AI and SAP Integration for Intelligent Stormwater Management in Smart Cities

Abstract

Municipal stormwater management is increasingly data-intensive, drawing on sensor telemetry, geographic information systems, maintenance records, and capital planning data that, in many cities, remain fragmented across departmental systems rather than integrated with the enterprise resource planning (ERP) platforms that manage the city’s assets, work orders, and finances. This paper examines how artificial intelligence (AI) and machine learning (ML) can be integrated with SAP enterprise systems, particularly SAP S/4HANA and SAP Enterprise Asset Management (EAM), to support intelligent stormwater infrastructure management within a smart city context. We describe an integration architecture in which stormwater sensor data, GIS asset records, and inspection findings feed AI-driven predictive models whose outputs are surfaced directly within SAP EAM as prioritized maintenance notifications and work orders, closing the gap between infrastructure condition insight and the enterprise systems municipal operations and finance teams already use to execute and fund maintenance work. The paper discusses relevant SAP modules and integration mechanisms, including SAP EAM, SAP Analytics Cloud, and IoT integration through SAP Business Technology Platform, alongside the AI/ML techniques applied to stormwater deterioration forecasting and flood-risk anomaly detection. A discussion of governance, data quality, and change-management considerations specific to municipal SAP deployments is included, along with directions for future research on interoperability between municipal GIS ecosystems and enterprise ERP platforms.