Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment

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dc.authoridGehlot, Anita/0000-0001-6463-9581
dc.authoridSalah, Bashir/0000-0003-2709-760X
dc.authoridIvkovic, Nikola/0000-0003-1730-2518
dc.authorwosidGehlot, Anita/HGD-3508-2022
dc.authorwosidSah, Dinesh/A-4912-2018
dc.authorwosidSalah, Bashir/ABC-5845-2020
dc.contributor.authorKumari, Rani
dc.contributor.authorSah, Dinesh Kumar
dc.contributor.authorCengiz, Korhan
dc.contributor.authorIvkovic, Nikola
dc.contributor.authorGehlot, Anita
dc.contributor.authorSalah, Bashir
dc.date.accessioned2024-05-19T14:46:25Z
dc.date.available2024-05-19T14:46:25Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe rainfall weather station employs a tipping bucket rain gauge, which serves as a specialized instrument for the meticulous assessment and documentation of various rainwater parameters. The implementation of a tipping bucket rain gauge for rainfall monitoring bears significant implications for both societal productivity as well as improvement of human life. A noteworthy example can be the constructive influence of rainwater over the sustainable agricultural irrigation practices, wherein the precise monitoring of rainfall through a tipping bucket rain gauge enables the formulation of tedious irrigation strategies. The rainfall monitoring if often handle using rain gauge which majorly faces two challenges named as mechanical devices failure and high installation and maintenance cost. Considering the challenges, we propose the fully automated rain gauge (RG) based on the principle of sound and its properties for rainfall monitoring. The working prototype is part of our work whose primary task is to collect the rainfall acoustic value and store it in the cloud. Our mechanism is to use the acoustic property of rain data to categorize rainfall intensity. We perform blind signal separation on the received signal (acoustic signal recorded with the help of microphone sensor) and feed the separated signal to a recurrent convolution neural network (RCNN). The source separation of the collected acoustic signals is primarily being done using independent component analysis and principal components analysis. The proposed solution can be able to make the classification of rain intensity with more than 80% accuracy. In addition to this, the developed method provides the sustainable solution to the challenges with the low-cost and application-specific acceptable threshold criteria and supplement rain measurement techniques.en_US
dc.identifier.doi10.1016/j.seta.2023.103398
dc.identifier.issn2213-1388
dc.identifier.issn2213-1396
dc.identifier.scopus2-s2.0-85169976794en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.seta.2023.103398
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5518
dc.identifier.volume60en_US
dc.identifier.wosWOS:001124259800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSustainable Energy Technologies and Assessmentsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectRain Gaugeen_US
dc.subjectBlind Source Separationen_US
dc.subjectAmbient Environmenten_US
dc.subjectAcoustic Sensoren_US
dc.subjectRecurrent Convolution Neural Networken_US
dc.titleAcoustic signal-based indigenous real-time rainfall monitoring system for sustainable environmenten_US
dc.typeArticleen_US

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