Modelling of Binary Coyote Optimization Algorithm with Deep Learning based Ground Water Index Classification

Groundwater quality fundamentally defines the utility of water in a source related to the nature and attention of the impurities present in the instance. An interaction influence of the uninterrupted decline in water quantity and quality, around one billion people globally face a lack of satisfactory and safer water supply. The most effective technique for categorizing water quality is utilizing the Water Quality Index (WQI). Water quality can frequently be evaluated dependent on WQIs. A tool can be widely exploited for assessing the efficiency of water quality management algorithms. The study offers the modelling of Binary Coyote Optimization Algorithm with Deep Learning based Ground Water Index Classification (BCOADL-GWIC) technique. The presented BCOADL-GWIC technique classifies the WQI into different levels. To achieve this, the BCOADL-GWIC technique follows an initial stage of data normalization. In addition, the BCOADL-GWIC technique comprises BCOA based feature selection technique to generate a collection of feature vectors. For WQI classification, the BCOADL-GWIC technique uses gated recurrent unit (GRU) model. Finally, the experimental outcome of the BCOADL-GWIC method is tested on WQI dataset, collected from Thiruvallur district, India. The comprehensive results highlighted the outperforming results of the BCOADL-GWIC technique.