Publication: Towards Estimating Rainfall Using Cellular Phone Signal
Date
2022
Authors
Low C.Y.
Solihin M.I.
Yanto
Ang C.K.
Lim W.H.
Hayder G.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
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