Alpha 2.0
Login Register
» Articles » PMID: 21188505

A Probabilistic Water Quality Index for River Water Quality Assessment: a Case Study

Overview
Journal Environ Monit Assess
Publisher Springer
Specialty Environmental Health
Date 2010 Dec 29
PMID 21188505
Citations 12
Authors
Mohammad Reza Nikoo
Reza Kerachian
Siamak Malakpour-Estalaki
Seyyed Nasser Bashi-Azghadi
Mohammad Mahdi Azimi-Ghadikolaee
Affiliations
Soon will be listed here.
Abstract

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.

Citing Articles

Evaluation of Shallow Groundwater Quality at Regional Scales Using Adaptive Water Quality Indices.

Bretcan P, Tanislav D, Radulescu C, Serban G, Danielescu S, Reid M Int J Environ Res Public Health. 2022; 19(17).

PMID: 36078359 PMC: 9517783. DOI: 10.3390/ijerph191710637.

Assessing the Water Quality of Lake Hawassa Ethiopia-Trophic State and Suitability for Anthropogenic Uses-Applying Common Water Quality Indices.

Lencha S, Tranckner J, Dananto M Int J Environ Res Public Health. 2021; 18(17).

PMID: 34501493 PMC: 8430541. DOI: 10.3390/ijerph18178904.

Development of a groundwater quality index: GWQI, for the aquifers of the state of Bahia, Brazil using multivariable analyses.

Barbosa Filho J, de Oliveira I Sci Rep. 2021; 11(1):16520.

PMID: 34389745 PMC: 8363630. DOI: 10.1038/s41598-021-95912-9.

A fuzzy multi-stakeholder socio-optimal model for water and waste load allocation.

Mooselu M, Nikoo M, Sadegh M Environ Monit Assess. 2019; 191(6):359.

PMID: 31073749 DOI: 10.1007/s10661-019-7504-2.

Application of the index WQI-CCME with data aggregation per monitoring campaign and per section of the river: case study-Joanes River, Brazil.

de Almeida G, de Oliveira I Environ Monit Assess. 2018; 190(4):195.

PMID: 29516191 DOI: 10.1007/s10661-018-6542-5.

References
1.
Almeida C, Quintar S, Gonzalez P, Mallea M . Influence of urbanization and tourist activities on the water quality of the Potrero de los Funes River (San Luis - Argentina). Environ Monit Assess. 2007; 133(1-3):459-65. DOI: 10.1007/s10661-006-9600-3. View
2.
Debels P, Figueroa R, Urrutia R, Barra R, Niell X . Evaluation of water quality in the Chillán River (Central Chile) using physicochemical parameters and a modified water quality index. Environ Monit Assess. 2005; 110(1-3):301-22. DOI: 10.1007/s10661-005-8064-1. View
3.
Ocampo-Duque W, Ferre-Huguet N, Domingo J, Schuhmacher M . Assessing water quality in rivers with fuzzy inference systems: a case study. Environ Int. 2006; 32(6):733-42. DOI: 10.1016/j.envint.2006.03.009. View
4.
Said A, Stevens D, Sehlke G . An innovative index for evaluating water quality in streams. Environ Manage. 2004; 34(3):406-14. DOI: 10.1007/s00267-004-0210-y. View
5.
Ubeyli E . Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals. Neural Netw. 2008; 21(9):1410-7. DOI: 10.1016/j.neunet.2008.08.005. View