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Estimating rainfall intensity based on surveillance audio and deep-learning.

Meizhen Wang1,2,3, Mingzheng Chen1,2,3, Ziran Wang1,2,3,4

  • 1Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China.

Environmental Science and Ecotechnology
|August 20, 2024
PubMed
Summary
This summary is machine-generated.

Surveillance audio can now estimate rainfall intensity, offering a new method for hydrological sensing. This study introduces a dataset and deep learning model, paving the way for improved urban rainfall monitoring.

Keywords:
DatasetDeep learningRainfall intensityRegressionSurveillance audio

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Area of Science:

  • Hydrology
  • Environmental Sensing
  • Machine Learning

Background:

  • High-resolution rainfall data is crucial for urban hydrological modeling.
  • Surveillance cameras offer potential for rainfall monitoring through audio and video.
  • Existing audio-based methods often lack rainfall intensity estimation capabilities.

Purpose of the Study:

  • Introduce the Surveillance Audio Rainfall Intensity Dataset (SARID).
  • Develop and validate a deep learning model for estimating rainfall intensity from surveillance audio.
  • Explore the potential of audio-based sensing for urban rainfall observation.

Main Methods:

  • Created SARID from six real-world rainfall events, segmenting audio and annotating with intensity and environmental data.
  • Developed a deep learning baseline using Mel-Frequency Cepstral Coefficients (MFCC) and Transformer architecture.
  • Validated the model against ground truth data for performance evaluation.

Main Results:

  • The developed deep learning model achieved a root mean absolute error of 0.88 mm h⁻¹.
  • A coefficient of correlation of 0.765 was obtained, demonstrating model accuracy.
  • The SARID dataset contains 12,066 audio segments with detailed annotations.

Conclusions:

  • Surveillance audio-based models show significant potential for practical and effective rainfall observation.
  • This work initiates a new approach to rainfall intensity estimation using novel data sources.
  • Findings contribute to high-resolution hydrological sensing, urban sensing, and emergency response systems.