RNN Models for Rain Detection

Hai Victor Habi; Hagit Messer

doi:10.1109/SiPS47522.2019.9020603

RNN Models for Rain Detection

Hai Victor Habi, Hagit Messer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-Term memory (LSTM) units, here we used gated recurrent units (GRUs). We compare the wet-dry classification performance of LSTM and GRU based network architectures using data from operational cellular backhaul networks and meteorological measurements in Israel and Sweden, and draw conclusions based on datasets consisting of actual measurements over two years in two different geological and climatic regions.

Original language	English
Title of host publication	2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	184-188
Number of pages	5
ISBN (Electronic)	9781728119274
DOIs	https://doi.org/10.1109/SiPS47522.2019.9020603
State	Published - Oct 2019
Externally published	Yes
Event	33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019 - Nanjing, China Duration: 20 Oct 2019 → 23 Oct 2019

Publication series

Name	IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
Volume	2019-October
ISSN (Print)	1520-6130

Conference

Conference	33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019
Country/Territory	China
City	Nanjing
Period	20/10/19 → 23/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

CML
GRU
LSTM
RNN
rain detection

Access to Document

10.1109/SiPS47522.2019.9020603

Cite this

Habi, H. V., & Messer, H. (2019). RNN Models for Rain Detection. In 2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019 (pp. 184-188). Article 9020603 (IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SiPS47522.2019.9020603

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title = "RNN Models for Rain Detection",

abstract = "The task of rain detection, also known as wet-dry classification, using recurrent neural networks (RNNs) utilizing data from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies used long short-Term memory (LSTM) units, here we used gated recurrent units (GRUs). We compare the wet-dry classification performance of LSTM and GRU based network architectures using data from operational cellular backhaul networks and meteorological measurements in Israel and Sweden, and draw conclusions based on datasets consisting of actual measurements over two years in two different geological and climatic regions.",

keywords = "CML, GRU, LSTM, RNN, rain detection",

author = "Habi, {Hai Victor} and Hagit Messer",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019 ; Conference date: 20-10-2019 Through 23-10-2019",

year = "2019",

month = oct,

doi = "10.1109/SiPS47522.2019.9020603",

language = "אנגלית",

series = "IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation",

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Habi, HV & Messer, H 2019, RNN Models for Rain Detection. in 2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019., 9020603, IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 184-188, 33rd IEEE International Workshop on Signal Processing Systems, SiPS 2019, Nanjing, China, 20/10/19. https://doi.org/10.1109/SiPS47522.2019.9020603

RNN Models for Rain Detection. / Habi, Hai Victor; Messer, Hagit.
2019 IEEE International Workshop on Signal Processing Systems, SiPS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 184-188 9020603 (IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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RNN Models for Rain Detection

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Keywords

Access to Document

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