From smarter to intelligent monitoring: Harnessing AI and machine learning for CSO and SSO prediction

Combined Sewer Overflows (CSOs) and Sanitary Sewer Overflows (SSOs) remain among the most difficult issues facing wastewater utilities. When untreated wastewater reaches rivers, lakes, or city streets, it creates environmental harm, public health risks, and regulatory penalties. For many years, utilities relied on scheduled cleaning, manual inspections, and basic supervisory controls. While these measures provided a necessary framework, they were labor-intensive, reactive, and often too late to prevent major incidents.

The past decade has brought a new era of smart monitoring. With IoT sensors, flow meters, rainfall gauges, and SCADA systems, operators now have real-time visibility into their networks. Dashboards and alerts make it possible to detect warning signs earlier and respond more effectively. Even so, smart monitoring tends to stop at reporting, leaving utility staff to interpret the data and determine how to act.

The next step is intelligent monitoring. By applying advanced analytics and machine learning, utilities can move from observing problems to anticipating and preventing them. Intelligent systems can forecast risks, suggest responses, and, in some cases, adjust operations automatically. By closing the gap between monitoring and response, they reduce delays, cut down on errors, and help avoid costly events. At the same time, the rise of operational digital twins has expanded the ability of utilities to manage their systems. Real-time control has long allowed operators to react by adjusting pumps, gates, and storage. Digital twins go further by creating continuously updated models of sewer networks. By combining live data with predictive simulations, they allow operators to run “what-if” scenarios, stress-test against severe weather, and make more confident investment decisions.

Together, intelligent monitoring and digital twins are changing how CSOs and SSOs are managed. Utilities can now move from fragmented, reactive responses to integrated, predictive, and increasingly automated operations.

How advanced analytics are being applied

The use of analytics in sewer monitoring is pushing utilities beyond simple alarms and static reports into predictive and preventive management. Instead of relying entirely on operators to sift through raw information, these tools rapidly analyze data and present actionable insights.

One example is the automation of CCTV inspections. SewerAI applies computer vision to inspection footage, identifying defects, evaluating pipe conditions, and recommending maintenance priorities. Tasks that once consumed hours of manual review can now be completed in minutes, allowing staff to focus on planning and execution.

Many utilities are adopting a broader, enterprise-wide approach through smart water and analytics platforms. Built on modern cloud data lakes, these platforms unify information that was previously scattered across different departments — such as SCADA, work management, GIS, regulatory compliance, hydraulic modeling, and capital planning – into a single, integrated environment. Utilities typically progress through stages of adoption: starting with real-time dashboards, moving to targeted pilots to test predictive tools, and eventually advancing to predictive models that use sensor data to forecast failures or reduce dry-weather SSOs. Over time, these platforms scale to support applications such as automated CCTV defect coding, condition forecasting, and risk analysis of critical infrastructure. By pinpointing high-risk segments, utilities can target cleaning and maintenance activities, extend asset life, reduce emergency callouts, and lower operational costs. This structured, phased roadmap demonstrates how a data-driven platform can modernize operations, strengthen regulatory compliance, and serve as a model for other utilities seeking to transform their infrastructure management.

Forecasting overflows is another important application. By combining rainfall predictions with sewer flow and soil saturation data, utilities can anticipate when and where overflows are likely to occur. This allows operators to prepare storage, adjust pumping schedules, and dispatch crews before storms create problems. Anomaly detection adds another layer of protection, flagging unusual pump behavior or irregular flow patterns so operators can intervene early.

At a larger scale, platforms such as AQUADVANCED by SUEZ integrate multiple capabilities into one environment. They combine monitoring, forecasting, and anomaly detection and present operators with recommendations rather than raw numbers. When connected to digital twins, they go further still, allowing utilities to test storm response strategies, simulate infrastructure upgrades, and refine both day-to-day operations and long-term planning.

Building the digital foundation

For advanced analytics to work, utilities need a solid digital foundation. It begins with comprehensive data capture. Flow meters, level sensors, and rainfall gauges provide the continuous inputs required for predictive modeling. SCADA systems, once used mainly for control, become valuable sources of intelligence when paired with analytics. GIS and asset registries add spatial context, linking anomalies to specific pipes or districts to make planning and response more precise.

