Hybrid Machine Learning Algorithms for Effective Prediction of Water Quality

The molecular characteristics of water samples involve analyzing the different physical and chemical properties to understand the composition and quality of the water. Some common parameters studied in this field include: temperature, pH, turbidity, conductivity, biochemical, dissolved solids, chemical and dissolved oxygen (DO), nutrient levels, heavy metals, and microbiological analysis. By studying these physico-chemical characteristics, researchers can gain insights into the water’s quality, its potential uses, and identify any potential pollutants or sources of contamination. It is necessary to regularly check the health of water sources. To safeguard human health, ecosystems, and sustainable development, monitoring and maintaining the source’s water quality is crucial. Effective water quality management involves regular testing, pollution prevention measures, wastewater treatment, and the implementation of appropriate regulations and policies to protect water sources and ensure their sustainability. The most accurate projections of the effects of wastewater improvement will be produced by research on machine learning-based techniques. The dataset is analyzed using the supervised machine learning approach (SMLT), which gathers a variety of data points, including the categorization of variables and outcomes from univariate, bivariate, and multivariate studies. Using evaluation tools, the performance of several machine learning techniques on the presented dataset is contrasted and discussed. The error rate prediction shows the feasibility of the given model to work over the challenging environment. This model also works efficiently towards the unseen data to achieve better generalization.