Access to reliable water supply remains a major challenge in rapidly urbanizing cities of developing countries. This project was undertaken to examine the water supply situation in Ibadan North Local Government Area, with the aim of understanding the sources of water available to residents, the level of adequacy and reliability of supply, and the challenges faced in meeting daily water needs. The choice of the study area was informed by its high population density and its importance as a residential and commercial hub within Ibadan metropolis. Structured questionnaire were administered to residents to assess the water supply, determine the water consumption of the study area. The result of the questionnaire were analyzed using Microsoft Excel. A well yield test was carried out to know how much water flows into the well from surrounding rock or soil ion a given time, the water supply for the area was designed. For the prediction of household water demand, Machine learning algorithms which includes linear regression, SVR, Neural Network, random forest regression models were developed and evaluated using metrics like MAE, RMSE, MAPE and R2 The study combines field data, observations, and analytical methods to provide a realistic picture of water supply conditions in the area. Emphasis was placed on presenting findings in a clear and practical manner so that the outcomes of the study can be useful not only for academic purposes but also for policy makers, water supply agencies, and other stakeholders involved in urban water management. The random forest model outperformed the linear model, achieving an R2 of 0.99 It is hoped that the findings and recommendations presented in this project will contribute, in a modest way, to ongoing efforts aimed at improving water supply planning and management in Ibadan North Local Government Area and similar urban settings.
Ayoade et al. (Mon,) studied this question.