Development of Integrated Stormwater Asset Management Framework

South African (SA) municipalities are unable to sustainably manage stormwater assets because of the inability to incorporate Artificial intelligence (AI) and Best Management Practices (BMPs) into the existing stormwater asset management (SAM) frameworks. The research aims to develop a framework that interconnect conventional and BMPs into a single system that reduces the risk of stormwater asset failures. The study focused on quantitative and qualitative data analysis techniques. This resulted in the performance of desktop study and visual condition assessment of stormwater assets, focusing in the inner city of Johannesburg in Fordsburg. Risk-based asset management (RBAM) methodology is conducted, focusing on the asset management cycle's core components; asset inventory, level of service (LOS), criticality; life cycle costing (LCC) and long-term finance. The assessment indicates that most of the stormwater assets are slightly in good conditions. The performed risk analysis indicates moderate POF and consequences of failure (COF). There is however procrastination by the council in adaptation of stormwater BMPs. Stormwater infrastructure require extensive reinvestment in capital and methods of management to ensure world class infrastructure.