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Ground Water Quality Modelling Using Data Mining Techniques and Artificial Neural Network Based Approach


R. Priya and Dr.R. Mallika
Abstract

Data Mining (DM) applications are widely used for ground water quality prediction. This work provides an extensive investigation on application of DM technique, namely Artificial Neural Network (ANN) to assess the water quality related problem especially ground water. To predict the water quality changes of the normal water with respect to time and space using Artificial Neural Network (ANN) was performed and the test results were validated. The Artificial Neural Network model is helpful in revealing the unexposed interrelations in the classical data, therefore expediting the prediction idea, picture and forecasting of water quality. Based on their functioning metrics, we apply various formulas which have been illustrated in the ANN framework is definite and systematic adequate to make important and relevant decisions regarding data usage. Water contamination is a means that has discredited various lakes, oceans, river and other water resource. As an outcome, it is necessary to monitor the quantity and quality of water. Quality of water can be thought of as a physical, biological and chemical characteristics of water can be used to predict the water quality. ANN is a viable tool to assess the quality of groundwater for drinking and agricultural purposes. Total Dissolved solids (TDS) is a key hydro chemical parameter which comprises the wholesome quality of groundwater. Hence, the predictions of sum of TDS content will aid to evaluate the water quality of any locality.

Volume 11 | 10-Special Issue

Pages: 1001-1007

DOI: 10.5373/JARDCS/V11SP10/20192897