Developing Standardized Testing Datasets for Benchmarking Automated QC Algorithm Performance
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| Owners: | Ehsan Kahrizi |
| Type: | Resource |
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| Created: | Oct 30, 2024 at 2:27 a.m. |
| Last updated: | Mar 12, 2025 at 5:28 a.m. |
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| Sharing Status: | Public |
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Abstract
Diagnose Aquatic Sensor Data for Temperature and Water Quality Events
## Overview
This project is designed to diagnose and flag events in aquatic sensor data based on various conditions and thresholds. It processes raw data from aquatic sites and applies thresholds and logical conditions to identify different types of anomalies. The primary focus is to flag events that may indicate sensor anomalies, environmental conditions (e.g., frozen water), or technician site visits.
### Key Features
1. Event Detection: Detects and flags various event types, such as MNT (maintenance), LWT (low water table), ICE (frozen water), SLM (sensor logger malfunction), PF (power failure), and VIN (visual inspection).
2. Data Quality Control: Uses thresholds to validate sensor readings, ensuring accurate representation of water conditions.
3. Automated Labelling: Automatically labels events using a set of predefined indicators for anomaly detection.
Workflow of the model:
https://ibb.co/8BDFjsv
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How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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