The groundwater for aquatic purposes must be assessed prior to its consumption. Huge number of conventional methods are existing for assessing the quality of groundwater. The water quality index is one of the important conventional methods to assess the groundwater quality. But the conventional methods alone are not enough to assess groundwater quality as well as classify based on its purity. In this paper, we propose an enhanced weight update method for Simplified Fuzzy Adaptive Resonance Theory model to classify the groundwater quality depending on the relative weights of the groundwater quality parameters. Finding the optimal weights is the key to achieve better accuracy of the model, most of the nonlinear models fails to exhibit good accuracy if they fail to learn the optimal weights in the learning process. The aim of the work is to find the good fit between the predicted and the actual groundwater quality grades by identifying the optimal weights of the network by the enhanced weight update method. The Simplified Fuzzy Adaptive Resonance Theory map with the enhanced weight update method performance is justified by comparing it with the Simplified Fuzzy Adaptive Resonance Theory Map. The enhanced weight update method improves the accuracy of the Simplified Fuzzy Adaptive Resonance Theory Map in classifying and predicting the groundwater quality.
Groundwater quality assessment is primarily intended to determine whether the water in a particular area can be used for aquatic purpose or not. The assessment comprises analysis of physical, chemical and microbiological characteristics of groundwater samples. The quality of groundwater can be evaluated through few standard conventional methods viz. Water Quality Index, Canadian Council of Ministries Environmental Water Quality Index and Weighted Arithmetic Water Quality Index etc. In addition to the conventional methods, multivariate statistical methods like Principal Component Analysis, Factor Analysis and Cluster Analysis can also be used to assess the groundwater quality. As these methods are descriptive models, they are inadequate to predict the quality of unknown groundwater sample. Hence, an efficient predictive model is desirable to analyze the characteristic parameters of groundwater samples and predict the quality of an unknown sample. The sample may have both crisp and fuzzy values. Conventional supervised learning methods may not be suitable for constructing the required prediction model as they are not suitable for handling fuzzy input data. Therefore, simplified fuzzy adaptive resonance theory model is an appropriate choice for accomplishing the task of building the prediction model. The present work proposes to assess the quality of groundwater by applying the Weighted Arithmetic Water Quality Index method and Simplified Fuzzy Adaptive Resonance Theory model by considering 7 groundwater quality parameters. The accuracy of afore mentioned approach seems to be pleasing when compared to counter parts like Back propagation and Random Forests classifiers.
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