Fundamental feature-based assessment of water quality through optimal enhanced models

High water quality (WQ) is of the essence to human health and environmental sustainability. The resultant effects are viewed in industrial and agricultural productivity and the drinking water supply. In light of the recent trend of water-based tourism and recreational activities, it is now more than ever relevant to commit to stringent enforcement of the regulations on WQ. Compliance with these norms will ensure that the security of drinking water, agricultural productivity, and industrial processes is maintained accordingly. It can predict water quality by finding patterns within different water parameters by analyzing past and present data. The given study explains the use of Support Vector Classification (SVC), Gradient Boost Classification (GBC), and Random Forest Classification (RFC) that have been optimized using Fitness Dependent Optimizer Algorithm (FDOA) and Cheetah Optimization Algorithm (COA), respectively, to improve their accuracy in projection. Considering the results of evaluation metrics for models, the GBCO, SVCO, and RFCO models registered 0.992, 0.966, and 0.944 values of Accuracy, while the GBC, SVC, and RFC single models achieved 0.956, 0.933, and 0.904 values. Implementing the optimization algorithms, especially the COA, enhanced the Accuracy of single models as 3.6 %, 3.3 %, and 4 % for GBC, SVC, and RFC.