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 (CAN-FIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the preprocessor 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.

Background: Although experiments on water quality are time consuming and expensive, models are often employed as supplement to simulate water quality. Artificial neural network (ANN) is an efficient tool in hydrologic studies, yet it cannot predetermine its results in the forms of maps and geo-referenced data. Methods: In this study, ANN was applied to simulate groundwater quality and geographic information system (GIS) was used as pre-processing and post-processing tool in simulating water quality in the Mazandaran Plain (Caspian southern coasts, Iran). Groundwater quality was simulated using multilayer perceptron (MLP) network. The determination of groundwater quality index (GWQI) and the estimation of effective factors in groundwater quality were also undertaken. After modeling in ANN, the model validation was carried out. Also, the study area was divided with the pixels 1×1 km (raster format) in GIS medium. Then, the model input layers were combined and a raster layer which comprised the model inputs values and geographic coordinate was generated. Using geographic coordinate, the values of pixels (model inputs) were inputted into ANN (Neuro Solutions software). Groundwater quality was simulated using the validated optimum network in the sites without water quality experiments. In the next step, the results of ANN simulation were entered into GIS medium and groundwater quality map was generated based on the simulated results of ANN. 2016, 3(4), 173-182 IntroductionGroundwater is one of the most important water resources on earth, and its water quality studies are very vital for the protection and planning of water resources particularly in arid and semi-arid regions such as Iran. Groundwater presently accounts for more than 90% of Iran's total drinking water consumption. This water resource is less vulnerable to bacterial pollution and evaporation than surface water and therefore, groundwater is more important than surface water. One of the major limiting factors in water exploitation is unsuitable water quality. Human activities such as agricultures, manufacturing and urban development affect the quality of groundwater. Unfortunately, the groundwater quality is now being endangered due to inappropriate exploitation and increased human activity in recent decades. Thus, it is necessary to study water quality in order to manage water resources properly. Since experi-