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Develop standardized testing datasets for benchmarking automated QC algorithm performance

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Storage:	The size of this resource is 579.9 MB
Created:	Oct 30, 2024 at 2:27 a.m.
Last updated:	Nov 02, 2024 at 12:40 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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Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

Place/Area Name:

Logan River Observetory

Longitude

-111.7957°

Latitude

41.7390°

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How to Cite

Kahrizi, E. (2024). Develop standardized testing datasets for benchmarking automated QC algorithm performance, HydroShare, http://www.hydroshare.org/resource/61a71043bc5240bea4baf3ec18872e9d

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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