AI/ML-Based Intelligent Anomaly Detection for Automatic Weather Stations (AWS)

• Title SkyGuard AI: Intelligent Real-Time Anomaly Detection System for Temperature, Pressure, and Humidity Sensors in Automatic Weather Stations
• Background Automatic Weather Stations (AWS) are critical components of modern meteorological observation networks. These stations continuously monitor atmospheric parameters and provide real-time data for weather forecasting, climate monitoring, disaster management, aviation, agriculture, and scientific research.However, AWS observations often contain anomalies caused by sensor malfunction,communication failures, calibration drift, power fluctuations, harsh environmental conditions, and data corruption.Erroneous observations can significantly impact weather forecasting accuracy and decision-making systems. Traditional threshold-based quality control methods are often insufficient for identifying complex or hidden anomalies in meteorological data streams.
• Problem Statement Develop an AI/ML-based intelligent anomaly detection system capable of automatically identifying abnormal, inconsistent, or faulty observations from Automatic Weather Stations in real time using only the following parameters:
• Temperature (°C)
• Atmospheric Pressure (hPa)
• Relative Humidity (%)

The system should distinguish between genuine meteorological events and sensor/data anomalies while minimizing false alarms and enabling scalable deployment across large weather observation networks.

• Objectives
• Detect anomalies in real-time AWS data streams.
• Identify sensor faults, spikes, frozen values, and communication errors.
• Learn normal temporal and seasonal patterns of temperature, pressure, and humidity.
• Perform multivariate consistency analysis among atmospheric parameters.
• Provide confidence scores and explainable AI-based reasoning for detected anomalies.
• Predict possible sensor degradation and maintenance requirements.
• Optionally suggest corrected/imputed values for anomalous observations.
• Expected Inputs Participants may use historical AWS datasets, simulated anomalies, or streaming sensor data containing the following meteorological parameters:

Parameter- Unit Temperature - °C Atmospheric Pressure - hPa Relative Humidity - %

• Expected Outputs
• Real-time anomaly alerts
• Severity and confidence scores
• Root-cause classification
• Visualization dashboard
• Sensor health status
• Corrected data estimation (optional)
• Suggested Technologies
• Explainable AI (SHAP/LIME) (Preferable)
• Edge AI for low-power deployment on ESP32
• Evaluation Criteria (To be evaluated in anomaly injected data)

Criteria - Weightage Innovation & Novelty - 25% Detection Accuracy - 20% Real-Time Capability - 15% Explainability - 10% Scalability - 10% Practical Deployability - 10% Visualization/UI - 5% Energy Efficiency - 5%

• Example Use Case An AWS suddenly reports a temperature of 55°C with extremely high humidity and abnormal pressure variation while neighboring stations show normal conditions. The AI system should analyze temporal and spatial consistency, identify the reading as a probable sensor anomaly, generate an alert, and suggest corrective action.
• Grand Challenge Can AI build a self-aware and self-healing weather observation network capable of delivering trustworthy atmospheric data under all environmental conditions?
• Output:

Fully executable code with example usage and a document explaining various use cases