Open AccessAn Enhanced Weight Update Method for Simplified ARTMAP to Classify Groundwater Data
Author(s) -
Relangi Naga Durga Satya Siva Kiran,
Aparna Chaparala,
S. Radhika
Publication year - 2021
Publication title -
international journal of design and nature and ecodynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 13
eISSN - 1755-7445
pISSN - 1755-7437
DOI - 10.18280/ijdne.160505
Subject(s) - adaptive resonance theory , groundwater , fuzzy logic , artificial neural network , computer science , data mining , artificial intelligence , mathematics , mathematical optimization , engineering , geotechnical engineering
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.