AI & Latest Technologies in Drainage Operation and Maintenance

Details

A practical, tool-agnostic program linking AI, IoT sensors, SCADA, GIS, hydraulic modeling, computer vision, and digital twins to day-to-day drainage operations. Participants learn how to structure data, build predictive and rules-based workflows, inspect and classify defects, prioritize work orders, and coordinate emergency response. Templates and labs focus on measurable KPIs, governance, and safe, auditable adoption.

Target Group

Target Group

• Drainage operations supervisors and field engineers
• Asset management, CMMS, and GIS specialists
• Pump/lift station operators and maintenance planners
• SCADA/automation, data/analytics, and IT/OT partners
• Emergency response, HSE, and quality/compliance coordinators
  

Goals

• Map AI and modern technology use-cases across inspection, cleaning, pumping, and flood response
• Build data readiness for sensors, SCADA, CCTV inspections, work orders, and GIS assets
• Apply predictive maintenance, anomaly detection, and risk scoring to prioritize interventions
• Use hydraulic modeling and digital twins to test scenarios and optimize operations
• Standardize decision briefs, dashboards, and governance for safe, scalable adoption
  

Target Competencies

Target Competencies

• Data-driven asset management and risk-based prioritization
• Sensor/SCADA integration and anomaly detection
• Computer-vision-assisted inspection and condition grading
• Predictive maintenance, hydraulic scenario testing, and digital twins
• Emergency response playbooks, safety controls, and governance
  

Outlines

Intelligent Asset Management Foundations

• Drainage asset taxonomy: networks, laterals, manholes, pump/lift stations, outfalls
• Data readiness: IDs, attributes, condition history, work orders, and telemetry mapping
• Criticality and risk: consequence of failure, likelihood signals, and service levels
• CMMS/GIS integration: location accuracy, attachments, and field data loops
• KPI tree: blockages avoided, overflow/flood minutes, response time, cost per km

Sensors, SCADA & Anomaly Detection

• Sensor landscape: level, flow, rainfall, vibration, power, water quality, CCTV feeds
• SCADA signals to operations: setpoints, alarms, historian queries, and alarm hygiene
• Anomaly detection: thresholds vs. machine learning; false alarm reduction playbook
• Edge vs. cloud processing: latency, resilience, and data retention considerations
• Hands-on pattern: build a simple alarm-to-work-order triage with risk tags