Correlation of Groundwater Quality Index Prediction and Classification Using Optimal Deep Belief Network

Groundwater is considered a fundamental water resource for cultivation and consumption in semi-arid and arid areas. For effectual organization of water quality and quantity, it is crucial to evaluate the contamination level of the present surface water. Data-driven methods can vital to forecast groundwater quality that is controlling health of human beings. The water quality index (WQI) was established dependent upon the physicochemical parameters of water instances. Conventional WQI is frequently detected with massive errors in the estimation of sub-indices and computation needs more time. The study aims to present a Groundwater Quality Index Prediction and Classification using Optimal Deep Belief Network (GWQIPC-ODBN) model in Thiruvallur district, India. The presented GWQIPC-ODBN model classifies the quality of groundwater into different classes. To accomplish this, the presented GWQIPC-ODBN technique undergoes data normalization. Followed by, the groundwater classification occurs by implementing DBN method. At last, the cat swarm optimizer (CSO) method is used for the tuning process of the DBN method. The simulation outcomes of the GWQIPC-ODBN approach are investigated on the groundwater dataset collected from the Thiruvallur district. The investigational values reported that the GWQIPC-ODBN approach gains supreme performance over other techniques in terms of diverse measures.