Electric Profiling Based Fully Recurrent Deep Neural Learning Classification for Groundwater Quality Prediction

Abstract

Groundwater is present under Earth surface within soil pore spaces and rock formation. It is recharged via surface and typically discharged. Water pollution affects the quality of water and troubles human health, economic growth as well as social wealth. The groundwater quality identification is essential to maintain freshwater resources for sustainable development. But, the existing methods failed to improve the groundwater quality and minimize time consumption. To address these problems, an Electric Profiling Ground Water Identification based Fully Recurrent Deep Neural Learning Classification (EPGWI-FRDNLC) Method is designed to achieve efficient quality analytics by higher accuracy and minimum time consumption. In EPGWI-FRDNLC Method, electric profiling process is carried out for ground water identification. After that, a fully recurrent deep neural learning classification process is carried out for ground water quality prediction analytics. Fully recurrent deep neural learning classification process includes more than three layers for performing the ground water quality analysis. In EPGWI-FRDNLC Method Model, a lot of data were measured for input and given to the input layer. After that, input data were given to hidden layer 1. In that layer, softmax regression is used for performing the input parameter analysis like temperature, pH, turbidity, salinity, nitrates and phosphates. Then, the regression coefficient value is transferred to hidden layer 2. Tanimato similarity function is employed for identifying the similarity between the regression coefficient value of training data and threshold value. Tanimato similarity value ranges from 0 to 1 and the results are sent to the output layer. By this way, EPGWI-FRDNLC Method improves the ground water quality prediction analytics. Experimental evaluation of EPGWI-FRDNLC Method was performed with various metrics by an amount of data.