Name
Abstract | ||
|---|---|---|
In recent years, there has been growing interest in using precipitable water vapor (PWV) derived from global positioning system (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features such as
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,
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,
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,
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,
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along with
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and
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variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall
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, only a few of them, such as
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PWV</italic>
,
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,
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, and
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features, stand out for rainfall
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. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a 4-year (2012–2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TGRS.2019.2926110 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Global Positioning System,Delays,Rain,Mathematical model,Support vector machines,Numerical models | Meteorology,Atmosphere,False alarm,Support vector machine,Remote sensing,Relative humidity,Global Positioning System,Constant false alarm rate,Mathematics,Precipitation,Dew point | Journal |
Volume | Issue | ISSN |
57 | 11 | 0196-2892 |
Citations | PageRank | References |
1 | 0.42 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shilpa Manandhar | 1 | 4 | 3.05 |
| Soumyabrata Dev | 2 | 62 | 13.94 |
| Yee Hui Lee | 3 | 10 | 3.78 |
| Yu Song Meng | 4 | 28 | 8.62 |
| Stefan Winkler | 5 | 216 | 21.60 |