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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
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FloodSense street sign mounted flood depth sensor

Authors: Challagonda, Praneeth; Mydlarz, Charlie; Henaff, Elizabeth; Silverman, Andrea; Brain, Tega; Khan, Junaid;

FloodSense street sign mounted flood depth sensor

Abstract

Flood Depth Data (FDD) collected by a fleet of sensors deployed across 5 boroughs of New York City with a resolution of half an inch or less. The metadata for the sensors is included in the metadata.csv to identify the deployment coordinates of sensors, each with a unique deployment_id. The depth data is collected at least every five minutes and every minute in some locations depending on the ability to harvest solar energy at that deployment location. The final depth data field is depth_proc_mm, and the raw data is dist_mm. The raw measurement values received from the sensor are distance measurements (dist_mm), which are simply distance measurements collected from a ranging ultrasonic-based sensor. These distance measurements are converted to depths using night_median_dist_mm which is a daily calculated median of nighttime sensor readings. Direct sunlight affects ranging measurements due to high variance in the air column between the sensor and the concrete surface that it is mounted over. Additionally, the housing internally heats up when under direct sunlight, which affects the sensor readings and appears as if the surface dips with the daily increase and decrease in temperature during the daytime. After converting to raw depth values, a simple range filter is applied to the data removing any anomalies that lie below 10 millimeters and above unrealistic depth values (for example a person - between 5ft to 6ft), which is named depth_filt_mm. Further, this filtered depth value is processed through data filters eliminating blips, any pulse chains, or a flat line due to garbage or a car parked underneath the sensor. The output of these filters is labeled depth_proc_mm. This data is intended for use by communities, researchers, and New York City government agencies to better understand the frequency, severity, and impacts of flooding in New York City. Here is the live dashboard for these sensors deployed: FloodNet Data Dashboard More about this project at FloodNet.NYC This is an open-source project and for more information on the sensors and build manuals see the FloodNet FloodSensor GitHub page

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Keywords

ultrasonic, depth, water depth, water level, flood depth data, flood, street level, urban

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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