📊 IC³ Analytics & AI Dashboard Suite
Complete Implementation Guide
9 Purpose-Built Dashboards for Water Utility Operations
Date: June 4, 2026
Framework: React 18.3 + TypeScript + Vite
✅ All 9 Dashboards Implemented
Executive Summary
The IC³ water utility platform now features a comprehensive Analytics & AI menu with 9 purpose-built dashboards covering critical operational insights. Each dashboard is accessible from the sidebar under ANALYTICS & AI and provides real-time insights, predictive analytics, and AI-driven recommendations.
Key Capabilities
- 🤖 8 AI Models for predictive analytics and anomaly detection
- 📊 Real-time Monitoring with live KPI updates
- 🔍 Deep Insights into system health, efficiency, and quality
- ⚡ Fast Response with sub-minute detection latency
- 📈 Trend Analysis with historical data visualization
✅ Status: All 9 dashboards are fully implemented, integrated with the sidebar menu, and ready for backend API integration.
The 9 Analytics & AI Dashboards
Route: ?tab=executive-summary
Purpose: High-level strategic overview for decision makers
📈 Key Metrics Tracked:
- System Availability - Current uptime vs. SLA targets (Target: 99.5%+)
- Network Loss (NRW) - Non-Revenue Water tracking (Target: <15%)
- Water Quality Score - Compliance with standards (Target: >90/100)
- Equipment Efficiency - Pump and motor performance (Target: >85%)
- Critical Anomalies - Real-time threat level counter
- Detection Latency - AI response time (Target: <5 min)
- MTTR - Mean Time to Resolve incidents (Target: <30 min)
🎯 Use Case:
For: Plant managers, operations directors, executives
When: Daily standups, performance reviews, incident escalation
💡 Features:
- Executive KPI Cards with trend indicators (up/down/stable)
- Time-frame selector (day/week/month/quarter)
- Status-based color coding (healthy/warning/critical)
- Trend visualization with percentage changes
Route: ?tab=anomaly-detection
Purpose: AI-powered real-time sensor anomaly detection and alerting
🔍 Tabs Available:
- Live Tab: Real-time anomaly detection with severity matrix
- History Tab: Historical anomaly records with pattern analysis
- Configuration Tab: Alert rules customization and thresholds
- Reports Tab: Anomaly trend reports and system recommendations
📊 Components:
- Anomaly KPI Cards - Summary metrics and counts
- Anomaly Table - Detailed list with timestamps
- Severity Matrix - 2D visualization (frequency vs. impact)
- Sensor Health Scorecard - Individual sensor status
- Anomaly Detail Panel - Drill-down analysis
- Alert Rules Config - Rule management interface
🤖 AI Model:
Isolation Forest + LSTM for temporal pattern detection
🎯 Use Case:
For: Control room operators, field technicians, data analysts
When: Continuous monitoring, incident investigation, model tuning
Route: ?tab=leak-detection
Purpose: Identify and classify pipe leaks and burst conditions
📍 Key Metrics:
- Leak Probability Scoring (0-100%)
- Leak vs. Burst Classification
- Location Pinpointing (GPS coordinates + street address)
- Leak Volume Estimation (m³/day)
- Repair Urgency Ranking (Critical/High/Medium/Low)
- Leak Age - How long the leak has existed
- Cost Impact - Revenue loss estimation
📊 Data Sources:
- Real-time flow differential analysis
- Pressure wave detection
- Acoustic sensor data
- Historical repair records
🎯 Use Case:
For: Network maintenance teams, planning engineers
When: Leak hunting campaigns, asset replacement planning, budget forecasting
Route: ?tab=pump-health
Purpose: Predictive maintenance for pump stations
📊 Pump Fleet Overview:
- Total pumps monitored across system
