Determination of water quality index by fuzzy logic approach: a case of ground water in an Indian town

Construction and application of a composite model for acid mine drainage quality evaluation based on analytic hierarchy process, factor analysis and fuzzy comprehensive evaluation: Guizhou Province, China, as a case

The process of mining activities often causes the formation of acid mine drainage (AMD). Through rock fractures and underground rivers, AMD can easily enter the groundwater environment near mines and cause serious pollution to water quality. In order to effectively evaluate the quality of polluted mine water and to understand its threat to the ecosystem around the mine. In this study, four AMD pollution distribution areas, Guiyang City, Bijie City, Qianxinan Prefecture, and Qiandongnan Prefecture in Guizhou Province, were used as the study area. A composite model for mine water quality evaluation was constructed using factor analysis (FA), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). Furthermore, by introducing the weighted average method and the level characteristic value (J), the water quality type and the water body environmental quality were evaluated comprehensively, respectively. Compared with the traditional evaluation model, the AHP-FA-FCE model has obvious advantages in the selection of evaluation indicators, the determination of indicator weights, and the comprehensive evaluation of water quality types, and the evaluation results obtained are more reasonable and accurate. Three common factors mainly controlled by mineral oxidation factor, human activity factor, and mineral dissolution factor were extracted by dimension reduction of the original hydrochemical data by FA. The water quality of the mine water samples was evaluated using SO4 2- , Fe, Al, Mn, Na, and F- as evaluation indicators, and the results showed that the mine water samples in the study area as a whole were dominated by class V water, which accounted for 77.78% of the total. Based on the statistical analysis of the original data, it was found that influenced by the water-rock interactions in the study area and the AMD pollution components, the hydrochemical type of the mine water is mainly SO4 2- -Ca-Mg type. The water body environmental quality of mine water in four areas, Guiyang City, Qianxinan Prefecture, Bijie City, and Qiandongnan Prefecture, is from excellent to poor. The average level characteristic value of all the areas is more than 3, and the overall environmental quality of the water body is poor. The strong water-rock interaction and mining activities in the study area may be the main cause of AMD pollution. The results of this study may provide some theoretical reference for the water quality evaluation of AMD-polluted areas. PRACTITIONER POINTS: A composite model for mine water quality evaluation was constructed. A factor analysis-based evaluation indicator selection method is proposed. This study improved the weighting process of the traditional fuzzy comprehensive evaluation. A water quality discriminant based on the weighted average method is proposed. The water environmental quality of various types of mine water was evaluated.

Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index

Groundwater is a vital source of freshwater in both urban and rural regions of the world. However, its injudicious abstraction and rapidly increasing contamination are posing a severe threat for sustainable water supply worldwide. Geographical Information System (GIS)-based groundwater quality evaluation using Groundwater Quality Index (GQI) has been proved to be a cost-effective tool for assessing groundwater quality and its variability at a larger scale. However, the conventional GQI approach is unable to deal with uncertainties involved in the assessment of environmental problems. To overcome this limitation, a novel hybrid framework integrating Fuzzy Logic with the GIS-based GQI is proposed in this study for assessing groundwater quality and its spatial variability. The proposed hybrid framework is demonstrated through a case study in a hard-rock terrain of Southern India using ten prominent groundwater-quality parameters measured during pre-monsoon and post-monsoon seasons. Two conventional GIS-based GQI models GQI-10 (using all the ten groundwater-quality parameters) and GQI-7 (using seven 'concerned/critical' groundwater-quality parameters) as well as hybrid Fuzzy-GIS-based GQI (FGQI) models (using seven critical parameters) were developed for the two seasons and the results were compared. The Trapezoidal membership functions classified the model input parameters into 'desirable', 'acceptable' and 'unacceptable' classes based on the experts' knowledge and water quality standards for drinking purposes. The concentrations of Ca²⁺, Mg²⁺, and SO₄^2- in groundwater were found within the WHO desirable limits for drinking water throughout the year, while the concentrations of seven parameters (TDS, NO₃^--N, Na⁺, Cl^-, K⁺, F^- and Hardness) exceed their permissible limits during pre-monsoon and post-monsoon seasons. A comparative evaluation of GQI models revealed that the FGQI model predicts groundwater quality better than the conventional GQI-10 and GQI-7 models. GQI modeling results suggest that the groundwater of most of eastern and southern parts (∼60% in pre-monsoon season; ∼90% in post-monsoon season) of the study area is unsuitable for drinking. Further, the groundwater quality deteriorates during post-monsoon seasons compared to pre-monsoon seasons, which indicates an increased influx of contaminants from different industries, mining areas, waste disposal sites and agricultural fields during monsoon seasons. This finding calls for the strict enforcement of regulations for proper handling of effluents from various contamination sources in the study area. It is concluded that the fuzzy logic-based decision-making approach (FGQI) is more reliable and pragmatic for groundwater-quality assessment and analysis at a larger scale. It can serve as a useful tool for the water planners and decision makers in efficiently monitoring and managing groundwater quality at watershed or basin scales.

A two-stage fuzzy chance-constrained water management model

In this study, an inexact two-stage fuzzy gradient chance-constrained programming (ITSFGP) method is developed and applied to the water resources management in the Heshui River Basin, Jiangxi Province, China. The optimization model is established by incorporating interval programming, two-stage stochastic programming, and fuzzy gradient chance-constrained programming within an optimization framework. The hybrid model can address uncertainties represented as fuzzy sets, probability distributions, and interval numbers. It can effectively tackle the interactions between pre-regulated economic targets and the associated environmental penalties attributed to water allocation schemes and reflect the tradeoffs between economic revenues and system-failure risk. Furthermore, uncertainties associated with the decision makers' preferences are considered in decision-making processes. The obtained results can provide decision support for the local sustainable economic development and water resources allocation strategies under multiple uncertainties.

A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices

Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy indices developed in this research are reliable and flexible when used in groundwater quality assessment for drinking purposes.