Industry Challenge
Water utilities are under pressure to maintain aging underground infrastructure while minimizing service disruptions and damage claims. Traditional leak detection methods are reactive, labor-intensive, and often fail to catch critical failures in time. As costs rise and resources remain limited, there’s a growing need for smarter, more proactive infrastructure management.
Key challenges include:
- Missed early warning signs of pipe rupture
- Rising costs due to property damage claims (fincas)
- Manual prioritization and dispatching
- Lack of structured guidance for field crews
The Solution: Intelligent Leak Prediction & Field Optimization by DinoCloud on AWS
DinoCloud’s solution uses advanced AI and machine learning to anticipate pipe ruptures and optimize operational response. Built on Amazon Web Services (AWS), the system combines predictive analytics, proactive alerting, and real-time field feedback to deliver a closed-loop leak management workflow.
AI-Powered Capabilities
🔮 Predictive Model for Pipe Failures
At the core is a machine learning model (XGBoost) trained on infrastructure, environmental, and historical leak data. It identifies pipes at high risk of rupture and automatically generates a prioritized list of potential leaks based on criticality.
📊 Proactive Alerting & Claim Prioritization
The AI system issues alerts to operations teams, focusing attention on the most urgent issues. This allows preventive interventions that reduce property damage and shorten response times.
👷 Field Crew Optimization & Guidance
The system recommends where to send crews based on model outputs. Once in the field, crews use a digital checklist of AI-generated steps tailored to each case, helping them detect invisible leaks and standardize resolution efforts.
🔁 Feedback Loop for Continuous Improvement
After inspections, crews report outcomes (e.g., “leak confirmed” or “false positive”). This feedback feeds into the model’s learning cycle, improving future prediction accuracy and refining prioritization logic.
📈 Centralized Data Dashboard
All system data flows into a visual control panel that consolidates performance metrics, prediction accuracy, and team response effectiveness—empowering strategic, data-driven decision-making.
System Workflow at a Glance
- ✅ Model predicts pipe leaks
- 📋 Generates a prioritized list of high-risk cases
- 🛠 Dispatches a crew to inspect the critical location
- 🔍 Crew confirms or refutes the prediction on-site
- 📊 Outcome is logged and fed back into the system for ongoing improvement
This closed-loop system enables a shift from reactive repairs to strategic infrastructure care.
Built on AWS
The solution is built on scalable, secure AWS services:
- Amazon SageMaker for training and inference
- Amazon Lambda and Step Functions for orchestrating workflows
- Amazon S3 and DynamoDB for storing predictions and outcomes
- Amazon CloudWatch for system monitoring
- Fully compatible with future integrations such as GIS systems or mobile field apps
Business Impact
Water utilities benefit from:
📍 Clear visibility into performance via intuitive dashboards
💸 Reduced claims and repair costs
⏱ Faster, more targeted interventions
🧠 Smarter resource use and reduced downtime
📉 Lower operational risk through proactive detection
Conclusion
DinoCloud’s AI-powered solution for leak detection and field coordination empowers water utilities to transition from firefighting to foresight. With machine learning at its core, and built on trusted AWS infrastructure, this solution enhances both operational efficiency and infrastructure resilience—ultimately protecting assets, communities, and budgets.
