Data-based multiple criteria decision-making model and visualized monitoring of urban drinking water quality

It is important to comprehensively evaluate and monitor urban drinking water quality to ensure a safe and clean drinking water supply. This paper discusses evaluating, analyzing and monitoring of urban drinking water quality and application systematically and proposes a multiple criteria decision-making model, which integrates analytic hierarchy process (AHP), Kullback–Leibler divergence ratio (KLDR) and comprehensive weighted index (CWI) method to evaluate the quality of drinking water comprehensively. AHP method and KLDR are employed to distribute reasonable weight to indices, and CWI method is used to get comprehensive score of multiple criteria system for evaluation. Association analysis is used to find the useful association rules between criteria and drinking water quality. Geographic information system (GIS) technology is employed to show the distribution map of drinking water quality visually. The proposed method is applied to real-time comprehensive evaluation and visualized monitoring of drinking water quality in Shanghai City. The distribution map of drinking water quality based on GIS can provide monitoring and government agencies with an overall assessment and enable them to make better informed decisions. Real-time application shows that the proposed methods are effective for the assessment and monitoring of urban water quality.

[1]  C I Amos,et al.  Entropy‐based information gain approaches to detect and to characterize gene‐gene and gene‐environment interactions/correlations of complex diseases , 2011, Genetic epidemiology.

[2]  T. Breurch,et al.  A simple test for heteroscedasticity and random coefficient variation (econometrica vol 47 , 1979 .

[3]  Kang Rui Expert Evaluation Method for Equipment Support Scheme During Development , 2009 .

[4]  C. Moe,et al.  Global challenges in water, sanitation and health. , 2006, Journal of water and health.

[5]  Bauke de Vries,et al.  Assessing Regional Sustainability Using a Model of Coordinated Development Index: A Case Study of Mainland China , 2014 .

[6]  Xiang Wan,et al.  Application of Association Rules Data Mining in the Determination the Operation Target Values in the Thermal Power Plant , 2014 .

[7]  T. G. Jagtap,et al.  Evaluation of significant sources influencing the variation of water quality of Kandla creek, Gulf of Katchchh, using PCA , 2010, Environmental monitoring and assessment.

[8]  Lazim Abdullah,et al.  A new preference scale mcdm method based on interval-valued intuitionistic fuzzy sets and the analytic hierarchy process , 2016, Soft Comput..

[9]  Wei Chen,et al.  An Optimal Combination Weights Method Considering Both Subjective and Objective Weight Information in Power Quality Evaluation , 2011 .

[10]  M. Lu,et al.  Integrating QFD, AHP and Benchmarking in Strategic Marketing , 1994 .

[11]  Mohsen Rezaei,et al.  Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran , 2014, Environmental Earth Sciences.

[12]  Zone-Ching Lin,et al.  Evaluation of machine selection by the AHP method , 1996 .

[13]  Shahab Araghinejad,et al.  Development of a Multi Criteria Decision Making Tool for a Water Resources Decision Support System , 2015, Water Resources Management.

[14]  K. Rajesh,et al.  Monitoring Water quality of Kosi River in Rampur District,Uttar Pradesh, India , 2011 .

[15]  C. K. Kwong,et al.  A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment , 2002, J. Intell. Manuf..

[16]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[17]  D. Chakraborti,et al.  Arsenic Groundwater Contamination and Sufferings of People in North 24-Parganas, One of the Nine Arsenic Affected Districts of West Bengal, India , 2003, Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering.

[18]  J. Jakumeit,et al.  Parameter optimization of the sheet metal forming process using an iterative parallel Kriging algorithm , 2005 .

[19]  Sai Peck Lee,et al.  Functional and non-functional requirements prioritization: empirical evaluation of IPA, AHP-based, and HAM-based approaches , 2015, Soft Computing.

[20]  Ming-Chyuan Lin,et al.  Using AHP and TOPSIS approaches in customer-driven product design process , 2008, Comput. Ind..

[21]  Mehdi Fasanghari,et al.  An intuitionistic fuzzy group decision making method using entropy and association coefficient , 2012, Soft Computing.

[22]  R. Pavanello,et al.  Assessment of surface water quality by a single index of pollution , 1971 .