- Operating vs. idle status distribution
- Health status breakdown (Healthy/Degrading/Critical)
⚙️ Individual Pump Metrics:
- Vibration Analysis (mm/s)
- Current Draw Trends (A)
- Temperature Monitoring (°C)
- Runtime Hours Tracking
- Efficiency Degradation (%)
🔮 Predictive Indicators:
- Remaining Useful Life (RUL) estimation in days
- Failure Probability (7-day forecast)
- Recommended Maintenance Windows
- Spare Parts Demand Forecasting
🎯 Use Case:
For: Maintenance planners, pump technicians
When: Preventive maintenance scheduling, inventory planning, warranty claims
Route: ?tab=water-quality
Purpose: Monitor water quality parameters and compliance risks
🧬 Quality Metrics Monitored:
- pH Level Trends
- Turbidity Analysis (NTU)
- Chlorine Residual Tracking (mg/L)
- Coliform Detection Risk
- Heavy Metal Indicators
- Taste & Odor Complaint Forecasting
✅ Compliance Status:
- Regulatory Requirement Alignment
- Deviation Alerts & Notifications
- Historical Compliance Tracking
- Corrective Action Tracking
📋 Regulatory Standards Covered:
- EU Drinking Water Directive
- US Safe Drinking Water Act
- Local Health Authority Standards
🎯 Use Case:
For: Quality assurance teams, compliance officers
When: Daily quality checks, regulatory audits, incident response
Route: ?tab=demand-forecast
Purpose: AI-based water demand prediction for optimal operations
⏱️ Forecast Horizons:
- Short-term (24-72 hours): Hourly predictions with peak timing
- Medium-term (1-4 weeks): Weekly patterns and seasonal trends
- Long-term (months/years): Growth projections and planning
📊 Forecast Inputs:
- Historical consumption data patterns
- Weather forecasts (temperature, rainfall)
- Event calendars (holidays, festivals)
- Population growth trends
- Industrial activity indicators
- School calendar effects
📈 Accuracy Metrics:
- MAPE (Mean Absolute Percentage Error) tracking
- Confidence intervals for predictions
- Model performance metrics
🎯 Use Case:
For: Operations planners, supply chain managers
When: Pump scheduling optimization, treatment plant loading, intermittent supply planning
Route: ?tab=root-cause
Purpose: AI-guided root cause analysis for incidents and failures
🔍 Analysis Features:
- Multi-variable Correlation Analysis
- Causality Chain Identification
- Contributing Factor Ranking
- Historical Incident Database Search
- Similar Case Pattern Matching
💡 Recommendations:
- Corrective Action Suggestions
- Preventive Measure Recommendations
- Resource Requirement Estimation
- Learning Feedback Loop Integration
🤖 AI Techniques:
- Bayesian Networks for Causal Inference
- Graph Analysis for Dependency Mapping
- NLP for Knowledge Base Querying
- Clustering for Pattern Similarity
🎯 Use Case:
For: Incident investigators, engineers, training teams
When: Post-incident reviews, system optimization, knowledge capture
Route: ?tab=nrw-attribution
Purpose: Disaggregate Non-Revenue Water losses into components
💧 NRW Loss Breakdown:
- Real Losses (Physical): Distributed network, mains, connections
- Apparent Losses: Metering errors, illegal connections, theft/bypass
🗺️ Geographic Analysis:
- District Metered Area (DMA) Analysis
- Critical Loss Zones Identification
- Geographic Distribution Heatmaps
- Repair Strategy Optimization
💰 Financial Impact:
- Revenue Recovery Potential Calculation
- Cost-Benefit Analysis for Interventions
- ROI Calculations for Leak Repairs
- Financial Impact Estimation
📊 IWA Methodology Compliance:
- Water Balance Calculation (IWA standard)
- SRII (System Real Reduction Index) Tracking
- UARL (Unavoidable Annual Real Loss) Benchmarking