Integration platforms — whether cloud-based or centralized — pull these streams together into a single environment, forming the backbone for analysis. As more systems are connected, cybersecurity and governance become essential. Following frameworks such as NIST and ISO/IEC ensures that infrastructure is protected and data remains reliable.

Digital twins represent the most advanced part of this foundation. Asset-level twins track the condition of pumps, tanks, and other components. Network twins model how flows move through the system. Operational twins combine both, integrating live data with predictive simulations to support real-time decisions, storm planning, and long-term investment strategies. These capabilities allow utilities not only to monitor conditions but also to anticipate risks, automate responses, and plan with greater accuracy.

Costs and benefits

The cost of introducing analytics and predictive monitoring depends on the size of the system and the existing infrastructure. Pilot projects in high-risk sub-basins can often be launched for a few hundred thousand dollars and serve as proof of concept before larger commitments are made. Expanding citywide can cost millions, involving thousands of sensors, integrated SCADA and GIS systems, and the advanced analytics needed to support digital twins. Utilities must also budget for ongoing expenses such as software, cloud hosting, and staff training.

Despite the investment, the return is strong. Many utilities report savings two to four times their original investment. These savings come from avoided fines, fewer emergency responses, optimized maintenance, and longer asset life. Just as important, predictive monitoring improves compliance, strengthens public confidence, and builds resilience against climate change and aging systems.

Case studies

Lower Paxton Township Authority, Pennsylvania

In Pennsylvania, the Lower Paxton Township Authority, supported by GHD, launched an ambitious inflow and infiltration elimination program in response to consent decrees. By analyzing flow and performance data, predictive tools helped identify priority mini-basins and measure the effectiveness of rehabilitation. This enabled the Authority to move from patch repairs to full “replacement by basin.” The program eliminated more than 40 overflow locations, enabled the system to handle 97 percent of historical wet weather events, and avoided over $127 million in infrastructure costs and met compliance ahead of schedule.

An international example

Internationally, AQUADVANCED by SUEZ has demonstrated the global reach of predictive monitoring. In several cities, the platform integrates rainfall forecasts, sewer flow data, and real-time controls to dynamically adjust pumping and storage. The result has been significant reductions in CSO discharges, improved water quality, and stronger resilience against severe storms.

For the 2024 Paris Summer Olympic Games, AQUADVANCED Urban Drainage was deployed to ensure the Seine remained safe for swimming events. The platform provided real-time simulation of rainfall, network conditions, and storage capacity enabling operators to prevent combined sewer overflows during a historically wet summer. As a result, over 100,000 athletes were able to safely swim in the Seine without any water quality issues. AQUADVANCED Urban Drainage delivered zero sanitary overflows into the river, improved plant loading efficiency, and reduced operational costs. By integrating AI-driven insights with Paris's infrastructure investments, AQUADVANCED helped the city meet its Olympic water quality goals while optimizing long-term network performance.

Conclusion

The management of CSOs and SSOs is undergoing a fundamental transformation. Utilities that once depended on manual inspections, fixed schedules, and after-the-fact responses are now embracing smart monitoring, intelligent analytics, and digital twins to stay ahead of problems. These technologies offer more than just visibility — they deliver foresight, actionable insights, and, increasingly, automated decision support.

Across the sector, utilities are demonstrating that predictive operations are not theoretical; they are practical, cost-effective, and scalable. By combining real-time monitoring with predictive models and operational digital twins, utilities can reduce overflow incidents, optimize maintenance, improve regulatory compliance, and extend the life of their assets. The shift from reactive to predictive management is enabling teams to respond faster, allocate resources more efficiently, and build resilience against aging infrastructure and climate pressures.

Ultimately, this evolution is not just about meeting compliance targets or controlling costs. It’s about creating adaptive, data-driven systems that anticipate challenges, act proactively, and give utilities the confidence to plan boldly for the future — protecting communities, the environment, and public trust for decades to come.