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AquaAI - Predictive Analytics

Advanced AI/ML-powered predictive analytics platform providing anomaly detection, consumption forecasting, predictive maintenance alerts, and intelligent optimization recommendations for water management systems.

Overview​

AquaAI is distinct from AquaGPT (chatbot). While AquaGPT provides conversational insights, AquaAI uses machine learning models to predict future trends, detect anomalies, forecast consumption, and provide proactive recommendations for system optimization.

Location: libs/aquaAi/

Route: /aqua-ai

Permission Required: AQUA_AI

Target Users: Operations managers, data analysts, maintenance teams

Key Features​

1. Anomaly Detection​

Identifies unusual patterns in:

  • Water consumption (sudden spikes/drops)
  • Quality parameters (abnormal values)
  • Pump performance (efficiency degradation)
  • Pressure/flow irregularities
  • Energy consumption anomalies

Detection Methods:

  • Statistical analysis
  • Machine learning models
  • Time-series pattern recognition
  • Multi-variable correlation
  • Real-time alerting

2. Consumption Forecasting​

Predicts future water usage:

  • Short-term (1-7 days ahead)
  • Medium-term (1-4 weeks)
  • Long-term (1-12 months)
  • Seasonal adjustments
  • Weather-based predictions

Forecast Accuracy:

  • Historical accuracy tracking
  • Confidence intervals
  • Model performance metrics
  • Continuous learning/improvement

3. Predictive Maintenance​

Predicts equipment issues before failure:

  • Pump bearing wear
  • Filter clogging predictions
  • Valve malfunction warnings
  • Sensor drift detection
  • Equipment lifespan estimation

Benefits:

  • Reduced downtime
  • Lower maintenance costs
  • Extended equipment life
  • Optimized spare parts inventory
  • Scheduled maintenance planning

4. Optimization Recommendations​

AI-generated suggestions for:

  • Pump operation scheduling
  • Treatment process optimization
  • Energy cost reduction
  • Water loss minimization
  • Quality improvement actions

5. Pattern Recognition​

Identifies trends and patterns:

  • Daily/weekly consumption cycles
  • Seasonal variations
  • Production correlations
  • Weather impact analysis
  • Event-based patterns

6. What-If Scenarios​

Simulate different scenarios:

  • Production increase impact
  • Equipment failure scenarios
  • Efficiency improvement outcomes
  • Cost-saving initiatives
  • Resource allocation changes

AI Models​

1. Time Series Forecasting​

Algorithms:

  • ARIMA (AutoRegressive Integrated Moving Average)
  • Prophet (Facebook's forecasting tool)
  • LSTM (Long Short-Term Memory neural networks)
  • Seasonal decomposition

2. Anomaly Detection​

Methods:

  • Isolation Forest
  • One-Class SVM
  • Autoencoder neural networks
  • Statistical outlier detection (Z-score, IQR)

3. Classification Models​

For categorizing:

  • Normal vs abnormal states
  • Equipment health status
  • Alert severity levels
  • Root cause identification

4. Regression Models​

For predicting:

  • Continuous values (flow, pressure, quality)
  • Equipment remaining useful life (RUL)
  • Maintenance costs
  • Energy consumption

Chat Interface​

Interactive AI Assistant:

  • Ask questions about predictions
  • Request forecasts
  • Get anomaly explanations
  • Receive recommendations
  • View model insights

Example Queries:

  • "What will be tomorrow's consumption?"
  • "Show me anomalies in the last week"
  • "When should I schedule pump maintenance?"
  • "How can I reduce water costs?"
  • "Predict next month's consumption"

Data Flow​

Architecture​

Key Components:

  • AiDataProvider - State management
  • ChatWindow - AI chat interface
  • ML Models - Prediction engines
  • Data Pipeline - Feature preparation
  • Model Storage - Trained models repository
  • Analytics Engine - Pattern analysis

Example Predictions​

Consumption Forecast​

{
  forecast: [
    { date: "26/02/2026", predicted: 1450, confidence: [1380, 1520] },
    { date: "27/02/2026", predicted: 1420, confidence: [1350, 1490] },
    { date: "28/02/2026", predicted: 1480, confidence: [1400, 1560] }
  ],
  accuracy: 94.5,  // % accuracy on historical data
  factors: ["historical pattern", "day of week", "weather"]
}

Anomaly Alert​

{
  type: "consumption_spike",
  detected: "25/02/2026 14:30",
  unit: "Borewell 1",
  actualValue: 2500,
  expectedValue: 1400,
  deviation: "+78%",
  severity: "High",
  possibleCauses: ["leakage", "valve malfunction", "meter error"],
  recommendation: "Inspect unit immediately for leakage"
}

Predictive Maintenance​

{
  equipment: "Raw Water Pump 1",
  prediction: "Bearing failure likely",
  estimatedTime: "7-10 days",
  confidence: 85,
  indicators: [
    "Vibration increase detected",
    "Temperature rising",
    "Efficiency declining"
  ],
  recommendation: "Schedule bearing replacement within 5 days"
}

Integration​

  • Dashboard - AI insights widget
  • Alerts - Anomaly-based alerts
  • AquaGPT - Conversational AI companion
  • Monitoring - Real-time data input
  • Reports - Forecast reports

Model Training​

Training Data:

  • Minimum 90 days of historical data
  • Quality-checked data points
  • Labeled anomalies
  • Equipment maintenance logs

Retraining Schedule:

  • Weekly model updates
  • Monthly full retraining
  • Continuous online learning
  • Performance monitoring

Performance Metrics​

Model Evaluation:

  • Accuracy: % of correct predictions
  • Precision: True positives / (True positives + False positives)
  • Recall: True positives / (True positives + False negatives)
  • RMSE: Root Mean Square Error for forecasts
  • MAE: Mean Absolute Error

Use Cases​

1. Budget Planning​

  • Forecast annual water consumption
  • Predict treatment costs
  • Estimate energy requirements
  • Plan capacity expansion

2. Operational Optimization​

  • Optimize pump schedules
  • Reduce energy costs
  • Minimize water loss
  • Improve treatment efficiency

3. Risk Management​

  • Predict shortage scenarios
  • Identify critical failures
  • Plan contingencies
  • Manage compliance risks

4. Sustainability​

  • Track reduction initiatives
  • Forecast carbon footprint
  • Optimize resource usage
  • Meet ESG targets

Last Updated: February 2026 Module Location: libs/aquaAi/ Note: Requires sufficient historical data for accurate predictions