Prediction of Water Quality Index (WQI) Using a Novel Hybrid Water Wave-Optimized Extreme Gradient Boosting

Abstract

Determining the drastic changes in water quality in this contaminated period is an urgent requirement that is crucial for the continued existence of the current and growing water demand. This is why it is essential for maintaining the water supply. Due to variability and complexity in the data, the water quality index (WQI) has been developed as a reliable way to express water quality. Analyzing the data relative to the water quality has been extremely difficult.  In this investigation, the WQI prediction of water samples using the novel Hybrid Water Wave Optimized eXtreme Gradient Boosting (HWW-XGB).  In this research, the data preprocessing using the Outlier Detection (Box Plot Analysis) and Min-max normalization was finished to minimize the computational time. Pre-processing is a crucial first step in the proposed system because the dataset contains inappropriate and inaccurate data. The Independent Linear Analysis (ICA) is utilized to extract the required features. The pre-processed features are removed through feature extraction, eliminating unnecessary data for the next essential phase. The collected features are fed into Hybrid Water Wave Optimized eXtreme Gradient Boosting (HWW-XGB) classifier. The effectiveness of the proposed approach was demonstrated when compared to the existing method.