - 0301-4797 (Linking)
VI  - 122
DP  - 2013 Jun 15
TI  - Risk assessment of water quality using Monte Carlo simulation and artificial 
      neural network method.
PG  - 130-6
LID - S0301-4797(13)00167-9 [pii]
LID - 10.1016/j.jenvman.2013.03.015 [doi]
AB  - There is always uncertainty in any water quality risk assessment. A Monte Carlo 
      simulation (MCS) is regarded as a flexible, efficient method for characterizing 
      such uncertainties. However, the required computational effort for MCS-based risk 
      assessment is great, particularly when the number of random variables is large 
      and the complicated water quality models have to be calculated by a 
      computationally expensive numerical method, such as the finite element method 
      (FEM). To address this issue, this paper presents an improved method that 
      incorporates an artificial neural network (ANN) into the MCS to enhance the 
      computational efficiency of conventional risk assessment. The conventional risk 
      assessment uses the FEM to create multiple water quality models, which can be 
      time consuming or cumbersome. In this paper, an ANN model was used as a 
      substitute for the iterative FEM runs, and thus, the number of water quality 
      models that must be calculated can be dramatically reduced. A case study on the 
      chemical oxygen demand (COD) pollution risks in the Lanzhou section of the Yellow 
      River in China was taken as a reference. Compared with the conventional risk 
      assessment method, the ANN-MCS-based method can save much computational effort 
      without a loss of accuracy. The results show that the proposed method in this 
      paper is more applicable to assess water quality risks. Because the 
      characteristics of this ANN-MCS-based technique are quite general, it is hoped 
      that the technique can also be applied to other MCS-based uncertainty analysis in 
      the environmental field.
CI  - Copyright (c) 2013 Elsevier Ltd. All rights reserved.
FAU - Jiang, Yunchao
AU  - Jiang Y
AD  - College of Earth and Environmental Sciences, Lanzhou University, Chengguan 
      District, Lanzhou, Gansu Province, China.
FAU - Nan, Zhongren
AU  - Nan Z
FAU - Yang, Sucai
AU  - Yang S
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20130410
PL  - England
TA  - J Environ Manage
JT  - Journal of environmental management
JID - 0401664
SB  - IM
MH  - Environmental Monitoring
MH  - *Monte Carlo Method
MH  - *Neural Networks, Computer
MH  - *Risk Assessment
MH  - *Water Quality
EDAT- 2013/04/16 06:00
MHDA- 2013/10/25 06:00
CRDT- 2013/04/16 06:00
PHST- 2012/10/02 00:00 [received]
PHST- 2013/02/26 00:00 [revised]
PHST- 2013/03/06 00:00 [accepted]
PHST- 2013/04/16 06:00 [entrez]
PHST- 2013/04/16 06:00 [pubmed]
PHST- 2013/10/25 06:00 [medline]
AID - S0301-4797(13)00167-9 [pii]
AID - 10.1016/j.jenvman.2013.03.015 [doi]
PST - ppublish
SO  - J Environ Manage. 2013 Jun 15;122:130-6. doi: 10.1016/j.jenvman.2013.03.015. Epub 
      2013 Apr 10.