A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

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 (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.

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

“…Gholami et al [ 34 ] presented an advanced form of the ANFIS model for groundwater quality simulation; the proposed model was described as coactive-ANFIS (CANFIS) integrated with geographic information system (GIS). The training and validation of the proposed model was performed by considering a case study of Mazandaran Plain in the northern region of Iran.…”

“…The study also showed that the SVM model is a fast, reliable, and cost-effective AI technique. The feasibility of AI techniques in groundwater quality simulation has been evaluated by numerous scholars and such studies have produced efficient performances [32][33][34][35][36][37][38][39][40].…”

A preliminary study for selecting the appropriate AI-based forecasting model for hospital assets demand under disasters

“…Researchers propose some CANFIS models (e.g. Parthiban and Subramanian, 2008;Aytek, 2009;Gholami et al, 2017), but they are specific to other studies framework that is different from this one. Besides, their code is not available.…”

A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers

“…In this way, application of the nonlinear regression is in preparation of SRCs during the flooding periods (Ferguson 1986;Phillips et al 1999;Holtschlag 2001;Peng and Zhou 2011;Fan et al 2012). Also, recently it has been developed several deterministic models to estimate the sediment production of the watersheds such as neurofuzzy, artificial neural network (ANN), dendritic regression, and genetic algorithm methods (Asselman 2000;Horowitz 2003;Abrishamchi et al 2005;Schluter et al 2005;Stefan and Andrew 2008;Gholami et al 2017Gholami et al , 2018. Yang et al (2009) found that for estimating total bed material sediment load in rivers, it is better to use the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept.…”