Data · dataset · 2024
Measuring Amazon rainfall intensity with sound recorders: data and code
Listed in DataSuds
Many regions still lack a network of ground weather observations, hampering effective climate monitoring and disaster management.
Description
In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fulfill this gap.
Our approach allows to estimate rainfall based on a sound recorder fixed on a tree trunk and supervised machine learning. The method is very promising for future weather monitoring of remote tropical areas. This dataset provides all data used in the paper “Measuring Amazon rainfall intensity with sound recorders”, as well as three sample audio recordings, used for the plotting of Figure 2.
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These files can also be used to extract the frequency range associated with the power spectrum density values. Contents - SMM00894_20230510_224500.wav represents a light rainfall recording - SMM00894_20230510_225500.wav represents a heavy rainfall recording - SMM00894_20230510_223500.wav represents an audio without rainfall - data used for training the models - Jupyter Notebook (code).
Links
Where it is published
- Dataverse dataset page dataverse.ird.fr/dataset.xhtml?persistentId=doi%3A10.23708%2FI0QYNM ↗
landing page · from dataverse ird fr
- DOI doi.org/10.23708/i0qynm ↗
DOI / persistent id · from dataverse ird fr
Catalogue records · 1
- Dataverse API dataverse.ird.fr/api/datasets/:persistentId/?persistentId=doi%3A10.23708%2FI0QY… ↗
metadata API · from dataverse ird fr
Topics
- Stated by source
- Earth and Environmental Sciences
- From keywords
- Computer Science & AI · Earth & Environmental Science · Machine learning
- Inferred from text
- Audio 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataSuds | doi:10.23708/I0QYNM | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4611 | mapping · dataverse ird fr | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | /subjects |
| concepts[modality].local:modality:audio | enrichment · dataverse ird fr | keyword-concept-rules@1.0.0 | title+description (75%) |
| created_date | source · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | |
| description | source · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | /description |
| publication_date | source · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | |
| title | source · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | /name |
| updated_date | source · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 | |
| version_label | source · dataverse ird fr | connector:dataverse_ird_fr@1.0.0 |