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Machine Learning with Applications
Article . 2024 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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https://doi.org/10.2139/ssrn.4...
Article . 2024 . Peer-reviewed
Data sources: Crossref
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A Machine Learning Framework to Measure Water Drop Penetration Time (Wdpt) for Soil Water Repellency Analysis

Authors: Danxu Wang; Emma Regentova; Venkatesan Muthukumar; Markus Berli; Frederick C. Harris;

A Machine Learning Framework to Measure Water Drop Penetration Time (Wdpt) for Soil Water Repellency Analysis

Abstract

The heat from wildfires volatilizes soil’s organic compounds which form a waxy layer when condensed on cooler soil particles causing soil to repel water. Timely assessment of soil water repellency (SWR) is critical for prediction and prevention of detrimental impacts of hydrophobic soils such as soil erosion, reduced availability of water to plants, and water runoff after rainfalls leading to floods. The Water Drop Penetration Time (WDPT), i.e., the time elapsed from a drop landing on the soil surface to its complete absorption is commonly used to assess the SWR level. Its manual measurements have variability based on the used instruments and subjective observations. The goal of this work is to design an automated system to perform standardized WDPT tests and assess the SWR levels. It consists of an electronically controlled mechanism to release a water drop, and a video camera to record the water penetration process. The latter is modeled as an “action” in video and Temporal Action Localization (TAL) analytics is used for predicting the WDPT and assessing the SWR level.

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Keywords

Soil water repellency (SWR), TriDet, Temporal Action Localization, Electronic computers. Computer science, Water Drop Penetration Time (WDPT), Q300-390, QA75.5-76.95, ActionFormer, Cybernetics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold