Groundwater quality ranking for drinking purposes, using the entropy method and the spatial autocorrelation index

Gorgij, Alireza Docheshmeh; Kisi, Özgür; Moghaddam, Asghar Asghari; Taghipour, A A

doi:10.1007/s12665-017-6589-6

Cited by 134 publications

(44 citation statements)

References 21 publications

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“…In this method, m indicators and n samples are set in the evaluation, and the measured value of the ith indicator in the jth sample is recorded as x ij . e first step is the standardization of measured values [16,17]. e standardized value of the ith index in the jth sample is denoted as p ij , and its calculation method is as follows:…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of Entropy Weight Method in Decision-Making

Zhu

Tian²,

Feng

2020

Mathematical Problems in Engineering

290

145

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Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.

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Section: Methodsmentioning

confidence: 99%

Effectiveness of Entropy Weight Method in Decision-Making

Zhu

Tian²,

Feng

2020

Mathematical Problems in Engineering

290

145

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“…The calculated entropy weights were used by us to construct the entropy weighted index and to characterize the wastewater composition. A similar approach has been already used for the ground water quality assessment [26,27,31]. This is a simple way to describe complex water systems by one parameter.…”

Section: Entropy Weighted Indexmentioning

confidence: 99%

“…The concept has been used also for the analysis of urban ecosystems [17], environmental analysis [3,[18][19][20], medical records [21][22][23] and in scientometrics [24,25]. Several papers focusing on river and groundwater quality assessment have been published as well [26][27][28][29][30][31]. However, to date no papers have been published on the topic of wastewaters evaluation.…”

Section: Introductionmentioning

confidence: 99%

Information Entropy for Evaluation of Wastewater Composition

Praus

2020

Water

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The composition of wastewaters collected during one year was evaluated based on the Shannon information entropy. Eleven physico-chemical parameters, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), total suspended solids (TSS), total dissolved salts (TDS), pH, ammonium, phosphate, cyanide and phenol, were determined for their characterization. Entropy of the parameters calculated by means of their histograms decreased in the order: phosphate > ammonium > TDS > TN > pH > BOD > COD > TSS > TP > phenol > cyanide. Entropy weights of the parameters were calculated for the evaluation of wastewater composition by means of the entropy weighted index (EWI) defined according to the simple additive weighting (SAW) model. The EWI values were statistically processed by us to observe temporal wastewater composition changes and were verified by means of the principal component weighted index (PCWI). The EWI values were statistically analyzed by univariate statistics. The outlaying samples were also confirmed by multivariate analysis. The entropy-based approach allowed us to simply evaluate wastewater composition by means of one index instead of several parameters. The main advantage of EWI is the simple histogram-based calculation of entropy with no need of the normal distribution of the used parameters.

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“…Geographical information system (GIS) was used in the study area to analyze the spatial distribution of the groundwater quality index [2]. The World Health Organization (WHO) guidelines for a range of drinking water indices have been established [3]. Multi-Criteria Decision-Making (MCDM) methods are considered to be effective techniques in diverse areas, such as; contractors assessment, projects management, products selection, construction of roads, etc.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Assessment Using Feature Extraction Algorithm Combined with Complex Proportional Assessment Method and Standard Deviation

Shaaban¹

2021

IJIES

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Groundwater (GW) quality evaluation includes a variety of biological, chemical and physical parameters. The fundamental problem with water quality assessment is the difficulty with which a large number of parameters are evaluated. If all criteria have been used to evaluate the quality of GW, then computational difficulty will certainly increase. In this paper, a new hybrid three-stage assessment approach based on Feature Extraction Algorithm (FEA), standard deviation (SD) and Complex Proportional Assessment Method (COPRAS) was proposed. In the first stage the redundant criteria for GW quality assessment is removed using FEA. Secondly, the weights of the reduct parameters are evaluated based on SD. Finally, GW sites are ranked using (COPRAS). Sixteen GW samples were gathered from several GW wells. The collected samples were investigated for 12 various physicochemical water quality criteria to evaluate GW quality. The results reveal that sulfates (SO4), nitrate (NO3), Fluorides (F), sodium (Na), and Escherichia coli (E. coli) are the main parameters for GW quality assessment. Furthermore, the optimal concentrations of physicochemical parameters: (SO4), (NO3), (F), (Na), and (E. coli) are 18.9(mg/L), 8.18(mg/L), 0.222(mg/L), 21(mg/L), 1.9(MPN/100mL), respectively, with 40 WQI.The suggested approach is compared to three MCDM methods to validate the performance of the proposed methodology. The assessment results gained by the FEA combined with COPRAS and SD significantly minimize computational difficulty, reasonable and accurate. The approach presented in this study improves the system for evaluating GW quality.

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Groundwater quality ranking for drinking purposes, using the entropy method and the spatial autocorrelation index

Cited by 134 publications

References 21 publications

Effectiveness of Entropy Weight Method in Decision-Making

Effectiveness of Entropy Weight Method in Decision-Making

Information Entropy for Evaluation of Wastewater Composition

Groundwater Assessment Using Feature Extraction Algorithm Combined with Complex Proportional Assessment Method and Standard Deviation

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