دورية أكاديمية
Towards the development of a citizens’ science-based acoustic rainfall sensing system
| العنوان: | Towards the development of a citizens’ science-based acoustic rainfall sensing system |
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| المؤلفون: | Alkhatib, Mohammed I.I., Talei, Amin, Chang, Tak Kwin, Hermawan, Andreas Aditya, Pauwels, Valentijn R.N. |
| المصدر: | Alkhatib , M I I , Talei , A , Chang , T K , Hermawan , A A & Pauwels , V R N 2024 , ' Towards the development of a citizens’ science-based acoustic rainfall sensing system ' , Journal of Hydrology , vol. 633 , 130973 . https://doi.org/10.1016/j.jhydrol.2024.130973Test |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Acoustic features, Machine learning techniques, Rainfall sensing, Urban soundscape |
| الوصف: | Floods have become more frequent and intense, causing human fatalities and economic losses worldwide. A robust rainfall estimation plays a pivotal role in flood forecasting and mitigation. It becomes even more critical in tropical urban areas, having rainfall events with high intensity and patchily distributed at local scales compared to rainfall in temperate climates. Therefore, measuring rainfall with high temporal and spatial resolutions is necessary. Unfortunately, several countries lack dense rainfall gauge networks and sufficient radar coverage. In this regard, several studies proposed incorporating citizens' science rainfall gauge networks to provide complementary daily data to existing rainfall sensing networks. However, limited to no efforts have been presented in the literature to develop new citizen science tools for event-based rainfall sensing or at a sub-daily level. Therefore, this study presents a proof of concept on utilising rainfall audio collected from a professional recorder, smartphone, or any other potential audio source for rainfall sensing in an urban area. The investigation is based on a dataset collected using a professional audio recorder at five different environments (locations) with various physical and acoustic characteristics over two years. The change in loudness levels as an input to a model was hypothesised to be a major acoustic feature that allows a model to distinguish rainfall intensities from each other at a specific location. However, using it alone would not be sufficient if the model deals with multiple locations in an urban area. Thus, a total of 40 acoustic features from different acoustic domains were examined and analysed to calibrate a machine-learning (ML) model to convert 1-min of rainfall audio recorded at different locations into 1-min of rainfall intensity. The research results showed that combining a loudness feature with complementary features from the cepstral and frequency domains could improve the model's performance on the validation dataset from R 2 = ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| DOI: | 10.1016/j.jhydrol.2024.130973 |
| الإتاحة: | https://doi.org/10.1016/j.jhydrol.2024.130973Test https://research.monash.edu/en/publications/92691ec0-e510-4b09-93b8-feb78ce91695Test https://researchmgt.monash.edu/ws/files/599301551/578886806.pdfTest http://www.scopus.com/inward/record.url?scp=85186376270&partnerID=8YFLogxKTest |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.BD361301 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.jhydrol.2024.130973 |
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