Computational Intelligence and Optimization for Water Quality Management

This chapter is an attempt to establish the understanding of how CI methods and optimisation approaches can enhance the capacity in WM. Water safety and potability emerge as an international subject that requires better methods in identifying, assessing, and forecasting the quality of water. It explains how the water quality that is measured by pH, conductivity, hardness and turbidity data sets is analyzed through several computational intelligence based machine learning algorithms to provide an overview of the water quality. The approaches to enhancing the efficiency of water treatment and decision-making regarding the usage of the resources in sustainable water quality management are discussed here. In this section, we use machine learning on real world application where we predict water potability using water quality proxy data sets.