🎯 Use Case:
For: NRW coordinators, asset managers, CFO
When: NRW reduction campaigns, AMP planning, regulatory reporting
Route: ?tab=energy-optimization
Purpose: Minimize energy costs while maintaining service levels
⚡ Real-time Energy Tracking:
- Current Power Consumption (kW)
- Daily Energy Usage (kWh)
- Cost Tracking (€/day)
- Carbon Footprint (kg CO₂)
💡 Optimization Opportunities:
- Pump Scheduling Optimization
- Variable Frequency Drive (VFD) Tuning
- Demand Response Participation
- Off-Peak Load Shifting Recommendations
💰 Cost Analysis:
- Time-of-Use Tariff Optimization
- Demand Charge Management
- Peak vs. Off-Peak Breakdown
- Monthly Savings Potential (€)
🌱 Sustainability Metrics:
- Renewable Energy Integration Tracking
- Green Energy % Contribution
- Carbon Offset Tracking
- ESG (Environmental, Social, Governance) Reporting
🤖 Optimization Algorithms:
- Linear Programming for Pump Schedules
- Machine Learning for Demand Prediction
- Mixed-Integer Optimization for VFD Control
🎯 Use Case:
For: Operations engineers, sustainability teams, finance
When: Budget planning, energy audits, sustainability reporting
Architecture & Technical Implementation
Technology Stack
| Component |
Technology |
Version |
| Frontend Framework |
React |
18.3.1 |
| Language |
TypeScript |
5.5.3 |
| Build Tool |
Vite |
5.4.0 |
| Charts Library |
Recharts |
2.12.7 |
| Maps |
@react-google-maps/api |
2.20.8 |
| State Management |
React Hooks |
Built-in |
Directory Structure
frontend/src/modules/analytics/ai/
├── anomaly/
│ ├── AnomalyDetectionDashboard.tsx (main)
│ ├── hooks/useAnomalyDetection.ts
│ ├── types/index.ts
│ ├── components/
│ │ ├── AnomalyKPICards.tsx
│ │ ├── AnomalyTable.tsx
│ │ ├── SeverityMatrix.tsx
│ │ ├── SensorHealthScorecard.tsx
│ │ ├── AnomalyDetailPanel.tsx
│ │ ├── AlertRulesConfig.tsx
│ │ ├── AnomalyHistoryTab.tsx
│ │ └── AnomalyReports.tsx
│ └── styles/anomalyDetection.module.css
├── leak/ → LeakBurstClassifier.tsx
├── pump/ → PumpHealthMonitor.tsx
├── quality/ → QualityRiskModel.tsx
├── demand/ → DemandForecasting.tsx
├── rootcause/ → RootCauseAssistant.tsx
├── nrw/ → NRWAttribution.tsx
├── energy/ → EnergyOptimization.tsx
└── executive/ → ExecutiveSummary.tsx
Routing Integration
All dashboards are integrated into App.tsx with tab-based routing:
{tab === 'executive-summary' && <ExecutiveSummary />}
{tab === 'anomaly-detection' && <AnomalyDetectionDashboard />}
{tab === 'leak-detection' && <LeakBurstClassifier />}
{tab === 'pump-health' && <PumpHealthMonitor />}
{tab === 'water-quality' && <QualityRiskModel />}
{tab === 'demand-forecast' && <DemandForecasting />}
{tab === 'root-cause' && <RootCauseAssistant />}
{tab === 'nrw-attribution' && <NRWAttribution />}
{tab === 'energy-optimization' && <EnergyOptimization />}
{tab === 'trends' && <TrendChart telemetry={telemetry} />}
Sidebar Navigation Configuration
Location: Sidebar.tsx
Group ID: 'analytics'
Icon: 📊
Label: ANALYTICS & AI
Color: #aa66ff (Purple)
Default Open: false
Items: 10 (including Trends)
Component Patterns
Each dashboard follows a consistent architectural pattern:
┌─────────────────────────────────────────┐
│ Panel Header (icon + title + timestamp) │
├─────────────────────────────────────────┤
│ KPI Cards (metrics summary) │
├─────────────────────────────────────────┤
│ Tabs (live/history/config/reports) │
├─────────────────────────────────────────┤
│ Main Content Area │
│ - Tables, charts, or detail panels │
├─────────────────────────────────────────┤
│ Action Buttons (export, print, share) │
└─────────────────────────────────────────┘