Abstract
Water is life, human and all activities of living things needs water for its survival. There are growing concerns about the quality of water available for human consumption and other daily human activities. The pollution of water are traceable to economic activities, industrialization, urbanization, agricultural activities and other human activities. These activities also affect human life and water quality. This concern has necessitated the studies on prediction of water quality and an efficient water monitoring system. The most common method of ensuring good quality of water is by testing them in the laboratory, this current method wastes time, not economical, wastes human power. Many studies on the use of Artificial neural network and machine learning models to predict the quality of water has been on the increase, this study creates an Artificial neural network and random forest regression machine learning model using parameters such as ph., Hardness, Conductivity, Solids, Conductivity, Turbidity, Portability and does a comparative analysis of the best fit model and it was determined that the random forest model had the best fit model with a lower mean squared error of 2.325103283606211 when compared to the Artificial neural network with a mean squared error of 3.3122911 by determining the model with the least mean squared error. This study postulates that random forest method is the best fit model for prediction of water quality when compared to the Artificial neural network.