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Towards Estimating Rainfall Using Cellular Phone Signal

Cellular telephones; Crowdsourcing; Learning systems; Machine learning; Regression analysis; Cellular Phone; Cellular signal data; Cellular signals; Crowd sourcing; Data preprocessing; Machine-learning; Pre-processing method; Rainfall estimations; Signal data; Signal level; Rain

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Bibliographic Details
Main Authors: Low C.Y., Solihin M.I., Yanto, Ang C.K., Lim W.H., Hayder G.
Other Authors: 58068781900
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-270382023-05-29T17:38:54Z Towards Estimating Rainfall Using Cellular Phone Signal Low C.Y. Solihin M.I. Yanto Ang C.K. Lim W.H. Hayder G. 58068781900 16644075500 56685916900 56202445900 57224979685 56239664100 Cellular telephones; Crowdsourcing; Learning systems; Machine learning; Regression analysis; Cellular Phone; Cellular signal data; Cellular signals; Crowd sourcing; Data preprocessing; Machine-learning; Pre-processing method; Rainfall estimations; Signal data; Signal level; Rain A crowd-sourced method in rainfall estimation from mobile phones is attempted. The result of the study indicates high correlation between cellular signal levels and rainfall, suggesting that rainfall could be predicted using cellular signal levels from mobile phones. Custom data preprocessing methods have been employed to ensure significant results. Regression models using machine learning built upon the collected data show a borderline R2 score at only 0.39, while classification models show high performance with an average macro F1-score of 0.81 in predicting rain events instead of predicting rainfall levels. The result of this study paves the way for crowd-sourcing cellular signal data from mobile phones to better understand rainfall patterns. Further extensive data collection will need to be carried out to clarify the effectiveness of the method. This study is still limited in terms of data size. � 2022 IEEE. Final 2023-05-29T09:38:54Z 2023-05-29T09:38:54Z 2022 Conference Paper 10.1109/ICECCME55909.2022.9988111 2-s2.0-85146416871 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146416871&doi=10.1109%2fICECCME55909.2022.9988111&partnerID=40&md5=76be7bbfc4a4672ab9f09c610a706f6f https://irepository.uniten.edu.my/handle/123456789/27038 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Cellular telephones; Crowdsourcing; Learning systems; Machine learning; Regression analysis; Cellular Phone; Cellular signal data; Cellular signals; Crowd sourcing; Data preprocessing; Machine-learning; Pre-processing method; Rainfall estimations; Signal data; Signal level; Rain
author2 58068781900
author_facet 58068781900
Low C.Y.
Solihin M.I.
Yanto
Ang C.K.
Lim W.H.
Hayder G.
format Conference Paper
author Low C.Y.
Solihin M.I.
Yanto
Ang C.K.
Lim W.H.
Hayder G.
spellingShingle Low C.Y.
Solihin M.I.
Yanto
Ang C.K.
Lim W.H.
Hayder G.
Towards Estimating Rainfall Using Cellular Phone Signal
author_sort Low C.Y.
title Towards Estimating Rainfall Using Cellular Phone Signal
title_short Towards Estimating Rainfall Using Cellular Phone Signal
title_full Towards Estimating Rainfall Using Cellular Phone Signal
title_fullStr Towards Estimating Rainfall Using Cellular Phone Signal
title_full_unstemmed Towards Estimating Rainfall Using Cellular Phone Signal
title_sort towards estimating rainfall using cellular phone signal
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425677769998336
score 13.214268

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