ARTIFICIAL INTELLIGENCE: A MEDIATOR BETWEEN MACHINE LEARNING AND PROJECT RISK MANAGEMENT OF WATER SUPPLY UPGRADE IN LAGOS WATER CORPORATION, NIGERIA

Critical infrastructure projects, where water supply reliability is essential for public health and socio-economic development, efficient risk management is crucial. This study therefore examines the role of artificial intelligence (AI) as a mediator between machine learning (ML) and project risk management of the Water Supply Upgrade at Lagos Water Corporation. The specific objectives were to: (i) determine the effect of supervised machine learning on qualitative project risk management, and (ii) identify the impact of unsupervised machine learning on quantitative project risk management. The study adopted a descriptive survey design, with data collected through structured questionnaires administered to a sample of 94 respondents, including engineers, project managers, and ICT personnel involved in the project. Inferential statistics were employed in the analysis, with linear regression models used to test the hypotheses using SPSS version 25. The findings revealed that supervised machine learning significantly influenced qualitative risk management (R² = 0.596, p < 0.05), while unsupervised machine learning significantly impacted quantitative risk management (R² = 0.479, p < 0.05). The study therefore concludes that AI effectively mediates the application of machine learning in enhancing both qualitative and quantitative risk management processes in water supply projects. It recommends that Lagos Water Corporation integrate AI-driven machine learning systems into its risk management framework, strengthen data infrastructure, and train staff to optimize project efficiency and reduce uncertainties