Publication:
Towards Estimating Rainfall Using Cellular Phone Signal

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dc.contributor.authorLow C.Y.en_US
dc.contributor.authorSolihin M.I.en_US
dc.contributor.authorYantoen_US
dc.contributor.authorAng C.K.en_US
dc.contributor.authorLim W.H.en_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorid58068781900en_US
dc.contributor.authorid16644075500en_US
dc.contributor.authorid56685916900en_US
dc.contributor.authorid56202445900en_US
dc.contributor.authorid57224979685en_US
dc.contributor.authorid56239664100en_US
dc.date.accessioned2023-05-29T09:38:54Z
dc.date.available2023-05-29T09:38:54Z
dc.date.issued2022
dc.descriptionCellular 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; Rainen_US
dc.description.abstractA 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICECCME55909.2022.9988111
dc.identifier.scopus2-s2.0-85146416871
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85146416871&doi=10.1109%2fICECCME55909.2022.9988111&partnerID=40&md5=76be7bbfc4a4672ab9f09c610a706f6f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27038
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022
dc.titleTowards Estimating Rainfall Using Cellular Phone Signalen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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