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Intelligent earthquake prediction using animal vocal behavior analysis based on machine learning and deep learning

Rakesh Salakapuri1, Surya Pavan Kumar Gudla2, Panduranga Vital Terlapu3

  • 1Symbiosis Institute of Technology, Hyderabad Campus, Symbiosis International (Deemed University), Pune, India. srakesh@sithyd.siu.edu.in.

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|April 20, 2026
PubMed
Summary

This study introduces a novel framework using animal vocalizations and machine learning (ML) to predict earthquakes. Deep learning models, particularly Bidirectional LSTM, achieved 98.87% accuracy, offering a cost-effective early warning system.

Keywords:
Animal behavioural patternsData augmentationDeep learningEarthquake precursor detectionMachine learning

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Area of Science:

  • Earthquake prediction
  • Bioacoustics
  • Machine Learning (ML)
  • Deep Learning (DL)

Background:

  • Traditional earthquake monitoring lacks timely warnings, leading to significant global losses.
  • Early detection of seismic activity is crucial for mitigating environmental and infrastructural damage.
  • Innovative approaches are needed to supplement existing seismic monitoring systems.

Purpose of the Study:

  • To develop a novel framework for identifying earthquake precursors using animal bioacoustics.
  • To leverage Machine Learning (ML) and Deep Learning (DL) models for analyzing animal vocalizations.
  • To create a cost-effective and scalable early warning system for earthquakes.

Main Methods:

  • Utilized historical animal audio recordings, enhanced with data augmentation.
  • Extracted temporal and spectral features using the Librosa library.
  • Implemented and compared ML algorithms (XGBoost, Random Forest, MLP) and DL models (RNN, LSTM, Bi-LSTM, GRU).

Main Results:

  • Bidirectional LSTM (Bi-LSTM) achieved a test accuracy of 98.87% with an Area Under the Curve (AUC) close to 1.00.
  • Deep learning models demonstrated superior performance in handling temporal dependencies in audio data.
  • The model showed robustness to environmental noise and scalability with unseen datasets.

Conclusions:

  • The proposed bioacoustics-based ML/DL framework effectively predicts earthquakes.
  • This approach offers a cost-effective early warning system, especially for areas lacking advanced seismic infrastructure.
  • Future work includes integrating IoT and edge computing for enhanced scalability.