Abstract
The process of water quality testing is money/time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CANFIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the pre-processor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study’s results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.
Highlights
In the developing countries such as Iran, there is a need of efficient water supply especially in view of scarce water resources and water pollution problems
Quantitative amounts of the factors affecting groundwater quality included the average depth of water table, the transmissivity of aquifer formations, distance from the pollutant centers, site elevation and the number of households were estimated based on the secondary data, digital maps and field studies
We evaluated the coactive neuro-fuzzy inference system (CANFIS) performance by the statistical evolution criteria; which shows that this method significantly outperforms the assessing process and has a very good and acceptable performance for assessing groundwater quality
Summary
In the developing countries such as Iran, there is a need of efficient water supply especially in view of scarce water resources and water pollution problems. According to Kordel et al (2013) since the soundness of policy decisions in groundwater management almost directly depends on the reliability of the water resource management monitoring programs, an accurate and routine assessment of the groundwater quality (as an essential component of groundwater environment evaluation), and accurate prediction of the groundwater level and, is necessary to establishing optimal strategies for regional water resource management (Zhang et al 2009; Li et al 2012; Singh et al 2014) To access this important purpose, namely to make the best and optimal use of the available water, it. Adaptive neuro-fuzzy inference system (ANFIS) as a multilayer feed-forward network is capable of combining the benefits of both these fields and uses Gaussian functions for fuzzy sets, linear functions for the rule outputs and Surgeon’s inference mechanism and mainly has been used for mapping input–output relationship based on available data sets (Chang and Chang 2006; Nourani et al 2011; Subbaraj and Kannapiran 2010; Ullah and Choudhury 2013)
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